Solving the multi-objective path planning problem in mobile robotics with a firefly-based approach

Abstract

Currently, autonomous robotics is one of the most interesting and researched areas of technology. At the beginning, robots only worked in the industrial sector but, gradually, they started to be introduced into other sectors such as medicine or social environments becoming part of society. In mobile robots, the path planning (PP) problem is one of the most researched topics. Taking into account that the PP problem is an NP-hard problem, multi-objective evolutionary algorithms (MOEAs) are good candidates to solve this problem. In this work, a new multi-objective approach based on the flashing behavior of fireflies in nature, the multi-objective firefly algorithm (MO-FA), is proposed to solve the PP problem. This proposed algorithm is a swarm intelligence algorithm. The proposed MO-FA handles three different objectives to obtain accurate and efficient solutions. These objectives are the following: the path safety, the path length, and the path smoothness (related to the energy conCommunicated by sumption). Furthermore, and to test the proposed MOEA, we have used eight realistic scenarios for the path's calculation. On the other hand, we also compare our proposal with other approaches of the state of the art, showing the advantages of MO-FA. In particular, to evaluate the obtained results we applied specific quality metrics. Moreover, to demonstrate the statistical evidence of the obtained results, we also performed a statistical analysis. Finally, the study shows that the proposed MO-FA is a good alternative to solve the PP problem.

Ant colony optimization and firefly algorithms for robotic motion planning in dynamic environments

“…Computer simulation results show that the FF algorithm can be successfully employed for optimal path finding in a dynamic environment. A multiobjective path planning algorithm by using FF has been proposed . Path safety, path length, and smoothness of the path are the major concerns.…”

“…FF algorithm is based on three rules: All fireflies are unisex, one FF will be attracted to other irrespective of their sex. Attractiveness is proportional to their brightness, dimmer will move toward the brighter one. Random movements when they are equally bright. Brightness of fireflies is proportional to the value of the Objective function …”

“…A multiobjective path planning algorithm by using FF has been proposed. 33 Path safety, path length, and smoothness of the path are the major concerns. Patle et al 34 explores an environment and improves the global search in a small number of iterations and hence it can be implemented for real-time obstacle avoidance in dynamic environments.…”

“…Brightness of fireflies is proportional to the value of the Objective function. 33,45 The light intensity I varies with the distance r, given by I = I 0 exp(− r), where I 0 is the original light intensity, and is the absorption coefficient that ranges between 0 and 1.…”

An Approach to Multi-Objective Path Planning Optimization for Underwater Gliders

“…A survey of 2016 [38], about applying nature inspired algorithms to solve MOPP, analysed the usage of particle swarm optimization, ant colony optimization, and artificial bee colony. In addition, Hidalgo et al [39] presented a firefly-based approach to pursue MOPP considering path safety, length, and smoothness, pointing to the path planning as one of the most researched topics in robotics.…”