Robot path planning in an environment with many terrains based on interval multi-objective PSO

Abstract

In order to solve the problem of path planning in an environment with many terrains, we propose a method based on interval multi-objective Particle Swarm Optimization (PSO). First, the environment is modeled by the line partition method, and then, according to the distribution of the polygonal lines which form the robot path and taking the velocity's disturbance into consideration, robot's passing time is formulated as an interval by combining Local Optimal Criterion (LOC), and the path's danger degree is estimated through the area ratio between the robot path and the danger source. In addition, the path length is also calculated as an optimization objective. As a result, the robot path planning problem is modeled as an optimization problem with three objectives. Finally, the interval multiobjective PSO is employed to solve the problem above. Simulation and experimental results verify the effectiveness of the proposed method.

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

“…In Ahmed and Deb (2011), Chang and Liu (2009), and Davoodi et al (2013), different variants of NSGA-II were proposed, which also use two objectives: the path length and the path safety (referred to the obstacles), but ignoring the energy consumption. Other works in which the authors used these same objectives, also without taking into account the energy consumption, can be found in Gong et al (2011), Geng et al (2013, Wang et al (2009), andZhang et al (2013). On the other hand, Sedaghat also applied NSGA-II and proposed a variant of these two previous objectives, in particular she used the path length in combination with the path difficulty to solve the problem in Sedaghat (2011), also ignoring the energy consumption.…”

Multi-UAV Reconnaissance Task Assignment for Heterogeneous Targets Based on Modified Symbiotic Organisms Search Algorithm

“…The original MOSOS algorithm works on the cooperative behavior seen among organisms in nature. During the search process, each organism benefits from continuous interaction with others in three different phases [24]: mutualism, commensalism and parasitism. Mutualism allows the two sides to benefit from each other; commensalism benefits one party, while the other party is not affected; parasitism benefits one party, and the other party suffers.…”

“…Group intelligent optimization methods (such as Bayesian approach [15]) are employed to solve the problem with independent task points in a static environment within limited space, which can reduce the complexity of modeling and calculation. In recent years, heuristic and bio-inspired algorithms based group intelligent optimization methods have been adopted to solve the multi-objective optimization problem, including the simulated annealing algorithm [16], the Tabu search algorithm [17], genetic algorithms (NSGA-II) [18,19], the artificial fish school algorithm [20], ant colony algorithm [21] and the particle swarm optimization (niching PSO) [22,23,24,25]. These algorithms perform better than most traditional mathematical techniques in solving these problems, because they do not require substantial gradient information.…”

Path Planning Algorithm for Multi-Locomotion Robot Based on Multi-Objective Genetic Algorithm with Elitist Strategy

“…In [26], the researchers proposed a path planning method based on interval multiobjective PSO; this method concentrates on three objectives: path time, path safety, and path length. In the optimization of path safety, this method takes the road condition into consideration, which has enlightening values to our works, but the applied objects of these methods are traditional wheeled robots; so, this method is not suitable for MLR.…”