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Robot path planning using swarms of active particles

Robot path planning using swarms of active particles

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There exist a wide range of techniques that generate collision-free (optimal) trajectories, in an autonomous fashion, for mobile robotics. Probably, the most popular technique is that of artificial potential fields, where the robot is treated as a particle subject to a potential field that is generated by the obstacles and the goal position. The path generation problem is then treated as an optimization problem where gradient descent methods have been traditionally used. Particle swarm optimization has also been widely used to solve optimization problems, and although it has proven to be more efficient in the search of minima, it also suffers from early convergence, i.e. the swarm may get trapped in a local minima. Many of the modifications that cater for this weakness involve added complexity in the construction of the potential field and thus translates into ad-hoc algorithms. A particle swarm approach with the property of escaping local minima by forcing vortex-like dynamics when the gradient of the potential field is close to zero is proposed, proving to be more compact and intuitive than previously proposed algorithms.

Chapter Contents:

  • Abstract
  • 8.1 Introduction
  • 8.2 Path planning for mobile robots
  • 8.3 Active particle swarms
  • 8.3.1 Particle dynamics
  • 8.3.2 Particle swarm model
  • 8.3.2.1 Energy analysis
  • 8.3.2.2 Analysis of angular momentum
  • 8.3.3 Qualitative behavior of the swarm
  • 8.4 Vortex particle swarm path planning algorithm for APF
  • 8.5 Experimental results
  • 8.5.1 Convex external potential fields
  • 8.5.2 External potential fields with local minima
  • 8.5.3 External potential fields for barrier-type obstacles
  • 8.5.4 External potential fields for narrow passage obstacles
  • 8.5.5 External potential fields for U-type obstacles
  • 8.5.6 Statistical analysis
  • 8.6 Conclusions and future work
  • References

Inspec keywords: path planning; collision avoidance; particle swarm optimisation; mobile robots; optimisation; gradient methods

Other keywords: popular technique; gradient descent methods; path generation problem; collision-free trajectories; active particles; potential field; goal position; particle swarm approach; artificial potential fields; optimal; mobile robotics; optimization problem; robot path; autonomous fashion; local minima; particle subject; particle swarm optimization

Subjects: Optimisation techniques; Mobile robots; Optimisation techniques; Spatial variables control

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