Real-time obstacle avoidance for manipulators and mobile robots
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
Fido: vision and navigation for a robot rover
Fido: vision and navigation for a robot rover
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
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
Biologically inspired neural network approaches to real-time collision-free robot motion planning
Biologically inspired robot behavior engineering
Humanoid robots learning to walk faster: from the real world to simulation and back
Proceedings of the 2013 international conference on Autonomous agents and multi-agent systems
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In this paper, a novel neural network approach to real-time collision-free path planning of robot manipulators in a nonstationary environment is proposed, which is based on a biologically inspired neural network model for dynamic trajectory generation of a point mobile robot. The state space of the proposed neural network is the joint space of the robot manipulators, where the dynamics of each neuron is characterized by a shunting equation or an additive equation. The real-time robot path is planned through the varying neural activity landscape that represents the dynamic environment. The proposed model for robot path planning with safety consideration is capable of planning a real-time “comfortable” path without suffering from the “too close” nor “too far” problems. The model algorithm is computationally efficient. The computational complexity is linearly dependent on the neural network size. The effectiveness and efficiency are demonstrated through simulation studies.