How genetic algorithms work: a critical look at implicit parallelism
Proceedings of the third international conference on Genetic algorithms
Foundations of robotics: analysis and control
Foundations of robotics: analysis and control
Modeling and simulation of neuroadaptive controllers for robot manipulators
International Journal in Computer Simulation - Special issue: intelligent simulation of high autonomy systems
Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control and Artificial Intelligence
Robot Motion Planning
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Introduction to Robotics: Mechanics and Control
Introduction to Robotics: Mechanics and Control
An Evolutionary Algorithm with Non-random Initial Population for Path Planning of Manipulators
IEA/AIE '09 Proceedings of the 22nd International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems: Next-Generation Applied Intelligence
| Hi-index | 0.00 |
The authors present a straightforward method to handle collision avoidance among multiple robot manipulators. The method allows manipulators to move from a specified starting point to a goal without colliding with any other manipulators or objects in their 3D environment. Potential fields guide the end-effector through Cartesian space, and parallel genetic algorithms calculate suitable joint variables for the manipulator trajectory.