New GA Applied Route Calculation for Multiple Robots with Energy Restrictions

EasyChair Preprint 3809

Abstract

One of the biggest challenges encountered in mobile robots is in the planning of their trajectory and has received attention from researchers, both in industry and academia, as its development is directly related to the greater autonomy of robots. The complexity of the robot movement planning problem has motivated the development of several algorithms. This stems from the need to integrate robot navigation with sensing, efficiency, and route planning, as well as the need to save important resources such as energy, and the participation of multiple robots. Most robot missions that must meet multiple points in addition to having a cost to complete and weights that determine your priority of service if they prove to be quite complex, there is still the possibility that several robots in different locations collaboratively participate in the mission, which increases the complexity of the problem. This paper presents a route calculation approach for multiple collaborative robots with energy constraints and base shifting. The proposed method was modeled using the team orieentering problem (TOP) combined with the multiple knapsack problem (MKP), this combination allows each robot to have a unique energy restriction different from the traditional TOP where all agents have the same restriction value, to solve The problem a genetic algorithm was developed. To verify the effectiveness of the method, a comparison was made between the results obtained by the method proposed by [1] and that of this work. The results show a good efficiency in solving the problem.

Keyphrases: Path Planing, Team Orienteering Problem, metaheuristic algorithm, multi-robot

@booklet{EasyChair:3809,
  author    = {Killdary Aguiar de Santana and Vandilberto Pereira Pinto and Darielson A. Souza and João Lucas Oliveira Torres and Igor Antônio Gomes Teles},
  title     = {New GA Applied Route Calculation for Multiple Robots with Energy Restrictions},
  howpublished = {EasyChair Preprint 3809},
  year      = {EasyChair, 2020}}