Dynamic artificial potential field based multi-robot formation control

Zhang, Min; Shen, Yi; Wang, Qiang; Wang, Yibo

doi:10.1109/imtc.2010.5488238

Cited by 47 publications

(29 citation statements)

References 5 publications

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“…Therefore, the deployment of nodes in highly dynamic scenarios requires a deployment approach that equips the network with a self-organizing capa-bility. Artificial potential field (APF) techniques 9 have been applied to the problems of formation control and obstacle avoidance in multi-robot systems [277]. Since these problems are of similar nature to the deployment problem of sensor nodes, the APF techniques may also be used to devise a deployment approach for WSNs.…”

Section: Open Problems and Discussionmentioning

confidence: 99%

A Survey of Multi-Objective Optimization in Wireless Sensor Networks: Metrics, Algorithms, and Open Problems

Fei

Yang

et al. 2017

IEEE Commun. Surv. Tutorials

371

202

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Wireless sensor networks (WSNs) have attracted substantial research interest, especially in the context of performing monitoring and surveillance tasks. However, it is challenging to strike compelling trade-offs amongst the various conflicting optimization criteria, such as the network's energy dissipation, packet-loss rate, coverage and lifetime. This paper provides a tutorial and survey of recent research and development efforts addressing this issue by using the technique of multi-objective optimization (MOO). First, we provide an overview of the main optimization objectives used in WSNs. Then, we elaborate on various prevalent approaches conceived for MOO, such as the family of mathematical programming based scalarization methods, the family of heuristics/metaheuristics based optimization algorithms, and a variety of other advanced optimization techniques. Furthermore, we summarize a range of recent studies of MOO in the context of WSNs, which are intended to provide useful guidelines for researchers to understand the referenced literature. Finally, we discuss a range of open problems to be tackled by future research.

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Section: Open Problems and Discussionmentioning

confidence: 99%

A Survey of Multi-Objective Optimization in Wireless Sensor Networks: Metrics, Algorithms, and Open Problems

Fei

Yang

et al. 2017

IEEE Commun. Surv. Tutorials

371

202

View full text Add to dashboard Cite

show abstract

“…Numerous studies have been carried out on APF methods. First, because they are easy to implement and have low computational cost [24]. Second, because Khatib's method presents some weaknesses, and can then be improved, as local minima (which induce the robot to be trapped in), Goal Non Reachable with Obstacle Nearby (GNRON) problem or impossibility to reach the goal when it is aligned with an obstacle.…”

Section: B Apf-based Methodsmentioning

confidence: 99%

A 3D Mobility Model for Autonomous Swarms of Collaborative UAVs

Falomir

Chaumette

Guerrini

2019

2019 International Conference on Unmanned Aircraft Systems (ICUAS)

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Collaboration between several Unmanned Aerial Vehicles (UAVs) can produce high-quality results in numerous missions, including surveillance, search and rescue, tracking or identification. Such a combination of collaborative UAVs is referred to as a swarm. These several platforms enhance the global system capabilities by supporting some form of resilience and by increasing the number and/or the variety of the embedded sensors. Furthermore, several UAVs organized in a swarm can (should the ground control station support this) be considered as a single entity from an operator pointof-view. We aim at using such swarms in complex and unknown environments, and in the long term, allow compact flights. Dynamic path planning computation for each UAV is a major task to perform their mission. To define this path planning, we have implemented a three-dimensional (3D) mobility model for swarms of UAVs using both the Artificial Potential Fields (APF) principle and a global path planning method. In our model, the collaboration between the platforms is made by sharing information about the detected obstacles. To provide a significant validation of our mobility model, we have simulated real-world environments and real-world sensors characteristics, using the OMNeT++ network simulator.

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“…In this work, a modified form of the APF described by Khatib (1986) has been implemented. Here, a WMR has been mathematically modelled as a moving particle inside an APF that is generated by superposing an attractive potential which pulls the robot to a goal configuration and a repulsive potential that pushes the robot away from obstacles (Khatib, 1986;Ge and Cui, 2002;Zang et al, 2010). As explained in Section 2, then the potential function has been modified for the generation of the reference path velocity of the leader robot employing the incremental motion planning approach exploiting the concept of vortex field (Luca and Oriolo, 1994).…”

Section: Introductionmentioning

confidence: 99%

Autonomous leader-follower formation control of non-holonomic wheeled mobile robots by incremental path planning and sliding mode augmented tracking control

Kowdiki

Barai

Bhattacharya

2019

IJSCC

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This paper presents a novel robust and autonomous formation control scheme for wheeled mobile robots in the leader-follower formation control framework considering their non-holonomic constraints. In the proposed formation control scheme, the leader robot of the group plans its path of navigation autonomously in a cluttered environment by employing incremental path planning by modified artificial potential field. Then, the follower robots in the group plan their path in order to follow the leader robot by maintaining a particular formation using the separation-bearing (l-ψ) control. Then the formation control problem has been transformed into a trajectory tracking control problem. The kinematic control component of the tracking controller provides the necessary velocity input for eliminating the non-holonomic constraints, whereas, the sliding mode augmented robust trajectory tracking control component minimises the effects of nonlinearities, model uncertainties, parameter variations, and disturbances. The effectiveness of the proposed control law has been established by simulation studies.

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Dynamic artificial potential field based multi-robot formation control

Cited by 47 publications

References 5 publications

A Survey of Multi-Objective Optimization in Wireless Sensor Networks: Metrics, Algorithms, and Open Problems

A Survey of Multi-Objective Optimization in Wireless Sensor Networks: Metrics, Algorithms, and Open Problems

A 3D Mobility Model for Autonomous Swarms of Collaborative UAVs

Autonomous leader-follower formation control of non-holonomic wheeled mobile robots by incremental path planning and sliding mode augmented tracking control

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