Information-driven sensor path planning by approximate cell decomposition
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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
Modeling floor-cleaning coverage performances of some domestic mobile robots in a reduced scenario
Robotics and Autonomous Systems
Real-time robot path planning based on a modified pulse-coupled neural network model
IEEE Transactions on Neural Networks
Determination of robot drop location for military path planning using GIS application
CEA'10 Proceedings of the 4th WSEAS international conference on Computer engineering and applications
Path planning algorithm based on sub-region for agricultural robot
CAR'10 Proceedings of the 2nd international Asia conference on Informatics in control, automation and robotics - Volume 2
Probabilistic track coverage in cooperative sensor networks
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Cellular ants: A method to create collision free trajectories for a cooperative robot team
Robotics and Autonomous Systems
Bio-inspired navigation of mobile robots
AIS'12 Proceedings of the Third international conference on Autonomous and Intelligent Systems
Generation of environmental representation of a large indoor parking lot
ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part II
Compact Particle Swarm Optimization
Information Sciences: an International Journal
Collaborative path planning for event search and exploration in mixed sensor networks
International Journal of Robotics Research
The bio-inspired model based hybrid sliding-mode tracking control for unmanned underwater vehicles
Engineering Applications of Artificial Intelligence
A survey on coverage path planning for robotics
Robotics and Autonomous Systems
BA*: an online complete coverage algorithm for cleaning robots
Applied Intelligence
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Complete coverage path planning requires the robot path to cover every part of the workspace, which is an essential issue in cleaning robots and many other robotic applications such as vacuum robots, painter robots, land mine detectors, lawn mowers, automated harvesters, and window cleaners. In this paper, a novel neural network approach is proposed for complete coverage path planning with obstacle avoidance of cleaning robots in nonstationary environments. The dynamics of each neuron in the topologically organized neural network is characterized by a shunting equation derived from Hodgkin and Huxley's (1952) membrane equation. There are only local lateral connections among neurons. The robot path is autonomously generated from the dynamic activity landscape of the neural network and the previous robot location. The proposed model algorithm is computationally simple. Simulation results show that the proposed model is capable of planning collision-free complete coverage robot paths.