A multivalued logic approach to integrating planning and control
Artificial Intelligence - Special volume on planning and scheduling
Robot Motion Planning
Traversability Analysis and Path Planning for a Planetary Rover
Autonomous Robots
Guest Editorial: Agricultural Robotics
Autonomous Robots
A Genetic Algorithm for Robust Motion Planning
IEA/AIE '98 Proceedings of the 11th International Conference on Industrial and Engineering Applications of Artificial In telligence and Expert Systems: Tasks and Methods in Applied Artificial Intelligence
An efficient data-driven fuzzy approach to the motion planning problem of a mobile robot
Fuzzy Sets and Systems - Special issue: Fuzzy set techniques for intelligent robotic systems
Genetic Algorithms for Adaptive Motion Planning of an Autonomous Mobile Robot
CIRA '97 Proceedings of the 1997 IEEE International Symposium on Computational Intelligence in Robotics and Automation
Kinematics Modeling and Analyses of Articulated Rovers
IEEE Transactions on Robotics
Adaptive evolutionary planner/navigator for mobile robots
IEEE Transactions on Evolutionary Computation
Planning multiple paths with evolutionary speciation
IEEE Transactions on Evolutionary Computation
IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
Terrain roughness assessment for human assisted UGV navigation within heterogeneous terrains
ROBIO'09 Proceedings of the 2009 international conference on Robotics and biomimetics
Systematic kinematics analysis and balance control of high mobility rovers over rough terrain
Robotics and Autonomous Systems
| Hi-index | 0.20 |
The paper develops a hybrid intelligent approach to path planning for high mobility robots operating in rough environments. Path planning consists of characterization of the environment using a fuzzy logic framework, and a two-stage genetic algorithm planner. A global planner determines the path that optimizes a combination of terrain roughness and path curvature. A local planner uses sensory information, and in case of detection of previously unknown and unaccounted for obstacles, performs an on-line replanning to get around the newly discovered obstacle. Fuzzy adaptation of the genetic operators is achieved by adjusting the probabilities of the operators based on a diversity measure of the paths population and traversability measure of the paths. Path planning for an articulated rover in a rugged Mars terrain is presented to demonstrate the effectiveness of the proposed path planner.