Comparison of bio-inspired algorithms applied to the coordination of mobile robots considering the energy consumption

Abstract

Many applications related to autonomous mobile robots require to explore in an unknown environment searching for static targets, without any a priori information about the environment topology and target locations. Targets in such rescue missions can be fire, mines, human victims, or dangerous material that the robots have to handle. In these scenarios, some cooperation among the robots is required for accomplishing the mission. This paper focuses on the application of different bio-inspired metaheuristics for the coordination of a swarm of mobile robots that have to explore an unknown area in order to rescue some distributed targets. This problem is formulated by first defining an optimization model and then considering two subproblems: exploration and recruiting. Firstly, the environment is incrementally explored by robots using a modified version of ant colony optimization. Then, when a robot detects a target, a recruiting mechanism is carried out to recruit more robots to carry out the disarm task together. For this purpose, we have proposed and compared three approaches based on three different bio-inspired algorithms (Firefly Algorithm, Particle Swarm Optimization and Artificial Bee Algorithm). A computational study and extensive simulations have been carried out to assess the behavior of the proposed approaches and to analyze their performance in terms of total energy consumed by the robots to complete

Swarm Robotics and Rapidly Exploring Random Graph Algorithms Applied to Environment Exploration and Path Planning

“…One aim of environments exploration is to know a throughout region, with the purpose of detect some targets distributed randomly in the area [5]. For accomplish this task, the most applied algorithms are bio-inspired.…”

“…For accomplish this task, the most applied algorithms are bio-inspired. Among the approaches that have been developed, the stigmergy used by many colonies of insects to coordinate their activities, which allows a swarm indirect communication, has been widely researched [5], [6].…”

“…Palmieri et al implemented and tested three biologically inspired coordination strategies: firefly, particle swarm and artificial bee behaviour [5], the better performance was obtained with the firefly-based strategy. This scheme focus on finding targets and recruit robots around them.…”

Self-adaptive decision-making mechanisms to balance the execution of multiple tasks for a multi-robots team

“…In this way, the robots will prefer less frequently visited regions and it is more likely that it will direct towards an unexplored region. The exploration strategy is detailed in Algorithm 1 and it was previously validated [33]. At the first iteration of Algorithm 1, all cells are initialized with the same value of the pheromone trail, set to be zero that represents that the cells have not yet been visited by any of the robots, so that the initial probabilities that a cell would be chosen is almost random.…”

Swarm robotics in wireless distributed protocol design for coordinating robots involved in cooperative tasks

“…Recruitment plays a central role in social insects such as ants, bees, and termites. The recruitment task is a particular self-organization cooperative task in which robots need to aggregate in a point in order to accomplish a task as explained in [33], [34]. Other approches used chemical substances to recruit the robot for certain tasks, which was inspired by pheromone of some species of social insects, such as ants and termites [26], [36].…”