Abstract
A method is presented and tested for planning time optimal trajectories for a mobile robot with constraints by using an evolutionary technique with neural-networks components. The method establishes shortest time trajectories redefined to form a multi-constrained non-linear global optimization problem. The trajectory components such as the turning translational speeds of the mobile robot (i.e. the parameter vector of the problem) are found by using differential evolution algorithm (DE) to obtain the time optimally. DE is a floating-point genetic algorithm. Artificial neural networks learn kinematics structure and upper bound of the velocities on the trajectory. Experiments are successfully implemented on Nomad 2000 mobile robot.
| Original language | English |
|---|---|
| Title of host publication | 2003 IEEE International Symposium on Industrial Electronics, ISIE 2003 |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 352-357 |
| Number of pages | 6 |
| ISBN (Electronic) | 0780379128 |
| DOIs | |
| Publication status | Published - 2003 |
| Event | IEEE International Symposium on Industrial Electronics, ISIE 2003 - Rio de Janeiro, Brazil Duration: 9 Jun 2003 → 11 Jun 2003 |
Publication series
| Name | IEEE International Symposium on Industrial Electronics |
|---|---|
| Volume | I |
Conference
| Conference | IEEE International Symposium on Industrial Electronics, ISIE 2003 |
|---|---|
| Country/Territory | Brazil |
| City | Rio de Janeiro |
| Period | 9/06/03 → 11/06/03 |
Bibliographical note
Publisher Copyright:© 2003 IEEE.
Keywords
- Artificial neural networks
- Genetic algorithms
- Kinematics
- Mobile robots
- Neural networks
- Optimization methods
- Testing
- Trajectory
- Turning
- Upper bound
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