Performance characteristics of vision algorithms
Machine Vision and Applications - Special issue on performance evaluation
Stanley: The robot that won the DARPA Grand Challenge: Research Articles
Journal of Robotic Systems - Special Issue on the DARPA Grand Challenge, Part 2
Planning Algorithms
Autonomous driving in urban environments: Boss and the Urban Challenge
Journal of Field Robotics - Special Issue on the 2007 DARPA Urban Challenge, Part I
Online planning algorithms for POMDPs
Journal of Artificial Intelligence Research
Evaluation of navigation of an autonomous mobile robot
PerMIS '07 Proceedings of the 2007 Workshop on Performance Metrics for Intelligent Systems
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
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With the increasing complexity of robotic systems, system robustness and efficiency are harder to achieve, since they are determined by the interplay of all of a systemâ聙聶s components. In order to improve the robustness of such systems, it is essential to identify the system components that are crucial for each task and the extent to which they are affected by other components and the environment. Such knowledge will help developers to improve their systems, and can also be directly utilized by the systems themselves, for example, to detect failures and thereby correctly adjust the systemâ聙聶s behavior. In this article a method of system interdependence analysis is presented. The basic idea is to learn and quantitatively evaluate the coherence between performance indicators of different system components, as well as the influence of environmental parameters on the system. To validate the proposed approach, system interdependence analysis is applied to the navigation system of an autonomous mobile robot. Its navigational methods are presented and suitable indicators are derived. The results of using the method, based on experimental data from an extended field experiment, are given.