Abstract
Birds, bees, and fish often flock together in groups based on local information. Inspired by this natural phenomenon, flocking control algorithms are designed to coordinate the activities of multiple agents in cluttered and noisy environments, respectively. First, to allow agents to track and observe a target better in cluttered environments, an adaptive flocking control algorithm is proposed.With this algorithm, all agents can track the target better and maintain a similar formation and connectivity. Second, to deal with noisy measurements we proposed two flocking control algorithms, Multi-CoM-Shrink and Multi-CoM-Cohesion. Based on these algorithms, all agents can form a network and maintain connectivity, even with noisy measurements. We also investigate the stability and scalability of our algorithms. Simulations and real experiments are conducted to demonstrate the effectiveness of the proposed approach.
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La, H.M., Sheng, W. (2011). Flocking Control Algorithms for Multiple Agents in Cluttered and Noisy Environments. In: Meng, Y., Jin, Y. (eds) Bio-Inspired Self-Organizing Robotic Systems. Studies in Computational Intelligence, vol 355. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-20760-0_3
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DOI: https://doi.org/10.1007/978-3-642-20760-0_3
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