Deep reinforcement learning has produced many success stories in recent years. Some
example fields in which these successes have taken place include mathematics, games …

Recently, multiple unmanned vehicles have attracted a great deal of attention as viable
solutions to a wide variety of civilian and military applications. Among many topics in the …

Swarm Robotics (SR) is an extension of the study of Multi-Robot Systems that exploits
concepts of communication, coordination and collaboration among a large number of robots …

This paper introduces recently developed Aquila Optimization Algorithm specifically
configured for Multi-Robot space exploration. The proposed hybrid framework “Coordinated …

We present a method for autonomous exploration in complex three-dimensional (3D)
environments. Our method demonstrates exploration faster than the current state-of-the-art …

Autonomous underwater vehicles (AUVs) are cost-and time-efficient systems for
environmental sampling. Informative adaptive sampling has been shown to be an effective …

This paper proposes iTD3-CLN, a Deep Reinforcement Learning (DRL) based low-level
motion controller, to achieve map-less autonomous navigation in dynamic scene. We …

We study the problem of multi-robot active mapping, which aims for complete scene map
construction in minimum time steps. The key to this problem lies in the goal position …

This paper aims to present a cooperative and distributed navigation strategy, that is an on-
line path planner, for an autonomous multi-robot system. The robots are intended to …

This paper describes a novel framework for autonomous exploration in large and complex
environments. We show that the framework is efficient as a result of its hierarchical structure …