Decentralized Communication-Maintained Coordination for Multi-Robot Exploration: Achieving Connectivity and Adaptability

The realm of multi-robot autonomous exploration tasks underscores the critical role of communication in coordinating group activities. This paper introduces an innovative decentralized multi-robot exploration algorithm, meticulously crafted to ensure unbroken communication within robotic groups, a crucial element for effective coordination. The motivation for our work is two-fold: Firstly, seamless communication is vital for coordinating multi-robot autonomous exploration tasks. Secondly, in applications such as disaster rescue operations or military maneuvers, there are numerous scenarios where spatial congregation of multiple robots is imperative for joint task accomplishment. Our approach addresses these challenges through a stringent communication constraint, ensuring that each robot remains in constant communicative contact with the rest of the group. This is realized by employing a decentralized policy that integrates Graph Neural Network (GNN) layers with self-attention mechanism. Such policy network design allows adaptation to different numbers of robots and varied environments. After an initial imitation learning phase, the policy is refined through learning from experiences generated via a tree-search-based lookahead technique. Our experimental analysis validates that the algorithm not only maintains consistent communication links among all group members but also improve the exploration efficiency under the communication constraints. These results highlight the potential of our method in enhancing the effectiveness of robotic group explorations while ensuring robust communication connection.

Authors

• Wei Tang
• Chao Li
• Jun Wu 0003
• Qiuguo Zhu

Keywords

• Robot kinematics
• Imitation learning
• Disasters
• Graph neural networks
• Intelligent robots
• Multi-robot Exploration
• Learning Phase
• Policy Network
• Multiple Robots
• Exploration Task
• Communication Constraints
• Robotic Group
• Communication Connection
• Frontier
• Information Exchange
• Attention Mechanism
• Multilayer Perceptron
• Markov Decision Process
• Mobile Robot
• Linear Layer
• Swarm Robotics
• Transformer Encoder
• Communication Graph
• Monte Carlo Tree Search
• Forward Prediction
• Current Robot
• Direct Constraints
• Upper Confidence Bound
• Decision-making Capabilities
• Softmax Operation
• Large State Space