Robust Monte Carlo localization for mobile robots
Artificial Intelligence
Navigating Mobile Robots: Systems and Techniques
Navigating Mobile Robots: Systems and Techniques
Multi-robot collaboration for robust exploration
Annals of Mathematics and Artificial Intelligence
A Probabilistic Approach to Collaborative Multi-Robot Localization
Autonomous Robots
Exploring artificial intelligence in the new millennium
ANTS'10 Proceedings of the 7th international conference on Swarm intelligence
Performance analysis of multirobot Cooperative localization
IEEE Transactions on Robotics
ANTS'10 Proceedings of the 7th international conference on Swarm intelligence
A “thermodynamic” approach to multi-robot cooperative localization
Theoretical Computer Science
A "Thermodynamic" approach to multi-robot cooperative localization with noisy sensors
ANTS'12 Proceedings of the 8th international conference on Swarm Intelligence
| Hi-index | 0.00 |
We propose a new approach to the simultaneous cooperative localization of a group of robots capable of sensing their own motion on the plane and the relative position of nearby robots. In the last decade, the use of distributed optimal Kalman filters (KF) to solve this problem have been studied extensively. In this paper, we propose to use a suboptimal Kalman filter (denoted by EA). EA requires significantly less computation and communication resources then KF. Furthermore, in some cases, EA provides better localization. In this paper EA is analyzed in a soft "thermodynamic" fashion i.e. relaxing assumptions are used during the analysis. The goal is not to derive hard lower or upper bounds but rather to characterize the robots expected behavior. In particular, to predict the expected localization error. The predictions were validated using simulations. We believe that this kind of analysis can be beneficial in many other cases.