Adaptive and Risk-Aware Target Tracking for Robot Teams With Heterogeneous Sensors

Mayya, Siddharth; Ramachandran, Ragesh K.; Zhou, Lifeng; Kumar, Vinay; Thakur, Dinesh; Sukhatme, Gaurav S.; Kumar, Vijay

doi:10.1109/lra.2022.3155805

Cited by 12 publications

(8 citation statements)

References 23 publications

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“…-We consider a perimeter X homeomorphic to S 1 (a circle 7 ), with distances determined by arc length and total length normalized to 1; we represent X = [−1/2, 1/2] (but −1/2 and 1/2 are the same point). To denote this situation, we define the distance function…”

Section: Unit Horizon Theoretical Resultsmentioning

confidence: 99%

“…Equipping different agents with different capabilities can lead to synergy effects where the heterogeneous system outperforms the alternative homogeneous system composed of identical agents. As a result, in the last decade, there has been significant interest in the robotics community to define, explore, and quantify heterogeneity in different robot applications [19,14,11,7,12,10].…”

Section: Introductionmentioning

confidence: 99%

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The Role of Heterogeneity in Autonomous Perimeter Defense Problems

Adler¹,

Mickelin²,

Ramachandran³

et al. 2022

Preprint

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When is heterogeneity in the composition of an autonomous robotic team beneficial and when is it detrimental? We investigate and answer this question in the context of a minimally viable model that examines the role of heterogeneous speeds in perimeter defense problems, where defenders share a total allocated speed budget. We consider two distinct problem settings and develop strategies based on dynamic programming and on local interaction rules. We present a theoretical analysis of both approaches and our results are extensively validated using simulations. Interestingly, our results demonstrate that the viability of heterogeneous teams depends on the amount of information available to the defenders. Moreover, our results suggest a universality property: across a wide range of problem parameters the optimal ratio of the speeds of the defenders remains nearly constant.

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Section: Unit Horizon Theoretical Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

The Role of Heterogeneity in Autonomous Perimeter Defense Problems

Adler¹,

Mickelin²,

Ramachandran³

et al. 2022

Preprint

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“…Multi-robot multi-target tracking is usually formulated as an iterative state estimation problem [7,8,9]. Various methods have been proposed for this; most of them design different flavors of filters to reduce the uncertainty in target pose estimation during the tracking process [1,7,10,11]. In the more challenging scenario where the targets may adversarially cause damages to the robots, safety becomes a major concern when the robot team plans its motion.…”

Section: Related Workmentioning

confidence: 99%

“…Considering that a multi-robot system controlled by a central planner does not scale well [1], efforts have been made to decentralize the multi-target tracking task. For example, [13] adopts the local algorithm for solving the max-min problem to solve multitarget tracking in a distributed manner.…”

Section: Related Workmentioning

confidence: 99%

“…We apply the control barrier function (CBF) in a decentralized manner to guarantee network connectivity maintenance and collision avoidance; 3. Our system produces comparable results to its centralized counterpart -we extensively evaluate the performance of our system through simulated experiments in Gazebo environment under various settings, and compare it to its centralized counterpart from our previous work [1]. We present qualitative demonstrations to provide an intuitive illustration.…”

Section: Introductionmentioning

confidence: 97%

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Decentralized Risk-Aware Tracking of Multiple Targets

Liu¹,

Zhou²,

Ramachandran³

et al. 2022

Preprint

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We consider the setting where a team of robots is tasked with tracking multiple targets with the following property: approaching the targets enables more accurate target position estimation, but also increases the risk of sensor failures. Therefore, it is essential to address the trade-off between tracking quality maximization and risk minimization. In the previous work [1], a centralized controller is developed to plan motions for all the robots -however, this is not a scalable approach. Here, we present a decentralized and risk-aware multi-target tracking framework, in which each robot plans its motion trading off tracking accuracy maximization and aversion to risk, while only relying on its own information and information exchanged with its neighbors. We use the control barrier function to guarantee network connectivity throughout the tracking process. Extensive numerical experiments demonstrate that our system can achieve similar tracking accuracy and risk-awareness to its centralized counterpart.

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