Decentralised Multi-Robot Exploration Using Monte Carlo Tree Search

Abstract

Autonomous robotic systems are useful in automating tasks such as inspection and surveying of unknown areas, where speed is often an important factor. In order to effectively reduce the time required to complete missions, an efficient exploration and coordination strategy is needed. In this spirit, this work proposes an approach based on the Monte Carlo Tree Search (MCTS) algorithm to guide robots during exploration missions. Our method first expands a search tree of possible actions from the robot's position towards unknown regions, and then selects the sequence of movements that best drive the exploration process forward with respect to a given reward function. The proposed approach, which is able to balance short-and long-term decision-making, is then extended to accommodate the presence of multiple robots, in a bid to push the efficiency of exploration further. Our method allows for the coordination of the robots' movements in a decentralized manner, relying on point-to-point communication. This results in an efficient strategy, which we refer to as Decentralized Monte Carlo Exploration (DMCE). The experimental results demonstrate that our pipeline outperforms a greedy exploration approach, as well as state-of-the-art planners, with up to 30% reduction in exploration times in a series of real-world maps.

Multi-Robot Exploration Employing Harmonic Map Transformations

“…The next-best unexplored region is then selected by solving a linearized convex hull optimization problem. In [21], a novel exploration strategy is presented using the Monte Carlo Tree Search algorithm, optimized for multi-robot exploration missions in unknown areas. Navigation is based on a reward function, and a decentralized coordination model is used among multiple robots, resulting in significantly reduced exploration times.…”