Learning metric-topological maps for indoor mobile robot navigation
Artificial Intelligence
An algorithm for planning collision-free paths among polyhedral obstacles
Communications of the ACM
Multistrategy Adaptive Path Planning
IEEE Expert: Intelligent Systems and Their Applications
The virtual wall approach to limit cycle avoidance for unmanned ground vehicles
Robotics and Autonomous Systems
Real-Time Path Planning in Dynamic Virtual Environments Using Multiagent Navigation Graphs
IEEE Transactions on Visualization and Computer Graphics
Fuzzy logic techniques for navigation of several mobile robots
Applied Soft Computing
On redundancy, efficiency, and robustness in coverage for multiple robots
Robotics and Autonomous Systems
Robotics and Autonomous Systems
An experimental study of distributed robot coordination
Robotics and Autonomous Systems
Recurrent neuro fuzzy control design for tracking of mobile robots via hybrid algorithm
Expert Systems with Applications: An International Journal
On the design of an obstacle avoiding trajectory: Method and simulation
Mathematics and Computers in Simulation
A comparative study on some navigation schemes of a real robot tackling moving obstacles
Robotics and Computer-Integrated Manufacturing
CSIE '09 Proceedings of the 2009 WRI World Congress on Computer Science and Information Engineering - Volume 04
Disassembly Path Planning for Complex Articulated Objects
IEEE Transactions on Robotics
A Complete and Scalable Strategy for Coordinating Multiple Robots Within Roadmaps
IEEE Transactions on Robotics
Physical Path Planning Using a Pervasive Embedded Network
IEEE Transactions on Robotics
Path planning of 3-D objects using a new workspace model
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Adaptive evolutionary planner/navigator for mobile robots
IEEE Transactions on Evolutionary Computation
A neuro-fuzzy controller for mobile robot navigation and multirobotconvoying
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
New approach to intelligent control systems with self-exploring process
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A fast path planning by path graph optimization
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
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Path Planning is a classical problem in the field of robotics. The problem is to find a path of the robot given the various obstacles. The problem has attracted the attention of numerous researchers due to the associated complexities, uncertainties and real time nature. In this paper we propose a new algorithm for solving the problem of path planning in a static environment. The algorithm makes use of an algorithm developed earlier by the authors called Multi-Neuron Heuristic Search (MNHS). This algorithm is a modified A^@? algorithm that performs better than normal A^@? when heuristics are prone to sharp changes. This algorithm has been implemented in a hierarchical manner, where each generation of the algorithm gives a more detailed path that has a higher reaching probability. The map used for this purpose is based on a probabilistic approach where we measure the probability of collision with obstacle while traveling inside the cell. As we decompose the cells, the cell size reduces and the probability starts to touch 0 or 1 depending upon the presence or absence of obstacles in the cell. In this approach, it is not compulsory to run the entire algorithm. We may rather break after a certain degree of certainty has been achieved. We tested the algorithm in numerous situations with varying degrees of complexities. The algorithm was able to give an optimal path in all the situations given. The standard A^@? algorithm failed to give results within time in most of the situations presented.