Predictive Control for Multi-Robot Observation of Multiple Moving Targets Based on Discrete-Continuous Linear Models

Kühn, Juliane; Reinl, Christian; Stryk, Oskar von

doi:10.3182/20110828-6-it-1002.00274

Cited by 17 publications

(14 citation statements)

References 14 publications

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“…The approach presented in this paper can be seen as a starting point towards such a distributed application: While the authors already presented a possible distributed variant of the cooperative controller (Kuhn et al, 2011), the decentralization of simulation, estimation and observation point generation is subject of active research.…”

Section: Resultsmentioning

confidence: 99%

Adaptive Observation Strategy for Dispersion Process Estimation Using Cooperating Mobile Sensors

Ritter

Euler

Ulbrich

et al. 2014

IFAC Proceedings Volumes

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Efficient online state estimation of dynamic dispersion processes plays an important role in a variety of safety-critical applications. The use of mobile sensor platforms is increasingly considered in this context, but implies the generation of situation-dependent vehicle trajectories providing high information gain in real-time. In this paper, a new adaptive observation strategy is presented combining state estimation based on partial differential equation models of the dispersion process with a model-predictive control approach for multiple cooperating mobile sensors. In a repeating sequential procedure, based on the Ensemble Transform Kalman Filter, the uncertainty of the current estimate is determined and used to find valuable measurement locations. Those serve as target points for the controller providing optimal trajectories subject to the vehicles' motion dynamics and cooperation constraints. First promising results regarding accuracy and efficiency were obtained.

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Section: Resultsmentioning

confidence: 99%

Adaptive Observation Strategy for Dispersion Process Estimation Using Cooperating Mobile Sensors

Ritter

Euler

Ulbrich

et al. 2014

IFAC Proceedings Volumes

Self Cite

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“…Robot formation [65] Model-predictive control C [66] Clustering Dc [67] Integer linear program C [68], [ [78] Negotiation and auctions D…”

Section: Problem Formulationmentioning

confidence: 99%

Cooperative Robots to Observe Moving Targets: Review

Khan

Rinner

Cavallaro

2018

IEEE Trans. Cybern.

166

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The deployment of multiple robots for achieving a common goal helps to improve the performance, efficiency, and/or robustness in a variety of tasks. In particular, the observation of moving targets is an important multirobot application that still exhibits numerous open challenges, including the effective coordination of the robots. This paper reviews control techniques for cooperative mobile robots monitoring multiple targets. The simultaneous movement of robots and targets makes this problem particularly interesting, and our review systematically addresses this cooperative multirobot problem for the first time. We classify and critically discuss the control techniques: cooperative multirobot observation of multiple moving targets, cooperative search, acquisition, and track, cooperative tracking, and multirobot pursuit evasion. We also identify the five major elements that characterize this problem, namely, the coordination method, the environment, the target, the robot and its sensor(s). These elements are used to systematically analyze the control techniques. The majority of the studied work is based on simulation and laboratory studies, which may not accurately reflect real-world operational conditions. Importantly, while our systematic analysis is focused on multitarget observation, our proposed classification is useful also for related multirobot applications.

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“…More recent work on the CMOMMT problem has shown that simultaneous optimization of discrete target assignments and robot motion improves the team performance (Kuhn et al., ). The optimization of assignments and robot motions was found using an exact MILP solver, which scales exponentially in the number of robots and targets.…”

Section: Related Workmentioning

confidence: 99%

Decentralized Coordinated Tracking with Mixed Discrete–Continuous Decisions

Underwood

Sukkarieh

2013

Journal of Field Robotics

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In this paper, we study the problem of dynamically positioning a team of mobile robots for target tracking. We treat the coordination of mobile robots for target tracking as a joint team optimization to minimize uncertainty in target state estimates over a fixed horizon. The optimization is inherently a function of both the positioning of robots in continuous space and the assignment of robots to targets in discrete space. Thus, the robot team must make decisions over discrete and continuous variables. In contrast to methods that decouple target assignments and robot positioning, our approach avoids the strong assumption that a robot's utility for observing a target is independent of other robots’ observations. We formulate the optimization as a mixed integer nonlinear program and apply integer relaxation to develop an approximate solution in decentralized form. We demonstrate our coordinated multirobot tracking algorithm both in simulation and using a pair of mobile robotic sensor platforms to track moving pedestrians. Our results show that coupling target assignment and robot positioning realizes coordinated behaviors that are not possible with decoupled methods.

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Predictive Control for Multi-Robot Observation of Multiple Moving Targets Based on Discrete-Continuous Linear Models

Cited by 17 publications

References 14 publications

Adaptive Observation Strategy for Dispersion Process Estimation Using Cooperating Mobile Sensors

Adaptive Observation Strategy for Dispersion Process Estimation Using Cooperating Mobile Sensors

Cooperative Robots to Observe Moving Targets: Review

Decentralized Coordinated Tracking with Mixed Discrete–Continuous Decisions

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