Fitness Function Based Sensor Degradation Estimation Using H∞ Filter

Arosh, Sirshendu; Suryaprakash,; Nayak, S. K.; Duttagupta, Siddhartha P.

doi:10.1016/j.procs.2015.08.048

Cited by 4 publications

(1 citation statement)

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“…Finally, we assume that robots can estimate their sensor quality. Since there exist techniques for sensor fault detection [10] and degradation estimation [11], [12], this assumption does not impose undue restrictions on the applicability of our method.…”

Section: Introduction and Related Workmentioning

confidence: 99%

Resilience in multi-robot target tracking through reconfiguration

Ramachandran¹,

Fronda²,

Sukhatme³

2019

Preprint

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We address the problem of maintaining resource availability in a networked multi-robot system performing distributed target tracking. In our model, robots are equipped with sensing and computational resources enabling them to track a target's position using a Distributed Kalman Filter (DKF). We use the trace of each robot's sensor measurement noise covariance matrix as a measure of sensing quality. When a robot's sensing quality deteriorates, the systems communication graph is modified by adding edges such that the robot with deteriorating sensor quality may share information with other robots to improve the team's target tracking ability. This computation is performed centrally and is designed to work without a large change in the number of active communication links. We propose two mixed integer semi-definite programming formulations (an 'agent-centric' strategy and a 'team-centric' strategy) to achieve this goal. We implement both formulations and a greedy strategy in simulation and show that the teamcentric strategy outperforms the agent-centric and greedy strategies.

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Section: Introduction and Related Workmentioning

confidence: 99%