Safe motion planning for articulated robots using RRTs

We present a sampling-based motion planning approach for articulated manipulators that generates safe paths. It uses the rapidly-exploring random trees paradigm to establish a collision-free path in configuration space. The expansion of the trees is influenced by a modified version of the kinetostatic danger field - a safety assessment function recently proposed in the literature. The idea is to grow the trees towards safer regions. Thus, the planner provides not only collision-free paths, but strives for safer ones. We propose two versions of the planner. The first is a modification of the Jacobian Transpose-directed RRT (JT-RRT) algorithm that grows a single tree from the start configuration and uses the transpose of the Jacobian to guide the sampling towards the goal defined in the workspace. The second is an extension of the standard bidirectional RRT-connect planner where the inputs to the algorithm are the start and the goal configuration that serve as seeds for the trees growth.