Real-time obstacle avoidance for manipulators and mobile robots
International Journal of Robotics Research
Toward efficient trajectory planning: the path-velocity decomposition
International Journal of Robotics Research
A search algorithm for motion planning with six degrees of freedom
Artificial Intelligence
Topology-conserving maps for learning visuo-motor-coordination
Neural Networks
2D path planning: a configuration space heuristic approach
International Journal of Robotics Research
Neural models for sustained and ON-OFF units of insect lamina
Biological Cybernetics
Robot motion planning: a distributed representation approach
International Journal of Robotics Research
A neural theory of retino-cortical dynamics
Neural Networks
Neural network dynamics for path planning and obstacle avoidance
Neural Networks
Robot Motion Planning
Reinforcement Learning in the Multi-Robot Domain
Autonomous Robots
Robot Path Planning Using Fluid Model
Journal of Intelligent and Robotic Systems
Neural network approaches to real-time motion planning and control of robotic systems
Neural network approaches to real-time motion planning and control of robotic systems
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Real-time map building and navigation for autonomous robots inunknown environments
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Automatic path planning for a mobile robot among obstacles ofarbitrary shape
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Neural network approaches to dynamic collision-free trajectorygeneration
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A heuristic search algorithm for path determination with learning
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Primal and dual neural networks for shortest-path routing
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Shortest path planning on topographical maps
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
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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.