Towards Emerging Cubic Spline Patterns with a Mobile Robotics Swarm System
KAUST DepartmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Environmental Statistics Group
KAUST Grant NumberOSR-2019-CRG7-3800
Permanent link to this recordhttp://hdl.handle.net/10754/667073
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AbstractAn innovative and flexible approach is introduced to address the challenge of self-organizing a group of mobile robots into cubic-spline based patterns without any requirement of control points. Besides the self-organization of mobile robots, the approach incorporates a potential field-based control for obstacle/collision avoidance. This will offer more flexibility to swarm robots to efficiently dealing with many practical situations including smoothly avoiding obstacles during movement, or exploring and covering areas with complex curved patterns. Essentially, this challenge is approached by proposing a formation control model basing on a Smoothed Particle Hydrodynamic estimation technique, which uses special cubic-spline kernel functions applied here to interpolate the density of each robot in the swarm. The moving information is used to weight the distances to the robot’s neighbours available in its field of view. Then an artificial physics mesh is finally built among each robot and its three available neighbours having the smallest weighted distances. Significant results toward emerging cubic-spline patterns are shown with a swarm of foot-bot mobile robots simulated in the ARGoS platform. Analysis results with different metrics are also conducted to assess the performance of the model with different swarm sizes and in the presence of sensory noise as well in the presence of partially faulty robots.
CitationKhaldi, B., Harrou, F., Cherif, F., & Sun, Y. (2021). Towards Emerging Cubic Spline Patterns with a Mobile Robotics Swarm System. IEEE Transactions on Cognitive and Developmental Systems, 1–1. doi:10.1109/tcds.2021.3054997
SponsorsThis work is based upon a collaboration work supported by the King Abdullah University of Science and Technology (KAUST) Office of Sponsored Research (OSR) under Award No: OSR-2019-CRG7-3800, the École Supérieure en Informatique, 08 Mai 1945, Sidi Bel Abbés, Algeria, and the LESIA Laboratory, Department of Computer Science, University of Mohamed Khider, Biskra, Algeria.