Use of Dempster-Shafer Conflict Metric to Adapt Sensor Allocation to Unknown Environments

This paper considers a robot with multiple sensors navigating an unknown, heterogeneous environment. In these cases sensor errors may produce an unsuitable model of the world. For example, observations from a laser range finder often produce accurate maps in indoor environments but when glass walls are encountered, the laser sees through it and the accuracy degrades. The approach taken is based on prior work with the DempsterShafer Con metric showing that the Gambino indicator, without the use of a ground truth, can identify when a model of the world is inconsistent. This study investigates the impact, in terms of overall map quality, of applying this new capability. Experiments with a mobile robot carrying a ring of sonar and a laser range sensor operating in indoor environments shows a 19.6% to 77.4% improvement when a switch to a more suitable sensor was triggered by the Gambino indicator. While these results are preliminary, this is a major step toward self-aware autonomous agents that can identify anomalous situations and adapt to the unknown appropriately. Introduction and Approach An autonomous robot entering an unknown environment has only its sensors to provide the information it needs to navigate safely and carry out its assigned mission. This is not a problem if sensing provides consistent and accurate information, but how does such a robot detect when its sensors become unsuited for its surroundings? The findings in (Carlson & Murphy 2005) indicated that the Gambino indicator may provide a solution to this problem by identifying when an occupancy grid became inconsistent without the use of a ground truth. This short paper summarizes recent work exploring the application of the Gambino indicator to adaptively improve overall map quality in unknown environments. To improve an autonomous robot’s ability to adapt to unknown environments that are unfavorable to one of its sensors, an indicator must: alert the system when a sensor is in use that is unsuited for the robot’s environment, not raise false alarms for minor inconsistencies in readings from appropriately used sensors, and provide improvements in sensing as compared to the naive strategy of always using the Copyright c © 2006, American Association for Artificial Intelligence (www.aaai.org). All rights reserved. most sophisticated sensor available. The objectives of this study are to show that the Gambino indicator meets these criteria. This study applies the Gambino indicator to detect and respond to interpretation inconsistency. One motivation for this problem comes from sensing anomalies which refers to cases in which the physical sensor(s) are working within the manufacturer’s specifications but the readings would lead to an incorrect interpretation of the environment. Our approach provides a means of detecting sensing anomalies in unknown environments by assuming that such environments are consistent. For the purposes of this study consistency is defined as free of conflict where conflict, φ, is defined as the intersection of mutually exclusive hypotheses. Given this assumption, an appropriate measure of inconsistency in a model of that environment can be used to detect sensing problems. If conflict appears in a model built from sensor readings it was caused by one or more of the following: a flawed model design, inaccurate sensor readings, poor localization of the robot within the environment, or over-simplified sensor models. This formulation of consistency allows conflict to be used to detect a variety of sensing and modeling problems. For example in (Gambino, Ulivi, & Vendittelli 1997) an occupancy grid was used to model a dynamic simulated environment where the sensor readings were perfectly accurate, thus a conflict-based metric could be developed to detect changes in the environment. This study uses occupancy grids in a real, static environment where sensing anomalies are assumed to generate significantly more conflict than the other three factors. The Dempster-Shafer conflict metric, Con (Shafer 1976), or the conflict belief mass in Smets’ variant (Smets 1990) of Dempster-Shafer, provide theoretically suitable measures of inconsistency in a set of evidence. Previous work (Carlson & Murphy 2005) shows that the Gambino indicator (adapted from (Gambino, Ulivi, & Vendittelli 1997)), which uses Smets’ variant, can be used to classify the sensing situation as degraded (false negative rate of 7%) or normal (false positive rate of 0%) and estimate the overall map quality for both sonar and laser readings. The results from two response scenarios are included in this paper. Each scenario began by using either the laser or sonar sensors to build a map of the surrounding environment using Dempster-Shafer mapping while the Gambino indica-