Multi-Robot Informative Path Planning for Efficient Target Mapping using Deep Reinforcement Learning

2024-09-25

arXiv.org

AI Summary

We address the decentralized multi-robot collaborative search for stationary targets in unknown 3D environments, subject to path-length constraints, static obstacle avoidance, inter-robot collision avoidance, and communication/resource limitations. We propose a fully decentralized deep reinforcement learning framework: (i) an augmented graph representation enables dynamic scalability and eliminates the need for retraining when robot count changes; (ii) a novel joint trajectory modeling mechanism jointly enforces communication constraints and collision avoidance; and (iii) a centralized-training-with-decentralized-execution (CTDE) paradigm integrated with graph neural networks facilitates efficient distributed decision-making. Experiments demonstrate a 33.75% improvement in target discovery count over state-of-the-art methods. The source code and trained models are publicly available.

Technology Category

• Search and Optimization: Learning to Search
• Intelligent Robots: Learning & Optimization for ROB
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

Abstract

Autonomous robots are being employed in several mapping and data collection tasks due to their efficiency and low labor costs. In these tasks, the robots are required to map targets-of-interest in an unknown environment while constrained to a given resource budget such as path length or mission time. This is a challenging problem as each robot has to not only detect and avoid collisions from static obstacles in the environment but also has to model other robots' trajectories to avoid inter-robot collisions. We propose a novel deep reinforcement learning approach for multi-robot informative path planning to map targets-of-interest in an unknown 3D environment. A key aspect of our approach is an augmented graph that models other robots' trajectories to enable planning for communication and inter-robot collision avoidance. We train our decentralized reinforcement learning policy via the centralized training and decentralized execution paradigm. Once trained, our policy is also scalable to varying number of robots and does not require re-training. Our approach outperforms other state-of-the-art multi-robot target mapping approaches by 33.75% in terms of the number of discovered targets-of-interest. We open-source our code and model at: https://github.com/AccGen99/marl_ipp

Problem

• Maximize discovered targets in unknown 3D environments with resource constraints
• Coordinate multi-robot systems to avoid collisions and static obstacles
• Improve scalability and efficiency in Multi-Robot Informative Path Planning

Innovation

• Deep reinforcement learning for MRIPP
• Centralized training decentralized execution
• Coordination graph for unexplored regions