Parallel approach to motion planning in uncertain environments

Real world motion planning often suffers from the need to replan during execution of the trajectory. This replanning can be triggered as the robot fails to properly track the trajectory or new sensory information is provided that invalidates the planned trajectory. Particularly in the case of many occluded obstacles or in unstructured terrain, replanning is a frequent occurrence. Developing methods to allow the robots to replan efficiently allows for greater operation time and can ensure robot mission success. This paper presents a novel approach that updates heuristic weights of a sampling based A* planning algorithm. This approach utilizes parallel instances of this planner to quickly search through multiple heuristic weights within its allotted replanning time. These weights are employed upon triggered replanning to speed up computation time. The concept is tested on a simulated quadrupedal robot LLAMA with realistic constraints on computation time imposed.