Publications

S. G. Tarantos, T. Belvedere, G. Oriolo, "Dynamics-aware navigation among moving obstacles with application to ground and flying robots," Robotics and Autonomous Systems, vol. 172, 104582, 2024 (pdf) (link). DOI:10.1016/j.robot.2023.104582

We present a novel method for navigation of mobile robots in challenging dynamic environments. The method, which is based on Nonlinear Model Predictive Control (NMPC), hinges upon a specially devised constraint for dynamics-aware collision avoidance. In particular, the constraint builds on the notion of avoidable collision state, taking into account the robot actuation capabilities in addition to the robot–obstacle relative distance and velocity. The proposed approach is applied to both ground and flying robots and tested in a variety of static and dynamic environments. Comparative simulations with an NMPC using a purely distance-based collision avoidance constraint confirm the superiority of the dynamics-aware version, especially for high-speed navigation among moving obstacles. Moreover, the results indicate that the method can work with relatively short prediction horizons and is therefore amenable to real-time implementation.

M. Cipriano, P. Ferrari, N. Scianca, L. Lanari, G. Oriolo, "Humanoid motion generation in a world of stairs," Robotics and Autonomous Systems, vol. 168, 104495, 2023 (pdf) (link). DOI:10.1016/j.robot.2023.104495

Consider the problem of generating humanoid motions in an environment consisting of horizontal patches located at different heights (world of stairs). To this end, the paper proposes an integrated scheme which combines footstep planning and gait generation. In particular, footsteps are produced by a randomized algorithm that guarantees both feasibility and quality of the plan according to a chosen criterion; whereas for 3D gait generation we devise an ad hoc extension of the Intrinsically Stable MPC scheme. In its basic form, the proposed scheme addresses the off-line case (known environments), but a sensor-based adaptation is developed for the on-line case (unknown environments) based on an anytime version of the footstep planner. In order to validate the proposed approach, we present simulations in CoppeliaSim for the HRP-4 humanoid robot navigating scenarios of different complexity, both in the on-line and off-line case.

M. Selvaggio, A. Garg, F. Ruggiero, G. Oriolo, B. Siciliano, "Non-prehensile object transportation via model predictive non-sliding manipulation control," IEEE Transactions on Control Systems Technology, vol. 31, no. 5, pp. 2231-2244, 2023 (pdf) (link). DOI:10.1109/TCST.2023.3277224

This article proposes a Model Predictive Non-Sliding Manipulation (MPNSM) control approach to safely transport an object on a tray-like end-effector of a robotic manipulator. For the considered non-prehensile transportation task to succeed, both non-sliding manipulation and the robotic system constraints must always be satisfied. To tackle this problem, we devise a model predictive controller enforcing sticking contacts, i.e., preventing sliding between the object and the tray, and assuring that physical limits such as extreme joint positions, velocities, and input torques are never exceeded. The combined dynamic model of the physical system, comprising the manipulator and the object in contact, is derived in a compact form. The associated non-sliding manipulation constraint is formulated such that the parametrized contact forces belong to a conservatively approximated friction cone space. This constraint is enforced by the proposed MPNSM controller, formulated as an optimal control problem that optimises the objective of tracking the desired trajectory while always satisfying both manipulation and robotic system constraints. We validate our approach by showing extensive dynamic simulations using a torque-controlled 7- degree-of-freedom (DoF) KUKA LBR IIWA robotic manipulator. Finally, demonstrative results from real experiments conducted on a 21-DoF humanoid robotic platform are shown.

P. Ferrari, L. Rossini, F. Ruscelli, A. Laurenzi, G. Oriolo, N. G. Tsagarakis, E. Mingo Hoffman, "Multi-contact planning and control for humanoid robots: Design and validation of a complete framework," Robotics and Autonomous Systems, vol. 166, 104448, 2023 (pdf) (link). DOI:10.1016/j.robot.2023.104448

In this paper, we consider the problem of generating appropriate motions for a torque-controlled humanoid robot that is assigned a multi-contact loco-manipulation task, i.e., a task that requires the robot to move within the environment by repeatedly establishing and breaking multiple, non-coplanar contacts. To this end, we present a complete multi-contact planning and control framework for multi-limbed robotic systems, such as humanoids. The planning layer works offline and consists of two sequential modules: first, a stance planner computes a sequence of feasible contact combinations; then, a whole-body planner finds the sequence of collision-free humanoid motions that realize them while respecting the physical limitations of the robot. For the challenging problem posed by the first stage, we propose a novel randomized approach that does not require the specification of pre-designed potential contacts or any kind of pre-computation. The control layer produces online torque commands that enable the humanoid to execute the planned motions while guaranteeing closed-loop balance. It relies on two modules, i.e., the stance switching and reactive balancing module; their combined action allows it to withstand possible execution inaccuracies, external disturbances, and modeling uncertainties. Numerical and experimental results obtained on COMAN+, a torque-controlled humanoid robot designed at Istituto Italiano di Tecnologia, validate our framework for loco-manipulation tasks of different complexity.

F. M. Smaldone, N. Scianca, L. Lanari, G. Oriolo, "From walking to running: 3D humanoid gait generation via MPC," Frontiers in Robotics and AI, vol. 9, pp. 1-18, 2022 (pdf). DOI:10.3389/frobt.2022.876613

We present a real time algorithm for humanoid 3D walking and/or running based on a Model Predictive Control (MPC) approach. The objective is to generate a stable gait that replicates a footstep plan as closely as possible, that is, a sequence of candidate footstep positions and orientations with associated timings. For each footstep, the plan also specifies an associated reference height for the Center of Mass (CoM) and whether the robot should reach the footstep by walking or running. The scheme makes use of the Variable-Height Inverted Pendulum (VH-IP) as a prediction model, generating in real time both a CoM trajectory and adapted footsteps. The VH-IP model relates the position of the CoM to that of the Zero Moment Point (ZMP); to avoid falling, the ZMP must be inside a properly defined support region (a 3D extension of the 2D support polygon) whenever the robot is in contact with the ground. The nonlinearity of the VH-IP is handled by splitting the gait generation into two consecutive stages, both requiring to solve a quadratic program. Thanks to a particular triangular structure of the VH-IP dynamics, the first stage deals with the vertical dynamics using the Ground Reaction Force (GRF) as a decision variable. Using the prediction given by the first stage, the horizontal dynamics become linear time-varying. During the flight phases, the VH-IP collapses to a free-falling mass model. The proposed formulation incorporates constraints in order to maintain physically meaningful values of the GRF, keep the ZMP in the support region during contact phases, and ensure that the adapted footsteps are kinematically realizable. Most importantly, a stability constraint is enforced on the time-varying horizontal dynamics to guarantee a bounded evolution of the CoM with respect to the ZMP. Furthermore, we show how to extend the technique in order to perform running on tilted surfaces. We also describe a simple technique that receives input high-level velocity commands and generates a footstep plan in the form required by the proposed MPC scheme. The algorithm is validated via dynamic simulations on the full- scale humanoid robot HRP-4, as well as experiments on the small-sized robot OP3.

M. Beglini, T. Belvedere, L. Lanari, G. Oriolo, "An intrinsically stable MPC approach for anti-jackknifing control of tractor-trailer vehicles", IEEE/ASME Transactions on Mechatronics, vol. 27, no. 6, pp. 4417-4428, 2022 (pdf). DOI:10.1109/LRA.2022.3141658

Tractor-trailer vehicles are affected by jack-knifing, a phenomenon that consists in the divergence of the trailer hitch angle and ultimately causes the vehicle to fold up. For the case of backward motions, in which jackknifing can occur at any speed, we present a control method that drives the vehicle along generic reference Cartesian trajectories while avoiding the divergence of the hitch angle. This is obtained thanks to a feedback control law that combines two actions: a tracking term, computed using input–output linearization, and a corrective term, generated via IS-MPC, an intrinsically stable MPC scheme which is effective for stable inversion of non-minimum phase systems. The successful performance of the proposed anti-jackknifing control is verified through simulations and experiments on a purposely built one-trailer prototype. To show the generality of the approach, we also apply and test the proposed method on a two-trailer vehicle.

P. M. Viceconte, R. Camoriano, G. Romualdi, D. Ferigo, S. Dafarra, S. Traversaro, G. Oriolo, L. Rosasco, D. Pucci, "ADHERENT: Learning human-like trajectory generators for whole-body control of humanoid robots", IEEE Robotics and Automation Letters, vol. 7, no. 2, pp. 2779-2786, 2022 (pdf). DOI:10.1109/LRA.2022.3141658

Human-like trajectory generation and footstep planning represent challenging problems in humanoid robotics. Recently, research in computer graphics investigated machine-learning methods for character animation based on training human-like models directly on motion capture data. Such methods proved effective in virtual environments, mainly focusing on trajectory visualization. This letter presents ADHERENT, a system architecture integrating machine-learning methods used in computer graphics with whole-body control methods employed in robotics to generate and stabilize human-like trajectories for humanoid robots. Leveraging human motion capture locomotion data, ADHERENT yields a general footstep planner, including forward, sideways, and backward walking trajectories that blend smoothly from one to another. Furthermore, at the joint configuration level, ADHERENT computes data-driven whole-body postural reference trajectories coherent with the generated footsteps, thus increasing the human likeness of the resulting robot motion. Extensive validations of the proposed architecture are presented with both simulations and real experiments on the iCub humanoid robot, thus demonstrating ADHERENT to be robust to varying step sizes and walking speeds.

G. Turrisi, M. Capotondi, C. Gaz, V. Modugno, G. Oriolo, A. De Luca, "On-line learning for planning and control of underactuated robots with uncertain dynamics," IEEE Robotics and Automation Letters, vol. 7, no. 1, pp. 358-365, 2022 (pdf). DOI:10.1109/LRA.2021.3126899

We present an iterative approach for planning and controlling motions of underactuated robots with uncertain dynamics. At its core, there is a learning process which estimates the perturbations induced by the model uncertainty on the active and passive degrees of freedom. The generic iteration of the algorithm makes use of the learned data in both the planning phase, which is based on optimization, and the control phase, where partial feedback linearization of the active dofs is performed on the model updated on-line. The performance of the proposed approach is shown by comparative simulations and experiments on a Pendubot executing various types of swing-up maneuvers. Very few iterations are typically needed to generate dynamically feasible trajectories and the tracking control that guarantees their accurate execution, even in the presence of large model uncertainties.

B. Barros Carlos, A. Franchi, G. Oriolo, "Towards safe human-quadrotor interaction: mixed-initiative control via real-time NMPC," IEEE Robotics and Automation Letters, vol. 6, no. 4, pp. 7611-7618, 2021 (pdf). DOI:10.1109/LRA.2021.3096502

This article presents a novel algorithm for blending human inputs and automatic controller commands, guaranteeing safety in mixed-initiative interactions between humans and quadrotors. The algorithm is based on nonlinear model predictive control (NMPC) and involves using the state solution to assess whether safety- and/or task-related rules are met to mix control authority. The mixing is attained through the convex combination of human and actual robot costs, and is driven by a continuous function that measures the rules’ violation. To achieve real-time feasibility, we rely on an efficient real-time iteration (RTI) variant of a sequential quadratic programming (SQP) scheme to cast the mixed-initiative controller. We demonstrate the effectiveness of our algorithm through numerical simulations, where a second autonomous algorithm is used to emulate the behavior of pilots with different skill levels. Simulations show that our scheme provides suitable assistance to pilots, especially novices, in a workspace with obstacles while bolstering computational efficiency.

N. Scianca, P. Ferrari, D. De Simone, L. Lanari, G. Oriolo, "A behavior-based framework for safe deployment of humanoid robots," Autonomous Robots, vol. 45, no. 4, pp. 435-456, 2021 (pdf). DOI:10.1007/s10514-021-09978-5

We present a complete framework for the safe deployment of humanoid robots in environments containing humans. Proceeding from some general guidelines, we propose several safety behaviors, classified in three categories, i.e., override, temporary override, and proactive. Activation and deactivation of these behaviors is triggered by information coming from the robot sensors and is handled by a state machine. The implementation of our safety framework is discussed with respect to a reference control architecture. In particular, it is shown that an MPC-based gait generator is ideal for realizing all behaviors related to locomotion. Simulation and experimental results on the HRP-4 and NAO humanoids, respectively, are presented to confirm the effectiveness of the proposed method.

F. Cursi, V. Modugno, L. Lanari, G. Oriolo, P. Kormushev, "Bayesian neural network modeling and hierarchical MPC for a tendon-driven surgical robot with uncertainty minimization," IEEE Robotics and Automation Letters, vol. 6, no. 2, pp. 2642-2649, 2021 (pdf). DOI:10.1109/LRA.2021.3062339

In order to guarantee precision and safety in robotic surgery, accurate models of the robot and proper control strategies are needed. Bayesian Neural Networks (BNN) are capable of learning complex models and provide information about the uncertainties of the learned system. Model Predictive Control (MPC) is a reliable control strategy to ensure optimality and satisfaction of safety constraints. In this work we propose the use of BNN to build the highly nonlinear kinematic and dynamic models of a tendon-driven surgical robot, and exploit the information about the epistemic uncertainties by means of a Hierarchical MPC (Hi-MPC) control strategy. Simulation and real world experiments show that the method is capable of ensuring accurate tip positioning, while satisfying imposed safety bounds on the kinematics and dynamics of the robot.

F. M. Smaldone, N. Scianca, L. Lanari, G. Oriolo, " <b>Feasibility-Driven Step Timing Adaptation for Robust MPC-Based Gait Generation in Humanoid</b> Feasibility-driven step timing adaptation for robust MPC-based gait generation in humanoids," IEEE Robotics and Automation Letters, vol. 6, no. 2, pp. 1582-1589, 2021 (pdf). DOI:10.1109/LRA.2021.3059627 Feasibility-Driven Step Timing Adaptation for Robust MPC-Based Gait Generation in Humanoids

The feasibility region of a Model Predictive Control (MPC) algorithm is the subset of the state space in which the constrained optimization problem to be solved is feasible. In our recent Intrinsically Stable MPC (IS-MPC) method for humanoid gait generation, feasibility means being able to satisfy the dynamic balance condition, the kinematic constraints on footsteps as well as an explicit stability condition. Here, we exploit the feasibility concept to build a step timing adapter that, at each control cycle, modifies the duration of the current step whenever a feasibility loss is imminent due, e.g., to an external perturbation. The proposed approach allows the IS-MPC algorithm to maintain its linearity and adds a negligible computational burden to the overall scheme. Simulations and experimental results where the robot is pushed while walking showcase the performance of the proposed approach.

M. Cefalo, P. Ferrari, G. Oriolo, "An opportunistic strategy for motion planning in the presence of soft task constraints," IEEE Robotics and Automation Letters, vol. 5, no. 4, pp. 6294-6301, 2020 (pdf). DOI:10.1109/LRA.2020.3013893

Consider the problem of planning collision-free motions for a robot that is assigned a soft task constraint, i.e., a desired path in task space with an associated error tolerance. To this end, we propose an opportunistic planning strategy in which two subplanners take turns in generating motions. The hard planner guarantees exact realization of the desired task path until an obstruction is detected in configuration space; at this point, it invokes the soft planner, which is in charge of exploiting the available task tolerance to bypass the obstruction and returning control to the hard planner as soon as possible. As a result, the robot will perform the desired task for as long as possible, and deviate from it only when strictly needed to avoid a collision. We present several planning experiments performed in V-REP for the PR2 mobile manipulator in order to show the effectiveness of the proposed planner.

N. Scianca, D. De Simone, L. Lanari, G. Oriolo, "MPC for humanoid gait generation: Stability and feasibility", IEEE Transactions on Robotics, vol. 36, no. 4, pp. 1171-1188, 2020 (pdf). DOI:10.1109/TRO.2019.2958483

We present IS-MPC, an intrinsically stable MPC framework for humanoid gait generation that incorporates a stability constraint in the formulation. The method uses as prediction model a dynamically extended LIP with ZMP velocities as control inputs, producing in real time a gait (including footsteps with timing) that realizes omnidirectional motion commands coming from an external source. The stability constraint links future ZMP velocities to the current state so as to guarantee that the generated CoM trajectory is bounded with respect to the ZMP trajectory. Being the MPC control horizon finite, only part of the future ZMP velocities are decision variables; the remaining part, called {\em tail}, must be either conjectured or anticipated using preview information on the reference motion. Several options for the tail are discussed, each corresponding to a specific terminal constraint. A feasibility analysis of the generic MPC iteration is developed and used to obtain sufficient conditions for recursive feasibility. Finally, we prove that recursive feasibility guarantees stability of the CoM/ZMP dynamics. Simulation and experimental results on NAO and HRP-4 are presented to highlight the performance of IS-MPC.

A. Kheddar, S. Caron, P. Gergondet, A. Comport, A. Tanguy, C. Ott, B. Henze, G. Mesesan, J. Englsberger, M.A. Roa, P-B. Wieber, F. Chaumette, F. Spindler, G. Oriolo, L. Lanari, A. Escande, K. Chappellet, F. Kanehiro, and P. Rabaté, "Humanoid robots in aircraft manufacturing: The Airbus use cases", IEEE Robotics & Automation Magazine, vol. 26, no. 4, pp. 30-45, 2019 (pdf). DOI:10.1109/MRA.2019.2943395. This paper won the IEEE Robotics and Automation Magazine Best Paper Award for 2020.

L. Penco, N. Scianca, V. Modugno, L. Lanari, G. Oriolo, S. Ivaldi, "A multimode teleoperation framework for humanoid loco-manipulation: A demonstration using the iCub robot", IEEE Robotics & Automation Magazine, vol. 26, no. 4, pp. 73-82, 2019 (pdf). DOI:10.1109/MRA.2019.2941245.

A. Karami, H. Sadeghian, M. Keshmiri, G. Oriolo, "Force, orientation and position control in redundant manipulators in prioritized scheme with null space compliance," Control Engineering Practice, vol. 85, pp. 23–33, 2019 (pdf). DOI: 10.1016/j.conengprac.2019.01.003

This paper addresses the problem of executing multiple prioritized tasks for robot manipulators with compliant behavior in the remaining null space. A novel controller–observer is proposed to ensure accurate accomplishment of various tasks based on a predefined hierarchy using a new priority assignment approach. Force control, position control and orientation control are considered. Moreover, a compliant behavior is imposed in the null space to handle physical interaction without using joint torque measurements. Asymptotic stability of the task space error and external torque estimation error during executing multiple tasks are shown. The performance of the proposed approach is evaluated on a 7R light weight robot arm by several case studies.

M. Cefalo, G. Oriolo, "A general framework for task-constrained motion planning with moving obstacles," Robotica, vol. 37, pp. 575-598, 2019 (pdf). DOI: 10.1017/S0263574718001182

Consider the practically relevant situation in which a robotic system is assigned a task to be executed in an environment that contains moving obstacles. Generating collision-free motions that allow the robot to execute the task while complying with its control input limitations is a challenging problem, whose solution must be sought in the robot state space extended with time. We describe a general planning framework which can be tailored to robots described by either kinematic or dynamic models. The main component is a control-based scheme for producing configuration space subtrajectories along which the task constraint is continuously satisfied. The geometric motion and time history along each subtrajectory are generated separately in order to guarantee feasibility of the latter and at the same time make the scheme intrinsically more flexible. A randomized algorithm then explores the search space by repeatedly invoking the motion generation scheme and checking the produced subtrajectories for collisions. The proposed framework is shown to provide a probabilistically complete planner both in the kinematic and the dynamic case. Modified versions of the planners based on the exploration– exploitation approach are also devised to improve search efficiency or optimize a performance criterion along the solution. We present results in various scenarios involving non-holonomic mobile robots and fixed-based manipulators to show the performance of the planner.

A. Karami, H. Sadeghian, M. Keshmiri, G. Oriolo, "Hierarchical tracking task control in redundant manipulators with compliance control in the null-space," Mechatronics, vol. 55, pp. 171-179, 2018 (pdf). DOI: 10.1016/j.mechatronics.2018.09.005

In this paper, a new approach for dealing with multiple tracking tasks during physical interaction is proposed. By using this method, multiple tasks are accomplished based on the assigned priority in addition to a compliant behavior in the null-space of the main tasks. This issue is critical when robots are employed for complex manipulation in unknown environments and in the presence of human. During the manipulation in the dynamic environments, different objects may collide with the robot body and disturb its manipulation. In these cases, the robot is expected to continue execution of the tasks, accurately. Meanwhile, the robot should be compliant to ensure the safety during the interaction. A nonlinear controller-observer is proposed for tracking the desired trajectory based on a preallocated hierarchy. The suggested controller-observer estimates the external torques applied to the robot body without using joint torque measurements and compensates its projection on the task spaces. Asymptotic stability of the task space errors, the null-space velocity and the external interaction estimation error during accomplishing multiple tracking tasks are shown analytically. Finally, the algorithm performance is shown through experiments on a 7-DOF KUKA LWR robot arm.

G. Oriolo, M. Cefalo, M. Vendittelli, "Repeatable motion planning for redundant robots over cyclic tasks," IEEE Transactions on Robotics, vol. 33, no. 5, pp. 1170-1183, 2017 (pdf). DOI: 10.1109/TRO.2017.2715348

We consider the problem of repeatable motion planning for redundant robotic systems performing cyclic tasks in the presence of obstacles. For this open problem, we present a control-based randomized planner, which produces closed collision-free paths in configuration space and guarantees continuous satisfaction of the task constraints. The proposed algorithm, which relies on bidirectional search and loop closure in the task-constrained configuration space, is shown to be probabilistically complete. A modified version of the planner is also devised for the case in which configuration-space paths are required to be smooth. Finally, we present planning results in various scenarios involving both free-flying and nonholonomic robots to show the effectiveness of the proposed method.

A. Paolillo, A. Faragasso, G. Oriolo, M. Vendittelli, "Vision-based maze navigation for humanoid robots," Autonomous Robots, vol. 41, no. 2, pp. 293-309, 2017 (pdf). DOI: 10.1007/s10514-015-9533-1

We present a vision-based approach for navigation of humanoid robots in networks of corridors connected through curves and junctions. The objective of the humanoid is to follow the corridors, walking as close as possible to their center to maximize motion safety, and to turn at curves and junctions. Our control algorithm is inspired by a tech- nique originally designed for unicycle robots that we have adapted to humanoid navigation and extended to cope with the presence of turns and junctions. In addition, we prove here that the corridor following control law provides asymptotic convergence of robot heading and position to the corridor bisector even when the corridor walls are not parallel. A state transition system is designed to allow navigation in mazes of corridors, curves and T-junctions. Extensive experimental validation proves the validity and robustness of the approach.

P. Stegagno, M. Cognetti, G. Oriolo, H.H. Bülthoff, A. Franchi, "Ground and aerial mutual localization using anonymous relative-bearing measurements," IEEE Transactions on Robotics, vol. 32, no. 5 , pp. 1133-1151, 2016 (pdf). DOI: 10.1109/TRO.2016.2593454

We present a decentralized algorithm for estimating mutual poses (relative positions and orientations) in a group of mobile robots. The algorithm uses relative-bearing measurements, which for example can be obtained from onboard cameras, and information about the motion of the robots, such as inertial measurements. It is assumed that all relative-bearing measurements are anonymous; i.e., each of them specifies a direction along which another robot is located but not its identity. This situation, which is often ignored in the literature, frequently arises in practice and remarkably increases the complexity of the problem. The proposed solution is based on a two-step approach: in the first step, the most likely unscaled relative configurations with identities are computed from anonymous measurements using geometric arguments, while in the second step the scale is determined by numeric Bayesian filtering based on the motion model. The solution is first developed for ground robots in SE(2) and then for aerial robots in SE(3). Experiments using Khepera III ground mobile robots and quadrotor aerial robots confirm that the proposed method is effective and robust w.r.t. false positives and negatives of the relative-bearing measuring process.

G. Oriolo, A. Paolillo , L. Rosa, M. Vendittelli, "Humanoid odometric localization integrating kinematic, inertial and visual information," Autonomous Robots, vol. 40, no. 5, pp. 867–879, 2016 (pdf). DOI: 10.1007/s10514-015-9498-0

We present a method for odometric localization of humanoid robots using standard sensing equipment, i.e., a monocular camera, an Inertial Measurement Unit (IMU), joint encoders and foot pressure sensors. Data from all these sources are integrated using the prediction-correction paradigm of the Extended Kalman Filter. Position and orientation of the torso, defined as the representative body of the robot, are predicted through kinematic computations based on joint encoder readings; an asynchronous mechanism triggered by the pressure sensors is used to update the placement of the support foot. The correction step of the filter uses as measurements the torso orientation, provided by the IMU, and the head pose, reconstructed by a VSLAM algorithm. The proposed method is validated on the humanoid NAO through two sets of experiments: open-loop motions aimed at assessing the accuracy of localization with respect to a ground truth, and closed-loop motions where the humanoid pose estimates are used in real-time as feedback signals for trajectory control.

A. Franchi, P. Stegagno, G. Oriolo, "Decentralized multi-robot encirclement of a 3D target with guaranteed collision avoidance," Autonomous Robots, vol. 40, pp. 245-265, 2016 (pdf). DOI: 10.1007/s10514-015-9450-3

We present a control framework for achieving encirclement of a target moving in 3D using a multi-robot system. Three variations of a basic control strategy are proposed for different versions of the encirclement problem, and their effectiveness is formally established. An extension ensuring maintenance of a safe inter-robot distance is also discussed. The proposed framework is fully decentralized and only requires local communication among robots; in particular, each robot locally estimates all the relevant global quantities. We validate the proposed strategy through simulations on kinematic point robots and quadrotor UAVs, as well as experiments on differential-drive wheeled mobile robots.

H. Jabbari, G. Oriolo, H. Bolandi, "Output feedback image-based visual servoing control of an underactuated unmanned aerial vehicle," Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering, vol. 228, no. 7, pp. 435-448, 2014 (pdf). DOI: 10.1177/0959651814530698

In this article, image-based visual servoing control of an underactuated unmanned aerial vehicle is considered for the three-dimensional translational motion. Taking into account the low quality of accelerometers’ data, the main objective of this article is to only use information of rate gyroscopes and a camera, as the sensor suite, in order to design an image-based visual servoing controller. Kinematics and dynamics of the unmanned aerial vehicle are expressed in terms of visual information, which make it possible to design dynamic image-based visual servoing controllers without using lin- ear velocity information obtained from accelerometers. Image features are selected through perspective image moments of a flat target plane in which no geometric information is required, and therefore, the approach can be applied in unknown environments. Two output feedback controllers that deal with uncertainties in dynamics of the system related to the motion of the target and also unknown depth information of the image are proposed using a linear observer. Stability analysis guarantees that the errors of the system remain uniformly ultimately bounded during a tracking mission and converge to zero when the target is stationary. Simulation results are presented to validate the designed controllers.

H. Jabbari, G. Oriolo, H. Bolandi, "An adaptive scheme for image-based visual servoing of an underactuated UAV," International Journal of Robotics and Automation, vol. 29, no. 1, pp. 92-104, 2014 (pd f). DOI: 10.2316/Journal.206.2014.1.206-3942

An image-based visual servoing (IBVS) method is proposed for controlling the 3D translational motion and the yaw rotation of a quadrotor. The dynamic model of this Unmanned Aerial Vehicle (UAV) is considered at the design stage to account for its underactuation. In contrast with previous IBVS methods for underactuated UAVs, which used spherical image moments as visual features, the proposed controller makes use of appropriately defined perspective moments. As a consequence, we gain a clear improvement in performance, as satisfactory trajectories are obtained in both image and Cartesian space. In addition, an adaptation mechanism is included in the controller to achieve robust performance in spite of uncertainties related to the depth of the image features and to the dynamics of the robot. Simulation results in both nominal and perturbed conditions are presented to validate the proposed method.

A. Franchi, G. Oriolo, P. Stegagno, "Mutual localization in multi-robot systems using anonymous relative measurements," The International Journal of Robotics Research, vol. 32, no. 11, pp. 1302-1322, 2013 (pdf). DOI: 10.1177/0278364913495425

We propose a decentralized method to perform mutual localization in multi-robot systems using anonymous relative measurements, i.e., measurements that do not include the identity of the measured robot. This is a challenging and practically relevant operating scenario that has received little attention in the literature. Our mutual localization algorithm includes two main components: a probabilistic multiple registration stage, which provides all data associations that are consistent with the relative robot measurements and the current belief, and a dynamic filtering stage, which incorporates odometric data into the estimation process. The design of the proposed method proceeds from a detailed formal analysis of the implications of anonymity on the mutual localization problem. Experimental results on a team of differential-drive robots illustrate the effectiveness of the approach, and in particular its robustness against false positives and negatives that may affect the robot measurement process. We also provide an experimental comparison that shows how the proposed method outperforms more classical approaches that may be designed building on existing techniques. The source code of the proposed method is available within the MLAM ROS stack.

A. Censi, A. Franchi, A. Marchionni, G. Oriolo, "Simultaneous calibration of odometry and sensor parameters for mobile robots," IEEE Transactions on Robotics, vol. 29, no. 2, pp. 475-492, 2013 (pdf). DOI: 10.1109/TRO.2012.2226380

Consider a differential-drive mobile robot equipped with an on-board exteroceptive sensor that can estimate its own motion, e.g., a range-finder. Calibration of this robot involves estimating six parameters: three for the odometry (radii and distance between the wheels), and three for the pose of the sensor with respect to the robot. After analyzing the observability of this problem, this paper describes a method for calibrating all parameters at the same time, without the need for external sensors or devices, using only the measurement of the wheels velocities and the data from the exteroceptive sensor. The method does not require the robot to move along particular trajectories. Simultaneous calibration is formulated as a maximum-likelihood problem and the solution is found in a closed form. Experimental results show that the accuracy of the proposed calibration method is very close to the attainable limit given by the Cramèr–Rao bound.

A. Cherubini, F. Chaumette, G. Oriolo, "Visual servoing for path reaching with nonholonomic robots," Robotica, vol. 29, pp. 1037-1048, 2011 (pd f).

We present two visual servoing controllers (pose-based and image-based) enabling mobile robots with a fixed pinhole camera to reach and follow a continuous path drawn on the ground. The first contribution is the theoretical and experimental comparison between pose-based and image-based techniques for a nonholonomic robot task. Moreover, our controllers are appropriate not only for path following, but also for path reaching, a problem that has been rarely tackled in the past. Finally, in contrast with most works, which require the path geometric model, only two path features are necessary in our image-based scheme and three in the pose-based scheme. For both controllers, a convergence analysis is carried out, and the performance is validated by simulations, and outdoor experiments on a car-like robot.

A. Cherubini, F. Giannone, L. Iocchi, M. Lombardo, G. Oriolo, "Policy gradient learning for a humanoid soccer robot," Robotics and Autonomous Systems, vol. 57, pp. 808-818, 2009 (pd f).

In humanoid robotic soccer, many factors, both at low-level (e.g., vision and motion control) and at high-level (e.g., behaviors and game strategies), determine the quality of the robot performance. In particular, the speed of individual robots, the precision of the trajectory and the stability of the walking gaits, have a high impact on the success of a team. Consequently, humanoid soccer robots require fine tuning, especially for the basic behaviors. In recent years, machine learning techniques have been used to find optimal parameter sets for various humanoid robot behaviors. However, a drawback of learning techniques is time consumption: a practical learning method for robotic applications must be effective with a small amount of data. In this article, we compare two learning methods for humanoid walking gaits based on the Policy Gradient algorithm. We demonstrate that an extension of the classic Policy Gradient algorithm that takes into account parameter relevance allows for better solutions when only a few experiments are available. The results of our experimental work show the effectiveness of the policy gradient learning method, as well as its higher convergence rate, when the relevance of parameters is taken into account during learning.

A. Franchi, L. Freda, G. Oriolo, M. Vendittelli, "The Sensor-based Random Graph method for cooperative robot exploration," IEEE/ASME Transactions on Mechatronics, vol. 14, no. 2, pp. 163-175, 2009 (pd f).

We present a decentralized cooperative exploration strategy for a team of mobile robots equipped with range finders. A roadmap of the explored area, with the associate safe region, is built in the form of a Sensor-based Random Graph (SRG). This is expanded by the robots by using a randomized local planner which automatically realizes a trade-off between information gain and navigation cost. The nodes of the SRG represent view configurations that have been visited by at least one robot, and are connected by arcs that represent safe paths. These paths have been actually traveled by the robots or added to the SRG to improve its connectivity. Decentralized cooperation and coordination mechanisms are used so as to guarantee exploration efficiency and avoid conflicts. Simulations and experiments are presented to show the performance of the proposed technique.

A. Cherubini, G. Oriolo, F. Macrì, F. Aloise, F. Cincotti, D. Mattia, "A multimode navigation system for an assistive robotics project," Autonomous Robots, vol. 25, pp. 383-404, 2008 (pd f).

Assistive technology is an emerging area, where robotic devices can help individuals with motor disabilities to achieve independence in daily activities. This paper deals with a system that provides remote control of Sony AIBO, a commercial mobile robot, within the assistive project ASPICE. The robot can be controlled by various input devices, including a Brain-Computer Interface. AIBO has been chosen for its friendly-looking aspect, in order to ease interaction with the patients. The development of the project is described by focusing on the design of the robot navigation system. Single step, semi-autonomous and autonomous navigation modes have been realized to provide different levels of control. Automatic collision avoidance is integrated in all cases. Other features of the system, such as the video feedback from the robotic platform to the user, and the use of AIBO as communication aid, are briefly described. The performance of the navigation system is shown by simulations as well as experiments. The system has been clinically validated, in order to obtain a definitive assessment through patient feedback.

A. De Luca, G. Oriolo, P. Robuffo Giordano, "Feature depth observation for image-based visual servoing: Theory and experiments," The International Journal of Robotics Research, vol. 27, no. 10, pp. 1093-1116, 2008 (pd f).

In the classical image-based visual servoing framework, error signals are directly computed from image feature parameters, allowing in principle to obtain control schemes that need neither a complete 3D model of the scene, nor a perfect camera calibration. However, when the computation of control signals involves the interaction matrix, the current value of some 3D parameters is required for each considered feature, and typically a rough approximation of this value is used. With reference to the case of a point feature, for which the relevant 3D parameter is the depth Z, we propose a visual servoing approach where Z is observed and made available for servoing. This is achieved by interpreting depth as an unmeasurable state with known dynamics, and by building a nonlinear observer that asymptotically recovers the actual value of Z for the selected feature. A byproduct of our analysis is the rigorous characterization of camera motions that actually allow such observation. Moreover, in the case of a partially uncalibrated camera, it is possible to exploit complementary camera motions in order to preliminarily estimate the focal length without knowing Z. Simulation and experimental results are presented for a mobile robot with an on-board camera in order to illustrate the benefits of integrating the depth observation within classical visual servoing schemes.

F. Cincotti, D. Mattia, F. Aloise, S. Bufalari, G. Schalk, G. Oriolo, A. Cherubini, M.G. Marciani, F. Babiloni, "Non-invasive brain-computer interface system: Towards its application as assistive technology," Brain Research Bulletin, vol. 75, no. 6, pp. 796-803, 2008 (pdf).

The quality of life of people suffering from severe motor disabilities can benefit from the use of current assistive technology capable of ameliorating communication, house-environment management and mobility, according to the user’s residual motor abilities. Brain–computer interfaces (BCIs) are systems that can translate brain activity into signals that control external devices. Thus they can represent the only technology for severely paralyzed patients to increase or maintain their communication and control options. Here we report on a pilot study in which a system was implemented and validated to allow disabled persons to improve or recover their mobility (directly or by emulation) and communication within the surrounding environment. The system is based on a software controller that offers to the user a communication interface that is matched with the individual’s residual motor abilities. Patients (n = 14) with severe motor disabilities due to progressive neurodegenerative disorders were trained to use the system prototype under a rehabilitation program carried out in a house-like furnished space. All users utilized regular assistive control options (e.g., microswitches or head trackers). In addition, four subjects learned to operate the system by means of a non-invasive EEG-based BCI. This system was controlled by the subjects’ voluntary modulations of EEG sensorimotor rhythms recorded on the scalp; this skill was learnt even though the subjects have not had control over their limbs for a long time. We conclude that such a prototype system, which integrates several different assistive technologies including a BCI system, can potentially facilitate the translation from pre-clinical demonstrations to a clinical useful BCI.

L. Freda, G. Oriolo, "Vision-based interception of a moving target with a nonholonomic mobile robot," Robotics and Autonomous Systems, vol. 55, pp. 419-432, 2007 (pdf).

A novel vision-based scheme is presented for driving a nonholonomic mobile robot to intercept a moving target. The proposed method has a two-level structure. On the lower level, the pan-tilt platform carrying the on-board camera is controlled so as to keep the target as close as possible to the center of the image plane. On the higher level, the relative position of the target is retrieved from its image coordinates and the camera pan-tilt angles through simple geometry, and used to compute a control law which drives the robot to the target. Various possible choices are discussed for the high-level robot controller, and the associated stability properties are rigorously analyzed. The proposed visual interception method is validated through simulations as well as experiments on the mobile robot MagellanPro.

A. De Luca, G. Oriolo, P. Robuffo Giordano, "Image-based visual servoing schemes for nonholonomic mobile manipulators," Robotica, vol. 25, no. 2, pp. 129-145, 2007 (pdf).

We consider the task-oriented modeling of the differential kinematics of nonholonomic mobile manipulators (NMMs). A suitable NMM Jacobian is defined that relates the available input commands to the time derivative of the task variables, and can be used to formulate and solve kinematic control problems. When the NMM is redundant with respect to the given task, we provide an extension of two well-known redundancy resolution methods for fixed-base manipulators (Projected Gradient and Task Priority) and introduce a novel technique (Task Sequencing) aimed at improving performance, e.g., avoiding singularities. The proposed methods are applied then to the specific case of image-based visual servoing, where the NMM image Jacobian combines the interaction matrix and the kinematic model of the mobile manipulator. Comparative numerical results are presented for two case studies.

G. L. Mariottini, G. Oriolo, D. Prattichizzo, "Image-based visual servoing for nonholonomic mobile robots using epipolar geometry," IEEE Transactions on Robotics, vol. 23, no. 1, pp. 87-100, 2007 (pdf).

We present an image-based visual servoing strategy for driving a nonholonomic mobile robot equipped with a pinhole camera toward a desired configuration. The proposed approach, which exploits the epipolar geometry defined by the current and desired camera views, does not need any knowledge of the 3-D scene geometry. The control scheme is divided in two steps. In the first, using an approximate input-output linearizing feedback, the epipoles are zeroed so as to align the robot with the goal. Feature points are then used in the second translational step to reach the desired configuration. Asymptotic convergence to the desired configuration is proven, both in the calibrated and partially calibrated case. Simulation and experimental results show the effectiveness of the proposed control scheme.

G. Oriolo, M. Vendittelli, "A framework for the stabilization of general nonholonomic systems with an application to the plate-ball mechanism," IEEE Transactions on Robotics, vol. 21, no. 2, pp. 162-175, 2005 (pdf).

We present a framework for the stabilization of nonholonomic systems that do not possess special properties such as flatness or exact nilpotentizability. Our approach makes use of two tools: an iterative control scheme and a nilpotent approximation of the system dynamics. The latter is used to compute an approximate steering control which, repeatedly applied to the system, guarantees asymptotic stability with exponential convergence to any desired set-point, under appropriate conditions. For illustration, we apply the proposed strategy to design a stabilizing controller for the plate-ball manipulation system, a canonical example of non-flat nonholonomic mechanism. The theoretical performance and robustness of the algorithm are confirmed by simulations, both in the nominal case and in the presence of a perturbation on the ball radius.

G. Oriolo, "Wheeled robots," in Encyclopedia of Systems and Control - 2nd Edition, J. Baillieul, T. Samad, Eds., Springer, London, pp. 1-8 (online), 2020 (pdf). ISBN: 978-1-4471-5102-9. DOI: 10.1007/978-1-4471-5102-9_178-2