Voronoi-GRU-Based Multi-Robot Collaborative Exploration in Unknown Environments

Voronoi-GRU-Based Multi-Robot Collaborative Exploration in Unknown Environments

Blog Article

In modern society, the autonomous exploration of unknown environments has attracted extensive attention due to its broad applications, 6-Piece Power Reclining Sectional such as in search and rescue operations, planetary exploration, and environmental monitoring.This paper proposes a novel collaborative exploration strategy for multiple mobile robots, aiming to quickly realize the exploration of entire unknown environments.Specifically, we investigate a hierarchical control architecture, comprising an upper decision-making layer and a lower planning and mapping layer.

In the upper layer, the next frontier point for each robot is determined using Voronoi partitioning and the Multi-Agent Twin Delayed Deep Deterministic policy gradient (MATD3) deep reinforcement learning algorithm in a centralized training and decentralized execution framework.In the lower layer, navigation planning is achieved using A* and Timed Elastic Band (TEB) algorithms, while an improved Cartographer algorithm is used to construct a joint map for the multi-robot system.In addition, the improved Robot Operating System (ROS) and Gazebo simulation environments speed up simulation times, further alleviating the slow training of high-precision simulation engines.

Finally, the simulation results demonstrate the superiority of the proposed strategy, which achieves over 90% exploration SHEATHS coverage in unknown environments with a significantly reduced exploration time.Compared to MATD3, Multi-Agent Proximal Policy Optimization (MAPPO), Rapidly-Exploring Random Tree (RRT), and Cost-based methods, our strategy reduces time consumption by 41.1%, 47.

0%, 63.9%, and 74.9%, respectively.