PSO-Based Robot Path Planning for Multisurvivor Rescue in Limited Survival Time

Abstract

Since the strength of a trapped person often declines with time in urgent and dangerous circumstances, adopting a robot to rescue as many survivors as possible in limited time is of considerable significance. However, as one key issue in robot navigation, how to plan an optimal rescue path of a robot has not yet been fully solved. This paper studies robot path planning for multisurvivor rescue in limited survival time using a representative heuristic, particle swarm optimization (PSO). First, the robot path planning problem including multiple survivors is formulated as a discrete optimization one with high constraint, where the number of rescued persons is taken as the unique objective function, and the strength of a trapped person is used to constrain the feasibility of a path. Then, a new integer PSO algorithm is presented to solve the mathematical model, and several new operations, such as the update of a particle, the insertion and inversion operators, and the rapidly local search method, are incorporated into the proposed algorithm to improve its effectiveness. Finally, the simulation results demonstrate the capacity of our method in generating optimal paths with high quality.

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

“…The applications of multi-objective optimization to evolutionary robotics are diverse with increasing attention. Some common applications for multi-robot teams include foraging [20], path planning [48], search [10], distributed surveillance and security [37], search and rescue [42] [5], and emergency service [19,38,1]. However, many of these formulations have attempted to solve multiple objectives at the same time and researchers often model these common capabilities by defining them as optimization problems.…”

Particle swarm optimization algorithm for the optimization of rescue task allocation with uncertain time constraints

“…Very limited evidence can be found in the literature that rigorously assesses the time factor in robot rescue. Some of our previous researches show positive outcomes in handling complex rescue routes in real-world situations with an assumption that survival time values are stable [18,19]. Still, estimating the survival time in destructive locations remains a challenging issue.…”

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

“…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.…”