Experimental design for assessing the effectiveness of autonomous countermine systems

Isaac Chappell; Michael May; Franklin L. Moses

doi:10.1117/12.851214

29 April 2010 Experimental design for assessing the effectiveness of autonomous countermine systems

Isaac Chappell, Michael May, Franklin L. Moses

Proceedings Volume 7664, Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XV; 76641C (2010) https://doi.org/10.1117/12.851214
Event: SPIE Defense, Security, and Sensing, 2010, Orlando, Florida, United States

Abstract

The countermine mission (CM) is a compelling example of what autonomous systems must address to reduce risks that Soldiers take routinely. The list of requirements is formidable and includes autonomous navigation, autonomous sensor scanning, platform mobility and stability, mobile manipulation, automatic target recognition (ATR), and systematic integration and control of components. This paper compares and contrasts how the CM is done today against the challenges of achieving comparable performance using autonomous systems. The Soldier sets a high standard with, for example, over 90% probability of detection (Pd) of metallic and low-metal mines and a false alarm rate (FAR) as low as 0.05/m². In this paper, we suggest a simplification of the semi-autonomous CM by breaking it into three components: sensor head maneuver, robot navigation, and kill-chain prosecution. We also discuss the measurements required to map the system's physical and state attributes to performance specifications and note that current Army countermine metrics are insufficient to the guide the design of a semi-autonomous countermine system.

Citation Download Citation

Isaac Chappell, Michael May, and Franklin L. Moses "Experimental design for assessing the effectiveness of autonomous countermine systems", Proc. SPIE 7664, Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XV, 76641C (29 April 2010); https://doi.org/10.1117/12.851214

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KEYWORDS

Sensors

Curium

Head

Land mines

Detection and tracking algorithms

Palladium

Automatic target recognition

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