An Adaptive Coverage Control for Deployment of Nonholonomic Mobile Sensor Networks Over Time‐Varying Sensory Functions

Luna, José Marcio; Fierro, Rafael; Abdallah, Chaouki T.; Wood, John

doi:10.1002/asjc.636

Cited by 16 publications

(12 citation statements)

References 29 publications

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“…with k R i ∈ R + the range controller gain. We analyse the effect of the range control by replacing (15) into (14):…”

Section: Coverage Range Controlmentioning

confidence: 99%

“…First, we introduce an experiment in detail showing the results using our approach with = 200, k i = 3, and range limits R ∈ [5,15]. In Fig.…”

Section: Simulation With Variable Power and Rangementioning

confidence: 99%

“…There are areas of the map with different levels of desired coverage Λ * = {37.5, 75} to be performed by N = 4 agents. The parameters of the problem are defined as k i = 10, = 1∕4, R = [3,15], K = [0, 5000], Φ = 1, = 800, and g = 1. In the second experiment, presented in Fig.…”

Section: Different Desired Coverage Levels and Prioritiesmentioning

confidence: 99%

See 2 more Smart Citations

Adaptive Action for Multi‐Agent Persistent Coverage

Franco¹,

López‐Nicolás²,

Sagüés³

et al. 2015

Asian Journal of Control

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Persistent coverage with a team of agents refers to the task of covering an area where the coverage degrades with time. This is a dynamic task as opposed to the deployment problem. A key issue here is the coverage degradation that prevents the complete coverage fulfilment and requires persistence in the action. We propose a new method for persistent coverage where the agents' actions are computed in a partially distributed manner. The contribution is a control strategy based on variable coverage action and variable coverage range of the agents. This control provides adaptive behaviour in terms of actuator power and actuator domain in order to reduce the coverage error and energy consumption. The proposal is tested in simulation, showing clear improvement in terms of efficiency, flexibility, and scalability.

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“…with k R i ∈ R + the range controller gain. We analyse the effect of the range control by replacing (15) into (14):…”

Section: Coverage Range Controlmentioning

confidence: 99%

“…First, we introduce an experiment in detail showing the results using our approach with = 200, k i = 3, and range limits R ∈ [5,15]. In Fig.…”

Section: Simulation With Variable Power and Rangementioning

confidence: 99%

See 1 more Smart Citation

Adaptive Action for Multi‐Agent Persistent Coverage

Franco¹,

López‐Nicolás²,

Sagüés³

et al. 2015

Asian Journal of Control

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show abstract

“…In the works of Schwager et al,() the authors extended the algorithm of Cortés et al using adaptive control for the case where the density function is not fully known. Luna et al considered robots with nonholonomic kinematic models and developed algorithms for coverage. They also considered time‐varying density functions, which can be parameterized using time‐varying parameters and showed uniform ultimate boundedness of trajectories when the time variation of the parameters are small.…”

Section: Introductionmentioning

confidence: 99%

Decentralized and adaptive control of multiple nonholonomic robots for sensing coverage

Razak

Sukumar

Chung

2018

Intl J Robust & Nonlinear

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Summary This work deals with decentralized control of multiple nonholonomic mobile sensors for optimal coverage of a given area for sensing purposes. We assume a density function over the region to be covered, which can be viewed as a probability density of the phenomena to be sensed. The density function is unknown but assumed to be linearly parameterized with unknown parameter weights. We consider a second‐order dynamic model for the mobile agents and derive decentralized adaptive control laws to achieve optimal coverage of the region. We then consider the case where the dynamic model of the agents are not fully known, and then develop parameter adaptation laws to achieve the optimal coverage objective. We test the derived algorithms using simulations and compare our proposed controllers with kinematics‐based controllers. We find that the feedback control design based on the dynamic model performs significantly better than controllers solely relying on kinematic models. Furthermore, for the unknown dynamics case, our controller outperforms the nonadaptive controller with poor initial parameter estimates.

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“…Adaptive control is popular in engineering and science [1][2][3][4][5][6][7][8][9][10]. However, it still faces many important challenges, such as the handling of non-smooth nonlinearity.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Tracking Control for Nonlinear Systems with a Class of Input Nonlinearities

Liu¹,

Dong²,

Xue³

et al. 2015

Asian Journal of Control

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An adaptive tracking control approach is presented for nonlinear systems with a class of input nonlinearities. A generalized model has been developed for a class of non‐smooth nonlinearities that include dead‐zone, backlash and ‘backlash‐like’ hysteresis. By using the developed model and Nussbaum‐gain technique, the problem of input nonlinearity is solved perfectly. The proposed method is available even when the designer is uncertain about the type of input nonlinearities mentioned above, and the knowledge on the bounds of these nonlinearity parameters is not required. Furthermore, it is proved that all closed‐loop signals are bounded and the tracking error converges to a small residual set asymptotically. Two simulation examples are provided to demonstrate the effectiveness of the proposed method.

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An Adaptive Coverage Control for Deployment of Nonholonomic Mobile Sensor Networks Over Time‐Varying Sensory Functions

Cited by 16 publications

References 29 publications

Adaptive Action for Multi‐Agent Persistent Coverage

Adaptive Action for Multi‐Agent Persistent Coverage

Decentralized and adaptive control of multiple nonholonomic robots for sensing coverage

Adaptive Tracking Control for Nonlinear Systems with a Class of Input Nonlinearities

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