Multi-Robot Informative Path Planning in Unknown Environments Through Continuous Region Partitioning

This research activity is primarily focused to obtain information from an environment with the help of a group of coordinated robots. Each robot is responsible to plan its path independently but the robots, as an overall system, have a common goal of maximum information collection. This domain of research is known as Multi-Robot Informative Path Planning (MIPP). MIPP is very motivating due to its challenging nature and numerous real-world applications. It has shown its presence from semiautomatic applications like robotic search and rescue to fully automatic applications like interplanetary missions. We consider the NP-Hard problem of MIPP in an unknown environment having communication constraints and budget limitations in robots. We propose a novel approach that uses continuous region partitioning to efficiently divide an initially unknown environment among the robots based on the discovered obstacles. The research objective is to collect a higher amount of information and target a uniform work distribution among robots. Simulation results show that our proposed approach is successful in reducing the initial imbalance in the work distribution of the robots while ensuring close-to-reality spatial modeling within a reasonable amount of time

Multi-robot informative path planning in unknown environments through continuous region partitioning

We consider the NP-hard problem of multirobot informative path planning in the presence of communication constraints, where the objective is to collect higher amounts of information of an ambient phenomenon. We propose a novel approach that uses continuous region partitioning into Voronoi components to efficiently divide an initially unknown environment among the robots based on newly discovered obstacles enabling improved load balancing between robots. Simulation results show that our proposed approach is successful in reducing the initial imbalance of the robots’ allocated free regions while ensuring close-to-reality spatial modeling within a reasonable amount of time