Target Search and Navigation in Heterogeneous Robot Systems with Deep Reinforcement Learning

TL;DR

This paper presents a multi-stage deep reinforcement learning framework with a curiosity module for heterogeneous robot systems, enabling efficient target search and navigation in unknown environments, demonstrated through simulation experiments.

Contribution

It introduces a novel multi-stage reinforcement learning approach with curiosity-driven exploration for heterogeneous robot collaboration in search and rescue tasks.

Findings

Effective in training heterogeneous robots for target search in unknown environments.

Accelerates training speed compared to baseline methods.

Achieves successful navigation and target localization in maze-like environments.

Abstract

Collaborative heterogeneous robot systems can greatly improve the efficiency of target search and navigation tasks. In this paper, we design a heterogeneous robot system consisting of a UAV and a UGV for search and rescue missions in unknown environments. The system is able to search for targets and navigate to them in a maze-like mine environment with the policies learned through deep reinforcement learning algorithms. During the training process, if two robots are trained simultaneously, the rewards related to their collaboration may not be properly obtained. Hence, we introduce a multi-stage reinforcement learning framework and a curiosity module to encourage agents to explore unvisited environments. Experiments in simulation environments show that our framework can train the heterogeneous robot system to achieve the search and navigation with unknown target locations while existing baselines may not, and accelerate the training speed.