Decentralized Global Connectivity Maintenance for Multi-Robot Navigation: A Reinforcement Learning Approach
Not on the map. Why?
Abstract (arXiv)
The problem of multi-robot navigation of connectivity maintenance is challenging in multi-robot applications. This work investigates how to navigate a multi-robot team in unknown environments while maintaining connectivity. We propose a reinforcement learning (RL) approach to develop a decentralized policy, which is shared among multiple robots. Given range sensor measurements and the positions of other robots, the policy aims to generate control commands for navigation and preserve the global connectivity of the robot team. We incorporate connectivity concerns into the RL framework as constraints and introduce behavior cloning to reduce the exploration complexity of policy optimization. The policy is optimized with all transition data collected by multiple robots in random simulated scenarios. We validate the effectiveness of the proposed approach by comparing different combinations of connectivity constraints and behavior cloning. We also show that our policy can generalize to unseen scenarios in both simulation and holonomic robots experiments.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 20 of its 31 references that are in the map. First posted on arXiv 17 Sep 2021.
Builds on
20 of 31 references.
References · 20
- 2015Trust Region Policy Optimizationcited by 2,503
- 2016OpenAI Gymcited by 1,684
- 2017Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environmentscited by 1,519
- 2017Domain Randomization for Transferring Deep Neural Networks from Simulation to the Real Worldcited by 1,401
- 2018Soft Actor-Critic Algorithms and Applicationscited by 1,179
- 2017Constrained Policy Optimizationcited by 928
- 2017Learning Complex Dexterous Manipulation with Deep Reinforcement Learning and Demonstrationscited by 685
- 2016Learning to Communicate with Deep Multi-Agent Reinforcement Learningcited by 561
- 2017Leveraging Demonstrations for Deep Reinforcement Learning on Robotics Problems with Sparse Rewardscited by 302
- 2019End-to-End Safe Reinforcement Learning through Barrier Functions for Safety-Critical Continuous Control Taskscited by 256
- 2018Deep Reinforcement Learning for Multi-Agent Systems: A Review of Challenges, Solutions and Applicationscited by 122
- 2016Decentralized Non-communicating Multiagent Collision Avoidance with Deep Reinforcement Learningcited by 104
8 more
- 2018Motion Planning Among Dynamic, Decision-Making Agents with Deep Reinforcement Learningcited by 101
- 2017Towards Optimally Decentralized Multi-Robot Collision Avoidance via Deep Reinforcement Learningcited by 81
- 2020Regularizing Action Policies for Smooth Control with Reinforcement Learningcited by 47
- 2016From Perception to Decision: A Data-driven Approach to End-to-end Motion Planning for Autonomous Ground Robotscited by 37
- 2018Reinforced Imitation: Sample Efficient Deep Reinforcement Learning for Map-less Navigation by Leveraging Prior Demonstrationscited by 35
- 2016Multi-objective Compositions for Collision-Free Connectivity Maintenance in Teams of Mobile Robotscited by 5
- 2019End-to-end Decentralized Multi-robot Navigation in Unknown Complex Environments via Deep Reinforcement Learningcited by 3
- 2020Connectivity Maintenance: Global and Optimized approach through Control Barrier Functionscited by 3
Built on by
Citing papers per year: 2026: 1.
1 paper.