Task-Based Motion Planning Using Optimal Redundancy for a Minimally Actuated Robotic Arm

In planning robotic manipulations, heuristic searches are commonly considered impractical due to the high dimensionality of the problem caused by redundancy in the kinematic chain. In this paper, we present an optimal motion planning algorithm for an overly redundant minimally actuated serial robot (MASR) using the manipulator workspace as a foundation for the heuristic search. By utilizing optimized numerical probability methods, a novel sub-workspace search was developed. The sub-workspace allows the search to quickly and accurately find the minimal sub-set of joints to be actuated and ensures the existence of a path to a given target. Further on, the search result is used as a search graph for the heuristic planning problem which guarantees an optimal solution within the problem boundaries. Using this approach, optimal heuristic search can become practical for various types of manipulators, tasks, and environments. We describe our workspace minimization and heuristic search using the example of a general robotic arm and then implement the approach on a MASR model, a robotic arm with five passive joints and a single mobile actuator that is free to travel along the arm and rotate each joint separately. A series of simulations show how our minimal redundancy approach can benefit from path planning in the case of traditional hyper-redundant manipulators, and its greater effectiveness when addressing the specific design of the MASR.