Gradient-based collision avoidance algorithm for second-order multi-agent formation control

Gong, Qinghai; Wang, Chunyan; Qi, Zhenqiang; Ding, Zhengtao

doi:10.23919/chicc.2017.8028652

Cited by 8 publications

(8 citation statements)

References 23 publications

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“…Collision avoidance by unmanned aerial vehicles can be implemented in many ways. Gong et al [ 8 ] used a gradient-based collision avoidance algorithm for the control of multi-agent formations. The algorithm uses a consensus theory and a graph theory applied to three topologies.…”

Section: Extended Examplementioning

confidence: 99%

Cross-Entropy as a Metric for the Robustness of Drone Swarms

Cofta

Ledziński

Śmigiel

et al. 2020

Entropy

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Due to their growing number and increasing autonomy, drones and drone swarms are equipped with sophisticated algorithms that help them achieve mission objectives. Such algorithms vary in their quality such that their comparison requires a metric that would allow for their correct assessment. The novelty of this paper lies in analysing, defining and applying the construct of cross-entropy, known from thermodynamics and information theory, to swarms. It can be used as a synthetic measure of the robustness of algorithms that can control swarms in the case of obstacles and unforeseen problems. Based on this, robustness may be an important aspect of the overall quality. This paper presents the necessary formalisation and applies it to a few examples, based on generalised unexpected behaviour and the results of collision avoidance algorithms used to react to obstacles.

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Section: Extended Examplementioning

confidence: 99%

Cross-Entropy as a Metric for the Robustness of Drone Swarms

Cofta

Ledziński

Śmigiel

et al. 2020

Entropy

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“…When s(ξ, µ) = 0, the state vector of (13) is located on the sliding surface, where u r will be replaced by some equivalent control u re defined soon. In addition, to ensure that the control laws in (14) and (15) are implementable, bounded-ness of ξ 1 , u r1 and µ over t ∈ [0, ∞) is needed. This is guaranteed by the stable eigenvalues of 11 .…”

Section: Existence and Properties For Smcmentioning

confidence: 99%

“…Now we are ready to claim existence and properties for the leader-average equation (12) to run into the sliding surface S t (•) and remain there under the control laws (14) and (15). The proof details for Theorem 2 are given in Appendix.…”

Section: Existence and Properties For Smcmentioning

confidence: 99%

“…Then, the state vector of (12) will be driven into the sliding surface S t (µ) in finite time t s < ∞ by the control input u r in (14) and (15)…”

Section: Existence and Properties For Smcmentioning

confidence: 99%

“…Formation control under iterative learning can be found in [4], [27], [28]. To achieve flexible or time-varying multi-agent formation, interesting discussions are summarized in [1], [9], [10], [15], [50].…”

Section: Introductionmentioning

confidence: 99%

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Multi-Agent Trajectory-Tracking Flexible Formation via Generalized Flocking and Leader-Average Sliding Mode Control

Zhou

Zeng

2020

IEEE Access

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This paper reports a flexible (or time-varying) multi-agent formation approach with average trajectory tracking for second-order integral multi-agent networks with single virtual leaders. The approach is developed by means of time-varying Olfati-Saber flocking algorithms, and sliding mode control (SMC) in terms of the leader-average dynamics. More precisely, SMC-specifying average trajectory tracking is combined with flexible multi-agent flocking driven by the Olfati-Saber flocking algorithms with timevarying weighting norm. Existence conditions and properties of the suggested multi-agent formation are examined rigorously, together with implementation formulas. It is shown that by designing the sliding surface and the time-varying weighting matrix appropriately, flexible formation with finite-time trajectory tracking can be achieved, free of control action chattering; moreover, the sliding mode control and formation control can be designed separately. Numerical examples are given to illustrate the main results. INDEX TERMS Multi-agent, flexible flocking formation, leader-average model, sliding mode control, trajectory tracking.

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Distributed dynamic matrix control with constrained optimization for collision and obstacle avoidance of simulated multiple quadcopters

Dubay

Pan

2023

Asian Journal of Control

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A system of fast moving quadcopters has a high risk of collisions with neighboring quadcopters or obstacles. The objective of this work is to develop a control strategy for collision and obstacle avoidance of multiple quadcopters. In this paper, the problem of distributed dynamic matrix control (DMC) for collision avoidance among a team of multiple quadcopters attempting to reach consensus in the horizontal plane and yaw direction ( , and ) is investigated. Violations of a predetermined safety radius generates output constraints on the DMC optimization function, which has not been dealt with in the literature. Different from past works, the proposed strategy can perform collision avoidance in the , , and ‐directions. In addition, logarithmic barrier functions are implemented as input rate constraints on the control actions. Extensive simulation studies for a team of quadcopters illustrate promising results of the proposed control strategy and case variations. In addition, DMC parameter effects on the system performance are studied, and a successful study for obstacle avoidance is presented.

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Gradient-based collision avoidance algorithm for second-order multi-agent formation control

Cited by 8 publications

References 23 publications

Cross-Entropy as a Metric for the Robustness of Drone Swarms

Cross-Entropy as a Metric for the Robustness of Drone Swarms

Multi-Agent Trajectory-Tracking Flexible Formation via Generalized Flocking and Leader-Average Sliding Mode Control

Distributed dynamic matrix control with constrained optimization for collision and obstacle avoidance of simulated multiple quadcopters

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