Multirobot autonomous landmine detection using distributed multisensor information aggregation

Jumadinova, Janyl; Dasgupta, Prithviraj

doi:10.1117/12.919269

Cited by 5 publications

(4 citation statements)

References 13 publications

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“…This paper addresses the problem of integrating multiple sensors using information markets a topic that has received less attention as it requires a higher degree of abstraction to make parallels to the real world economy. It is important to note that this paper is not the first to put forth a market-like framework for sensor data aggregation; Jumadinova and Dasgupta [18,19,20] have done some preliminary work looking at how a prediction market can be used to fuse a metal detector, a ground penetrating radar and an infra-red camera for a land mind detection system. Their work, while interesting and enlightening, has only scratched the surface on avenues of exploration, and has not taken advantage of the wealth of research that has emerged in the last 5-10 years within the EconCS community for developing prediction market mechanisms.…”

Section: Prior Workmentioning

confidence: 98%

Financialized methods for market-based multi-sensor fusion

Abernethy

Johnson-Roberson

2015

2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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Autonomous systems rely on an increasing number of input sensors of various modalities, and the problem of sensor fusion has received attention for many years. Autonomous system architectures are becoming more complex with time, and the number and placement of sensors will be modified regularly, sensors will fail for many reasons, information will arrive asynchronously, and the system will need to adjust to rapidly changing environments. To address these issues we propose a new paradigm for fusing information from multiple sources that draws from the rich of field pertaining to financial markets, particularly recent research on prediction market design. Among the many benefits of this financialized approach is that, both in theory and in practice, markets are wellequipped to robustly synthesize information from diverse sources in a decentralized fashion. Our framework poses sensor processing algorithms as profit-seeking market participants, data is incorporated via financial transactions, and the joint estimation is represented as a price equilibrium. We use pedestrian detection as a motivating application. Pedestrian detection is a well studied field and essential to autonomous driving. Real world fusion results are presented on RGB and LIDAR data from the KITTI Vision Benchmark Suite. We demonstrate we can achieve comparable performance to state-of-the-art hand designed fusion techniques using the proposed approach.

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Section: Prior Workmentioning

confidence: 98%

Financialized methods for market-based multi-sensor fusion

Abernethy

Johnson-Roberson

2015

2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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“…Details of the operation of the information aggregation technique along with analytical and experimental results of its performance are given in [53]; we provide an overview of its main features and a few significant results in the next section.…”

Section: Information Aggregation Techniques In Comradesmentioning

confidence: 99%

“…It then assigns a score or virtual reward to each sensor (robot) if it correctly reports the probability distribution over the different types of objects of interests such as landmine, metal but not landmine, and not landmine. The details of this technique are given in [53]. The aggregation mechanism outputs a single aggregated belief value, .…”

Section: Information Aggregation Techniques In Comradesmentioning

confidence: 99%

“…The aggregation mechanism also selectively includes weights of the sensor reports from a human expert about the accuracy of the sensors' reports based on the ambient conditions of the sensors. The aggregated belief value is then passed on to a decision maker agent that makes decisions about which other robots (sensor types) should be deployed to the potential landmine's location using a Bayesian inferencing based technique, so that the landmine can be confirmed rapidly and accurately [53].…”

Section: Information Aggregation Techniques In Comradesmentioning

confidence: 99%

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The COMRADE System for Multirobot Autonomous Landmine Detection in Postconflict Regions

Dasgupta

Baca

Guruprasad

et al. 2015

Journal of Robotics

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We consider the problem of autonomous landmine detection using a team of mobile robots. Previous research on robotic landmine detection mostly employs a single robot equipped with a landmine detection sensor to detect landmines. We envisage that the quality of landmine detection can be significantly improved if multiple robots are coordinated to detect landmines in a cooperative manner by incrementally fusing the landmine-related sensor information they collect and then use that information to visit locations of potential landmines. Towards this objective, we describe a multirobot system called COMRADES to address different aspects of the autonomous landmine detection problem including distributed area coverage to detect and locate landmines, information aggregation to fuse the sensor information obtained by different robots, and multirobot task allocation (MRTA) to enable different robots to determine a suitable sequence to visit locations of potential landmines while reducing the time required and battery expended. We have used commercially available all-terrain robots called Coroware Explorer that are customized with a metal detector to detect metallic objects including landmines, as well as indoor Corobot robots, both in simulation and in physical experiments, to test the different techniques in COMRADES.

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