For a robotic system subject to geometric task constraints, consider the problem of motion planning in the presence of obstacles moving along fully predictable trajectories. This problem, which we call TCMP_MO (Task-Constrained Motion Planning with Moving Obstacles), is an extension of the basic TCMP problem. Planning among moving obstacles is always very challenging. The addition of geometric task constraints, which is considered for the first time in this work, makes the problem even more difficult, because the set of feasible configurations is reduced to a lower-dimensional subset of the configuration space.
In [1], we propose a control-based motion planner that works directly in the task-constrained configuration space extended with the time dimension. The generated trajectories are collision-free and satisfy the task constraint with arbitrary accuracy. Kinematic constraints, such as bounds on the achievable generalized velocities, may also be considered.
A further development of the TCMP_MO planner is presented in [2] to take into account the presence of bounds on the forces/torques made available by the actuators (Dynamically Feasible Task-Constrained Motion Planning with Moving Obstacles, or DF_TCMP_MO). While this may appear as a relatively direct extension, it actually requires some radical changes. In particular, the new planner is based on a motion generation scheme that operates at the acceleration level and makes direct use of the robot dynamic model.
We implemented the proposed TCMP_MO planner in Kite (a cross-platform software for motion planning produced by Kineo CAM). The following clip shows results for two scenarios involving KUKA robots: an LWR-IV 7-DOF manipulator and a youBot mobile manipulator.
The DF_TCMP_MO planner was also implemented in Kite. The following clip shows comparative results (DF_TCMP_MO vs. TCMP_MO) for three scenarios involving the same KUKA robots, i.e., an LWR-IV 7-DOF manipulator and a youBot mobile manipulator. Dynamic (as opposed to kinematic) feasibility is now guaranteed.
[1] M. Cefalo, G. Oriolo, M. Vendittelli, Task Constrained Motion Planning with Moving Obstacles. 2013 IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS 2013), Tokyo, Japan, pp. 5758-5763, 2013 (pdf).
[2] M. Cefalo, G. Oriolo, Dynamically Feasible Task-Constrained Motion Planning with Moving Obstacles. To be presented at 2014 IEEE Int. Conf. on Robotics and Automation (ICRA 2014), Hong Kong, China, June 2014 (pdf).