Reactive Robot Navigation Using Behavioral Risk Perception for Uncertain Dynamic Obstacles

Abstract

Successful robotic deployment in challenging environments requires diverse reasoning and reactive control techniques to deal with uncertainty and risk. In this work, we propose a novel behavioral control framework to navigate in such environments with static and dynamic sources of risks. Different agent behaviors can create distinct environment assessments, leading to a variety of reactions while dealing with uncertain and risky situations. We construct a class of perceived risk functions to capture these different behaviors by taking inspiration from behavioral decision making models from Cumulative Prospect Theory (CPT). We then incorporate these perceived risks via local costmaps into a Model Predictive Controller (MPC) framework. Specifically, we use Model Predictive Path Integral (MPPI) Control framework that is capable of handling more general cost functions like our proposed perceived risks. Using this framework, we generate reactive control policies for any given behavioral profile, resulting in a diverse AI for reactive controls. We then illustrate the proposed algorithm in virtual experiments conducted in a high fidelity indoor ROS-Unity environment embedded with static and dynamic sources of risk. We show that our proposed framework is capable of producing a larger range of reactive behaviors leading to a more successful robot deployment.