Collaborative Complete Coverage and Path Planning for Multi-Robot Exploration

In mobile robotics research, the exploration of unknown environments has always been an important topic due to its practical uses in consumer and military applications. One specific interest of recent investigation is the field of complete coverage and path planning (CCPP) techniques for mobile robot navigation. In this paper, we present a collaborative CCPP algorithms for single robot and multi-robot systems. The incremental coverage from the robot movement is maximized by evaluating a new cost function. A goal selection function is then designed to facilitate the collaborative exploration for a multi-robot system. By considering the local gains from the individual robots as well as the global gain by the goal selection, the proposed method is able to optimize the overall coverage efficiency. In the experiments, our CCPP algorithms are carried out on various unknown and complex environment maps. The simulation results and performance evaluation demonstrate the effectiveness of the proposed collaborative CCPP technique.

Automated summary

In this paper, a collaborative CCPP algorithm for both single robot and multi-robot systems is proposed, maximizing incremental coverage through a new cost function and facilitating collaborative exploration through a goal selection function. Experiment results and performance evaluation show the effectiveness of the proposed technique.