An algorithm for planning collision-free paths among polyhedral obstacles
Communications of the ACM
Multiobjective Optimization Using Evolutionary Algorithms - A Comparative Case Study
PPSN V Proceedings of the 5th International Conference on Parallel Problem Solving from Nature
Cultural Particle Swarm Algorithms for Constrained Multi-objective Optimization
ICCS '07 Proceedings of the 7th international conference on Computational Science, Part IV: ICCS 2007
Neural-network-based path planning for a multirobot system with moving obstacles
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews - Special issue on information reuse and integration
Constrained multi-objective optimization using steady state genetic algorithms
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartI
Simulated annealing with adaptive neighborhood: A case study in off-line robot path planning
Expert Systems with Applications: An International Journal
On convergence of the multi-objective particle swarm optimizers
Information Sciences: an International Journal
Collision-Free path planning for mobile robots using chaotic particle swarm optimization
ICNC'05 Proceedings of the First international conference on Advances in Natural Computation - Volume Part III
A novel path planning approach based on AppART and particle swarm optimization
ISNN'05 Proceedings of the Second international conference on Advances in Neural Networks - Volume Part III
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
Handling multiple objectives with particle swarm optimization
IEEE Transactions on Evolutionary Computation
A Multiobjective Optimization-Based Evolutionary Algorithm for Constrained Optimization
IEEE Transactions on Evolutionary Computation
Multiobjective Particle Swarm Algorithm With Fuzzy Clustering for Electrical Power Dispatch
IEEE Transactions on Evolutionary Computation
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In many real-world applications, workspace of robots often involves various danger sources that robots must evade, such as fire in rescue mission, landmines and enemies in war field. Since it is either impossible or too expensive to get their precise positions, decision-makers know only their action ranges in most cases. This paper proposes a multi-objective path planning algorithm based on particle swarm optimization for robot navigation in such an environment. First, a membership function is defined to evaluate the risk degree of path. Considering two performance merits: the risk degree and the distance of path, the path planning problem with uncertain danger sources is described as a constrained bi-objective optimization problem with uncertain coefficients. Then, a constrained multi-objective particle swarm optimization is developed to tackle this problem. Several new operations/improvements such as the particle update method based on random sampling and uniform mutation, the infeasible archive, the constrained domination relationship based on collision times with obstacles, are incorporated into the proposed algorithm to improve its effectiveness. Finally, simulation results demonstrate the capability of our method to generate high-quality Pareto optimal paths.