Technical session talks from ICRA 2012
TechTalks from event: Technical session talks from ICRA 2012
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A Dependable Perception-Decision-Execution Cycle for Autonomous RobotsThe tasks robots are employed to achieve are becoming increasingly complex, demanding for dependable operation, especially if robots and humans share common space. Unfortunately, for these robots non-determinism is a severe challenge. Malfunctioning hardware, inaccurate sensors, exogenous events and incomplete knowledge lead to inconsistencies in the robotâ€™s belief about the world. Thus, a robot has to cope efficiently with such adversities while sensing its surroundings, deciding what to do next, and executing its decisions. In this paper, we present such a dependable perception-decision-execution cycle. It employs a belief management system that performs history-based diagnosis in the high-level control module. The belief management enables robots to detect these inconsistencies and thus operate successfully in non-deterministic environments. The main contributions of this paper are a robot design extending the high-level control IndiGolog by a belief management allowing to deal with a large variety of faults in a unique way, together with an evaluation on a real robot system.
Efficient Change Detection in 3D Environment for Autonomous Surveillance Robots based on Implicit VolumeThe ability to detect changes in the environment is an essential trait for robots commissioned to work in several applications. In surveillance, for instance, a robot needs to detect meaningful changes in the environment which is achieved by comparing current sensory data with previously acquired information from the environment. The large amount of sensory data, which are often complex and very noisy, explains the inherent difficulty of this task. As an attempt to tackle this hard problem, we present an efficient method to automatically segment 3D data, corrupted with noise and outliers, into an implicit volume bounded by a surface. The method makes it possible to efficiently apply Boolean operations to 3D data in order to detect changes and to update existing maps. We show that our approach is powerful, albeit simple, with linear time complexity. The method has been validated through several trials using mobile robots operating in real environments and their performance was compared to another state-of-art algorithm. Experimental results demonstrate the performance of the proposed method, both in accuracy and computational cost.
Stochastic Source Seeking in Complex EnvironmentsThe objective of source seeking problems is to determine the minimum of an unknown signal field, which represents a physical quantity of interest, such as heat, chemical concentration, or sound. This paper proposes a strategy for source seeking in a noisy signal field using a mobile robot and based on a stochastic gradient descent algorithm. Our scheme does not require a prior map of the environment or a model of the signal field and is simple enough to be implemented on platforms with limited computational power. We discuss the asymptotic convergence guarantees of algorithm and give specific guidelines for its application to mobile robots in unknown indoor environments with obstacles. Both simulations and real-world experiments were carried out to evaluate the performance of our approach. The results suggest that the algorithm has good finite time performance in complex environments.
Robust Sound Localization for Various Platform of Robots Using TDOA Map AdaptationIn realistic environments, mismatches between the calculated angle-TDOA map with its real exact values are the major reason of performance degradation in sound localization. Usually, those mismatches come from some certain configuration errors or deviations caused by the change of environments. To reduce those mismatches, in this paper we proposed an angle-TDOA map adaptation method, which can achieve the robust sound localization in various robot platforms (i.e., various types of microphone array configuration). Especially, the proposed method is possible to easily apply to the sound localization system by using only several sound sources which generated from some known directions. As a result, the proposed method not only showed a good localization performance, and the program running time is also very short.