Biologically inspired neural network approaches to real-time collision-free robot motion planning

In this chapter, a framework, based on biologically inspired neural networks, is proposed for real-time collision-free robot motion planning in a nonstationary environment. Each neuron in the topologically organized neural network is characterized by a shunting equation. The developed algorithms can be applied to point mobile robots, manipulation robots, car-like mobile robots, and multi-robot systems. The real-time optimal robot motion is planned through the dynamic neural activity landscape without explicitly searching over the free workspace or the collision paths, without any prior knowledge of the dynamic environment, without any learning procedures, and without any local collision checking procedures at each step of robot movement. Therefore the proposed algorithms are computationally efficient. The computational complexity linearly depends on the neural network size. The system stability is guaranteed by qualitative analysis and the Lyapunov stability theory. The effectiveness and efficiency of the proposed approaches are demonstrated by simulation and comparison studies.

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