A common assumption of coverage path planning research is a static environment. Such
environments require only a single visit to each area to achieve coverage. However, some …

Multiple autonomous robots are deployed to fulfill tasks collaboratively in real-world
applications with row-based settings as found in precision agriculture, warehouses, factory …

The multi-robot coverage problem is an essential building block for systems that perform
tasks like inspection, exploration, or search and rescue. We discretize the coverage problem …

Coverage of a partially known workspace for information gathering is the core problem for
several applications, such as search and rescue, precision agriculture and monitoring of …

We study graph-based Multi-Robot Coverage Path Planning (MCPP) that aims to compute
coverage paths for multiple robots to cover all vertices of a given 2D grid terrain graph G …

This paper considers a multi-target coverage problem where a robot team aims to efficiently
cover multi-targets while maintaining connectivity in a distributed manner. A novel …

We focus on the online multi-object k-coverage problem (OMOkC), where mobile robots are
required to sense a mobile target from k diverse points of view, coordinating themselves in a …

This paper demonstrates novel Complete Coverage Path Planning using reinforcement
learning to enable a robot to complete map coverage at high speeds in complicated …

We propose a centralized control framework to select suitable robots from a heterogeneous
pool and place them at appropriate locations to monitor a region for events of interest. In the …

Online coverage path planning to explore an unknown workspace with multiple
homogeneous robots could be either centralized or distributed. While distributed planners …