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
Multi robot exploration using a modified a algorithm
ACIIDS'11 Proceedings of the Third international conference on Intelligent information and database systems - Volume Part I
A focused wave front algorithm for mobile robot path planning
HAIS'11 Proceedings of the 6th international conference on Hybrid artificial intelligent systems - Volume Part I
Robotic path planning using hybrid genetic algorithm particle swarm optimisation
International Journal of Information and Communication Technology
Efficient strategy for co-ordinated multirobot exploration
Proceedings of the Second International Conference on Computational Science, Engineering and Information Technology
Journal of Intelligent and Robotic Systems
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Robotic Path planning is one of the most studied problems in the field of robotics. The problem has been solved using numerous statistical, soft computing and other approaches. In this paper we solve the problem of robotic path planning using a combination of A* algorithm and Fuzzy Inference. The A* algorithm does the higher level planning by working on a lower detail map. The algorithm finds the shortest path at the same time generating the result in a finite time. The A* algorithm is used on a probability based map. The lower level planning is done by the Fuzzy Inference System (FIS). The FIS works on the detailed graph where the occurrence of obstacles is precisely known. The FIS generates smoother paths catering to the non-holonomic constraints. The results of A* algorithm serve as a guide for FIS planner. The FIS system was initially generated using heuristic rules. Once this model was ready, the fuzzy parameters were optimized using a Genetic Algorithm. Three sample problems were created and the quality of solutions generated by FIS was used as the fitness function of the GA. The GA tried to optimize the distance from the closest obstacle, total path length and the sharpest turn at any time in the journey of the robot. The resulting FIS was easily able to plan the path of the robot. We tested the algorithm on various complex and simple paths. All paths generated were optimal in terms of path length and smoothness. The robot was easily able to escape a variety of obstacles and reach the goal in an optimal manner.