A sensor processing model incorporating error detection and recovery
Traditional and non-traditional robotic sensors
Digital image processing (2nd ed.)
Digital image processing (2nd ed.)
An architecture for intelligent robotic sensor fusion
An architecture for intelligent robotic sensor fusion
Applications of Artificial Intelligence for Chemical Inference: The Dendral Project
Applications of Artificial Intelligence for Chemical Inference: The Dendral Project
Generate, test and debug: combining associational rules and causal models
IJCAI'87 Proceedings of the 10th international joint conference on Artificial intelligence - Volume 2
Agent-based expert assistance for visual problem solving
AGENTS '97 Proceedings of the first international conference on Autonomous agents
Position estimation for mobile robots in dynamic environments
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
Enhancement of Probabilistic Grid-based Map for Mobile Robot Applications
Journal of Intelligent and Robotic Systems
Improving Robustness of Mobile Robots Using Model-based Reasoning
Journal of Intelligent and Robotic Systems
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This paper presents a characterization of sensing failures in autonomous mobile robots, a methodology for classification and recovery, and a demonstration of this approach on a mobile robot performing landmark navigation. A sensing failure is any event leading to defective perception, including sensor malfunctions, software errors, environmental changes, and errant expectations. The approach demonstrated in this paper exploits the ability of the robot to interact with its environment to acquire additional information for classification (i.e., active perception). A Generate and Test strategy is used to generate hypotheses to explain the symptom resulting from the sensing failure. The recovery scheme replaces the affected sensing processes with an alternative logical sensor. The approach is implemented as the Sensor Fusion Effects Exception Handling (SFX-EH) architecture. The advantages of SFX-EN are that it requires only a partial causal model of sensing failure, the control scheme strives for a fast response, tests are constructed so as to prevent confounding from collaborating sensors which have also failed, and the logical sensor organization allows SFX-EH to be interfaced with the behavioral level of existing robot architectures.