PuRR-RRT*: Behavioral path planning in uncertain, risky, and rewarding environments

Aamodh Suresh, Carlos Nieto-Granda and Sonia Martínez
Proceedings of the 2023 20th Int. Conf. on Ubiquitous Robots (UR), Honolulu, HI, USA, June 2023

Abstract:

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.

File: main.pdf

Bib-tex entry:

@InProceedings{AS-CNG-SM:23-ur,
author = {A. Suresh and C. Nieto-Granda and S. Mart{\'\i}nez},
title = {PuRR-RRT*: Behavioral path planning in uncertain, risky, and rewarding environments},
booktitle = {Proceedings of the 2023 20th Int. Conf. on Ubiquitous Robots},
pages = {},
year = {2023},
address = {Honolulu, Hawaii},
month = {June}
}