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Anup Parikh

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3 papers

1 author row

IROS Conference 2021 Conference Paper

Decentralized Classification with Assume-Guarantee Planning

Steven Carr 0002
Jesse Quattrociocchi
Suda Bharadwaj
Steven J. Spencer
Anup Parikh
Carol C. Young
Stephen P. Buerger
Bo Wu 0005

We study the problem of decentralized classification conducted over a network of mobile sensors. We model the multiagent classification task as a hypothesis testing problem where each sensor has to almost surely find the true hypothesis from a finite set of candidate hypotheses. Each sensor makes noisy local observations and can also share information on their observations with other mobile sensors in communication range. In order to address the state-space explosion in the multiagent system, we propose a decentralized synthesis procedure that guarantees that each sensor will almost surely converge to the true hypothesis even in the presence of faulty or malicious agents. Additionally, we employ a contract-based synthesis approach that produces trajectories designed to empirically increase information-sharing between mobile sensors in order to converge faster to the true hypothesis. We implement and test the approach on experiments with both physical and simulated hardware to showcase the approach’s scalability and viability in real-world systems. Finally, we run a Gazebo/ROS simulated experiment with 12 agents to demonstrate the scalability of our approach in large environments with many agents.

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IROS Conference 2020 Conference Paper

Autonomous Detection and Assessment with Moving Sensors

Steven J. Spencer
Anup Parikh
Daniel R. McArthur
Carol C. Young
Timothy J. Blada
Jonathon E. Slightam
Stephen P. Buerger

Current approaches to physical security suffer from high false alarm rates and frequent human operator involvement, despite the relative rarity of real-world threats. We present a novel architecture for autonomous adaptive physical security called autonomous detection and assessment with moving sensors (ADAMS). ADAMS is a framework for reducing nuisance and false alarms by placing mobile robotic platforms equipped with sensors outside the normal asset perimeter. These robotic agents integrate sensor data from multiple perspectives over time, and autonomously move to obtain the best new data to reduce uncertainty in the threat scene. Inferences drawn from data fused over time provide ultimate decisions regarding whether to alert human operators. This paper describes the framework and algorithms used in a prototype ADAMS implementation. We describe the results of simulations comparing this framework to alternate paradigms. These simulations show ADAMS has a 4x increase in the range at which threats are identified versus traditional static sensors, and a 5x reduction in false alarms triggered versus frameworks where all sensor detections become alarms, leading to reduced operator load. Further, these simulations show this framework for reacting to new potential threats significantly outperforms methods which merely patrol the site. We also present the results of preliminary hardware trials of an exemplar prototype system, providing limited validation of the simulations in a real-time physical demonstration.

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IROS Conference 2020 Conference Paper

Rapid Autonomous Semantic Mapping

Anup Parikh
Mark W. Koch
Timothy J. Blada
Stephen P. Buerger

A semantic understanding of the environment is needed to enable high level autonomy in robotic systems. Recent results have demonstrated rapid progress in underlying technology areas, but few results have been reported on end-to-end systems that enable effective autonomous perception in complex environments. In this paper, we describe an approach for rapidly and autonomously mapping unknown environments with integrated semantic and geometric information. We use surfel-based RGB-D SLAM techniques, with incremental object segmentation and classification methods to update the map in realtime. Information theoretic and heuristic measures are used to quickly plan sensor motion and drive down map uncertainty. Preliminary experimental results in simple and cluttered environments are reported.

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