Scalable Multi-Robot Informative Path Planning for Target Mapping via Deep Reinforcement Learning
Autonomous robots are widely utilized for mapping and exploration tasks due to their cost-effectiveness. Multi-robot systems offer scalability and efficiency, especially in terms of the number of robots deployed in more complex environments. These tasks belong to the set of Multi-Robot Informative Path Planning (MRIPP) problems. In this paper, we propose a deep reinforcement learning approach for the MRIPP problem. We aim to maximize the number of discovered stationary targets in an unknown 3D environment while operating under resource constraints (such as path length). Here, each robot aims to maximize discovered targets, avoid unknown static obstacles, and prevent inter-robot collisions while operating under communication and resource constraints. We utilize the centralized training and decentralized execution paradigm to train a single policy neural network. A key aspect of our approach is our coordination graph that prioritizes visiting regions not yet explored by other robots. Our learned policy can be copied onto any number of robots for deployment in more complex environments not seen during training. Our approach outperforms state-of-the-art approaches by at least 26.2% in terms of the number of discovered targets while requiring a planning time of less than 2 sec per step. We present results for more complex environments with up to 64 robots and compare success rates against baseline planners. Our code and trained model are available at - https://github.com/AccGen99/marl_ipp
Code (1)
Tasks
Collision AvoidanceDeep Reinforcement Learningreinforcement-learningReinforcement LearningMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Efficient Informative Sensing using Multiple Robots
The need for efficient monitoring of spatio-temporal dynamics in large environmental applications, such as the water quality monitoring in rivers and lakes, motivates the use of robotic sensors in order to achieve suffic…
Adaptive Informative Path Planning Using Deep Reinforcement Learning for UAV-based Active Sensing
Aerial robots are increasingly being utilized for environmental monitoring and exploration. However, a key challenge is efficiently planning paths to maximize the information value of acquired data as an initially unknow…
Deep Reinforcement LearningReinforcement Learning (RL)Multi-Robot Coordination for Planning under Context Uncertainty
Real-world robots often operate in settings where objective priorities depend on the underlying context of operation. When the underlying context is unknown apriori, multiple robots may have to coordinate to gather infor…
Scalable Multi-Robot Path Planning via Quadratic Unconstrained Binary Optimization
Multi-Agent Path Finding (MAPF) remains a fundamental challenge in robotics, where classical centralized approaches exhibit exponential growth in joint-state complexity as the number of agents increases. This paper inves…
Expected Free Energy-based Informative Path Planning for Robotic Mars Exploration
An autonomous robot efficiently exploring an unknown environment, such as looking for water sources on Mars, faces two simultaneous demands: building an accurate information map while quickly finding the regions of great…