PuRR-RRT*: Behavioral Path Planning in Uncertain, Risky and Rewarding Environments

Effective robotic deployment in uncertain risky and rewarding environments demand diverse reasoning and planning capabilities from robots. In this work, we propose a novel behavioral planning algorithm to navigate in such environments with continuous and uncertain sources of risks and rewards. Agents can express a variety of different behaviors, leading to various environment assessments and correspondingly different planned paths. We take inspiration from behavioral decision making models from Cumulative Prospect Theory (CPT), to construct a class of novel perceived loss functions to capture these different behaviors. We then incorporate these perceived losses into path costs and leverage sampling based planning techniques from RRT*. Our planner Perceived uncertain Risk and Reward RRT* (PuRR-RRT*) plans asymptotically optimal paths, consistent with any given behavioral profile, resulting in a diverse AI for path planning. We then illustrate the proposed algorithm in virtual experiments conducted in a ROS-Unity environment embedded with risk and reward sources. We show that our proposed planner is capable of producing a larger range of diverse paths, aligned with the required behavior.