Bearing-only formation control with auxiliary distance measurements, leaders, and collision avoidance

Tron, Roberto; Thomas, Justin; Loianno, Giuseppe; Daniilidis, Kostas

doi:10.1109/cdc.2016.7798527

Cited by 29 publications

(51 citation statements)

References 39 publications

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“…This paper presented a new stability analysis of the bearing-only formation control law proposed in [1]. The new stability analysis is based on standard Lyapunov approaches and reveals some new properties of the control law.…”

Section: Discussionmentioning

confidence: 99%

“…Despite the recent advances on bearing-only formation control, many problems in this area are still unsolved. In particular, the existing bearing-only control laws are merely applicable to single-integrator agent models and stationary target formations [1], [5]- [11]. From the practical point of view, it is necessary to study more realistic models and how to track moving target formations.…”

Section: Introductionmentioning

confidence: 99%

“…In this paper, we revisit a bearing-only formation control law proposed in [1]. Unlike many other existing bearingonly formation control laws, this one is a gradient-descent control law, which is favorable from the stability analysis point of view.…”

Section: Introductionmentioning

confidence: 99%

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A Revisit to Gradient-Descent Bearing-Only Formation Control

Zhao

Ding

2018

2018 IEEE 14th International Conference on Control and Automation (ICCA)

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This paper addresses the problem of bearing-only formation control of multi-agent systems, where each agent can merely obtain the relative bearing measurements of their neighbor neighbors whereas relative distance or position measurements are unavailable. In particular, we revisit a bearingonly formation control law proposed in [1]. Unlike many other existing ones, this control law is gradient-descent, which is favorable from the stability analysis point of view. It has the potential to be extended to handle more complex agent models and moving target formations. Up to now, this control law has not attracted sufficient attention probably because its stability analysis is based on optimization techniques and challenging to generalize. The contribution of this paper is to present a new stability analysis of this formation control law based on Lyapunov approaches. The new stability analysis reveals some new properties of the control law such as exponential convergence rate and lays a foundation for deriving new bearing-only control laws in the future.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Revisit to Gradient-Descent Bearing-Only Formation Control

Zhao

Ding

2018

2018 IEEE 14th International Conference on Control and Automation (ICCA)

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“…An optimization-based approach for bearing-only formation control can be found in [10], [65], where a bearing-only control law is proposed aṡ…”

Section: Bearing-only Formation Controlmentioning

confidence: 99%

“…Simulation shows that this control law may steer all the agents to the same position given certain initial conditions. To avoid this problem, leaders must be introduced [65].…”

Section: Bearing-only Formation Controlmentioning

confidence: 99%

Bearing Rigidity Theory and Its Applications for Control and Estimation of Network Systems: Life Beyond Distance Rigidity

2019

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Distributed control and estimation of multi-agent systems has received tremendous research attention in recent years due to their potential across many application domains [1], [2]. Here, the term "agent" can represent a sensor, an autonomous vehicle, or any general dynamical system. These multi-agent systems are becoming increasingly attractive because of their robustness against system failure, their ability to adapt to dynamic and uncertain environments, and their economic advantages compared to the implementation of more expensive monolithic systems.Formation control and network localization are two fundamental tasks for multi-agent systems that enable them to perform complex missions. The goal of formation control is to control each agent using local information from neighboring agents so that the entire team forms a desired spatial geometric pattern (see [2] for a recent survey on formation control). While the notion of a formation as a geometric pattern has a natural meaning for robotic systems, it may also correspond to more abstract configurations for the system state of a team of agents. The goal of network localization is to estimate the location of each agent in a network using locally sensed or communicated information from neighboring agents [3]-[6]. Network localization is usually the first step that must be completed before a sensor network provides other services like positioning mobile robots or monitoring areas of interest.For a formation control or network localization task, the type of information available to each agent is an important factor that determines the design of the corresponding control or estimation algorithms. Most of the existing approaches for formation control assume that each agent can obtain the relative positions of their nearest neighbors. In order to obtain relative positions in practice, each agent can measure their absolute positions using, for example, GPS, and then share their positions with their neighbors via wireless communications. This method is, however, not applicable when operating in GPS-denied environments such as indoors, underwater, or in deep space. Furthermore, the absolute accuracy of the GPS may not meet the requirements of high-accuracy formation control tasks. Rather than relying on external positioning systems such as GPS, each agent can use onboard sensors to sense their neighbors.Optical cameras are widely used onboard sensors for ground and aerial vehicles to achieve various sensing tasks due to their characteristics of being low-cost, light-weight, and low-power. It is notable that optical cameras are inherently bearing-only sensors. Specifically, once a target has been recognized in an image, its bearing relative to the camera can be calculated immediately from its pixel coordinate based on the pin-hole camera model [7, Section 3.3]. As a comparison, the range from the target to the camera is more complicated to obtain because it requires additional geometric information of the target and extra estimation algorithms, which may significantly in...

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Underwater guidance of distributed autonomous underwater vehicles using one leader

Kim

2022

Asian Journal of Control

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Consider the case where autonomous underwater vehicles (AUVs) are deployed to monitor a 3D underwater environment. This paper tackles the problem of guiding all AUVs to the destination while not colliding with a priori unknown 3D obstacles. Suppose that among all AUVs, only the leader AUV has an ability of locating itself, while accessing a destination location. A follower, an AUV that is not a leader, has no sensors for locating itself. Every follower can only measure the relative position of its neighbor AUVs utilizing its sonar sensors. Our paper addresses distributed controls, so that multiple followers track the leader while preserving communication connectivity. We design controls, so that all AUVs reach the destination safely, while maintaining connectivity in cluttered 3D environments. To the best of our knowledge, our article is novel in developing 3D underwater guidance controls, so that all AUVs equipped with sonar sensors are guided to reach a destination in a priori unknown cluttered environments. MATLAB simulations are used to validate the proposed guidance scheme in underwater environments with many obstacles.

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Bearing-only formation control with auxiliary distance measurements, leaders, and collision avoidance

Cited by 29 publications

References 39 publications

A Revisit to Gradient-Descent Bearing-Only Formation Control

A Revisit to Gradient-Descent Bearing-Only Formation Control

Bearing Rigidity Theory and Its Applications for Control and Estimation of Network Systems: Life Beyond Distance Rigidity

Underwater guidance of distributed autonomous underwater vehicles using one leader

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