Decentralized Multi-target Exploration and Connectivity Maintenance with a Multi-robot System

This paper presents a novel distributed control strategy that enables multi-target exploration while ensuring a time-varying connected topology in both 2D and 3D cluttered environments. Flexible continuous connectivity is guaranteed by gradient descent on a monotonic potential function applied on the algebraic connectivity (or Fiedler eigenvalue) of a generalized interaction graph. Limited range, line-of-sight visibility, and collision avoidance are taken into account simultaneously by weighting of the graph Laplacian. Completeness of the multi-target visiting algorithm is guaranteed by using a decentralized adaptive leader selection strategy and a suitable scaling of the exploration force based on the direction alignment between exploration and connectivity force and the traveling efficiency of the current leader. Extensive MonteCarlo simulations with a group of several quadrotor UAVs show the practicability, scalability and effectiveness of the proposed method

Decentralized Simultaneous Multi-target Exploration using a Connected Network of Multiple Robots

International audienceThis paper presents a novel decentralized control strategy for a multi-robot system that enables parallel multi-target exploration while ensuring a time-varying connected topology in cluttered 3D environments. Flexible continuous connectivity is guaranteed by building upon a recent connectivity maintenance method, in which limited range, line-of-sight visibility, and collision avoidance are taken into account at the same time. Completeness of the decentralized multi-target exploration algorithm is guaranteed by dynamically assigning the robots with different motion behaviors during the exploration task. One major group is subject to a suitable downscaling of the main traveling force based on the traveling efficiency of the current leader and the direction alignment between traveling and connectivity force. This supports the leader in always reaching its current target and, on a larger time horizon, that the whole team realizes the overall task in finite time. Extensive Monte Carlo simulations with a group of several quadrotor UAVs show the scalability and effectiveness of the proposed method and experiments validate its practicability