Task Space Planning with Complementarity Constraint-based Obstacle Avoidance
This work addresses motion planning challenges for robots in cluttered environments, though it appears incremental as it builds on existing methods like LCP and ScLERP.
The paper tackles the problem of local motion planning for robots by incorporating collision avoidance and end-effector constraints into a task space planner, demonstrating its efficacy through simulation and physical robot experiments.
In this paper, we present a task space-based local motion planner that incorporates collision avoidance and constraints on end-effector motion during the execution of a task. Our key technical contribution is the development of a novel kinematic state evolution model of the robot where the collision avoidance is encoded as a complementarity constraint. We show that the kinematic state evolution with collision avoidance can be represented as a Linear Complementarity Problem (LCP). Using the LCP model along with Screw Linear Interpolation (ScLERP) in SE(3), we show that it may be possible to compute a path between two given task space poses by directly moving from the start to the goal pose, even if there are potential collisions with obstacles. The scalability of the planner is demonstrated with experiments using a physical robot. We present simulation and experimental results with both collision avoidance and task constraints to show the efficacy of our approach.