Abstract
This paper addresses autonomous intelligent navigation of mobile robotic platforms based on the recently reported algorithms of language-measure- theoretic optimal control. Real-time sensor data and model-based information on the robot's motion dynamics are fused to construct a probabilistic finite state automaton model that dynamically computes a time-dependent discrete-event supervisory control policy. The paper also addresses detection and avoidance of livelocks that might occur during execution of the robot navigation algorithm. Performance and robustness of autonomous intelligent navigation under the proposed algorithm have been experimentally validated on Segway RMP robotic platforms in a laboratory environment.
| Original language | English |
|---|---|
| Pages (from-to) | 13-26 |
| Number of pages | 14 |
| Journal | International Journal of Control |
| Volume | 82 |
| Issue number | 1 |
| DOIs | |
| State | Published - Jan 2009 |
Funding
This work has been supported in part by the U.S. Army Research Laboratory and the U.S. Army Research Office under Grant Nos. W911NF-07-1-0376 and W911NF-06-1-0469.
| Funders |
|---|
| U.S. Army Research Office |
| Army Research Laboratory |
Keywords
- Discrete event supervisory control
- Language measure
- Robot navigation and obstacle avoidance
ASJC Scopus subject areas
- Control and Systems Engineering
- Computer Science Applications
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