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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.

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.

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In this paper, a new multi-objective particle swarm algorithm based on the cooperative sub-warms, called cooperative evolvement multi-objective particle swarm optimization (CEMOPSO) algorithm, is proposed to tackle complex multi-objective optimization problems. This algorithm consists of multiple sub-swarms which connect each other by the ring topology. Each sub-swarm is designed to optimize one of the objectives, but the update of its particles is performed based on species seeds from neighbor sub-swarms. A self-adaptive escape operation is proposed to allow the particles to explore un-searched solution space. Moreover, an external archive of elite particles are incorporated into CEMOPSO to store non-dominated solutions found so far. By compared with five multi-objective optimization algorithms the simulation results indicate that the proposed algorithm has much better performance.

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