A Fast Coordinated Motion Planning Method for Dual-Arm Robot Based on Parallel Constrained DDP

Safe and efficient coordinated motion planning is crucial for the dual-arm robot's manipulation in the constrained environment. This article studies a fast optimization-based coordinated motion planning method for dual-arm robots based on a parallel differential dynamic programming (DDP) solver. First, the proposed method models the unstructured environment using swept sphere volumes and turns the safety and task constraints, including collision avoidance, path constraints, and closed kinematics constraints, into mathematical equalities and inequalities in an optimization problem. Second, to efficiently solve this high-dimensional and multiconstraint optimization problem, a parallel constrained differential dynamic programming (PC-DDP) solver is developed based on a new multiple shooting strategy. It utilizes the augmented Lagrangian method to handle various constraints and achieves high solving efficiency using the parallelization strategy with an approximation and the serial correction of the value function. Simulations and experiments of opening doors and transferring the box are conducted on the robot with dual 7-DOF manipulators. The results show that the proposed method works well in the motion planning of coordinated manipulations in constrained environments and that the PC-DDP is much faster than other DDP variants and large-scale optimization solvers.