Robot path planning in uncertain environment using multi-objective particle swarm optimization

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...

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Bibliographic Details
Published in:Neurocomputing (Amsterdam) 2013-03, Vol.103, p.172-185
Main Authors: Zhang, Yong, Gong, Dun-wei, Zhang, Jian-hua
Format: Article
Language:English
Subjects:
Algorithmics. Computability. Computer arithmetics
Algorithms
Applied sciences
Computer science
control theory
systems
Constraints
Control theory. Systems
Danger source
Exact sciences and technology
Fires
Mathematics
Multi- objective optimization
Navigation
Optimization
Particle swarm optimization
Path planning
Probability and statistics
Risk
Robot path planning
Robotics
Robots
Sampling theory, sample surveys
Sciences and techniques of general use
Statistics
Theoretical computing
Uncertainty
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Summary: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.
ISSN:0925-2312
1872-8286
DOI:10.1016/j.neucom.2012.09.019