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
Topology-conserving maps for learning visuo-motor-coordination
Neural Networks
Neural network dynamics for path planning and obstacle avoidance
Neural Networks
Image segmentation based on oscillatory correlation
Neural Computation
SCG '85 Proceedings of the first annual symposium on Computational geometry
An algorithm for planning collision-free paths among polyhedral obstacles
Communications of the ACM
Robot Path Planning Using Fluid Model
Journal of Intelligent and Robotic Systems
Neural-network-based path planning for a multirobot system with moving obstacles
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews - Special issue on information reuse and integration
Virtual assembly with biologically inspired intelligence
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Autonomic mobile sensor network with self-coordinated task allocation and execution
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Neurofuzzy-Based Approach to Mobile Robot Navigation in Unknown Environments
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Real-time map building and navigation for autonomous robots inunknown environments
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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 neural network approach to complete coverage path planning
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
An efficient dynamic system for real-time robot-path planning
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Real-Time Robot Path Planning via a Distance-Propagating Dynamic System with Obstacle Clearance
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Mobile robot navigation in 2-D dynamic environments using an electrostatic potential field
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Finding the shortest path in the shortest time using PCNN's
IEEE Transactions on Neural Networks
Object detection using pulse coupled neural networks
IEEE Transactions on Neural Networks
Real-time collision-free motion planning of a mobile robot using a Neural Dynamics-based approach
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
A columnar competitive model for solving combinatorial optimization problems
IEEE Transactions on Neural Networks
Image shadow removal using pulse coupled neural network
IEEE Transactions on Neural Networks
A Neural Network Approach to Dynamic Task Assignment of Multirobots
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
Locally excitatory globally inhibitory oscillator networks
IEEE Transactions on Neural Networks
AICI'11 Proceedings of the Third international conference on Artificial intelligence and computational intelligence - Volume Part II
A novel neural network method for shortest path tree computation
Applied Soft Computing
Fuzzy Neural Network Control for Robot Manipulator Directly Driven by Switched Reluctance Motor
International Journal of Cognitive Informatics and Natural Intelligence
Hi-index | 0.01 |
This paper presents a modified pulse-coupled neural network (MPCNN) model for real-time collision-free path planning of mobile robots in nonstationary environments. The proposed neural network for robots is topologically organized with only local lateral connections among neurons. It works in dynamic environments and requires no prior knowledge of target or barrier movements. The target neuron fires first, and then the firing event spreads out, through the lateral connections among the neurons, like the propagation of a wave. Obstacles have no connections to their neighbors. Each neuron records its parent, that is, the neighbor that caused it to fire. The real-time optimal path is then the sequence of parents from the robot to the target. In a static case where the barriers and targets are stationary, this paper proves that the generated wave in the network spreads outward with travel times proportional to the linking strength among neurons. Thus, the generated path is always the global shortest path from the robot to the target. In addition, each neuron in the proposed model can propagate a firing event to its neighboring neuron without any comparing computations. The proposed model is applied to generate collision-free paths for a mobile robot to solve a maze-type problem, to circumvent concave U-shaped obstacles, and to track a moving target in an environment with varying obstacles. The effectiveness and efficiency of the proposed approach is demonstrated through simulation and comparison studies.