Multi-robot environmental coverage can be widely used in many applications like search
and rescue. However, it is challenging to coordinate the robot team for high coverage …

Deriving a distributed, time-efficient, and connectivity-guaranteed coverage policy in
multitarget environment poses huge challenges for a multirobot team with limited coverage …

This paper addresses the complete area coverage problem of a known environment by
multiple-robots. Complete area coverage is the problem of moving an end-effector over all …

Field coverage is a representative exploration task that has many applications ranging from
household chores to navigating harsh and dangerous environments. Autonomous mobile …

Multi-agent area coverage aims to accomplish the complete traversal of the target area
through cooperation between agents. Focusing on the problems of low coverage efficiency …

Multi-robot research has significant progress in many aspects. Recently, it tends to reduce
the time task type such as disaster relief and exploration. Multi-robots in large environments …

With the increasing complexity of patrol tasks, the use of deep reinforcement learning for
collaborative coverage path planning (CPP) of multi-mobile robots has become a new …

This paper develops a new approach to direct a set of heterogeneous agents, varying in
mobility and sensing capabilities, to quickly cover a large region, say for example in the …

This paper presents a novel multi-robot coverage path planning (CPP) algorithm-aka
SCoPP-that provides a time-efficient solution, with workload balanced plans for each robot …

Centralized approaches for multi-robot coverage planning problems suffer from the lack of
scalability. Learning-based distributed algorithms provide a scalable avenue in addition to …