Backstepping control design for motion coordination of self-propelled vehicles

Mellish, Rochelle; Paley, Derek A.

doi:10.1109/cdc.2010.5717256

Cited by 7 publications

(7 citation statements)

References 15 publications

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“…For realistic simulation, we have chosen a model in which energy decreases over time T according to Eqs. (3)- (7). For battery consumption, we randomly changed the direction of the drones in this simulation.…”

Section: Numerical Results and Analysismentioning

confidence: 99%

“…According to Reynolds [11], there are three key heuristic rules: separation at short-range to avoid collisions, local interactions (alignment rules) for aligning velocity vectors and long-range attraction of individuals to keep the group flight together. These rules can be interpreted mathematically in agent-based models, which are discrete or continuous dynamic systems that describe the time variation of the speed of individual units [7,12].…”

Section: Related Workmentioning

confidence: 99%

“…Moshtagh et al [21] presented a vision-based flocking control to enhance the existing control laws enabling a group of individuals to align with the leading orientation, as long as the proximity-based closed-graph representing the community topology is connected. In a similar method, Paley et al [7,22] assume an all-to-all communication topology and advance both the control laws that synchronize the group's cooperative movement and the topology of the entire communication infrastructure, that is, by aligning the agent orientation in a known flow area (e.g., a constant velocity wind). While the previous two papers treat objects such as unit velocity vectors from an engineering perspective with first-order rotational dynamics, Mellish et al [7] extended their work by using a backstepping approach to synchronize the second-order rotational dynamics and mass movements in unit space for unmanned vehicles in a uniform flow area.…”

Section: Related Workmentioning

confidence: 99%

“…Another challenge is group flight. If drones are to fly as a group [5][6][7] they must move in the same direction, and each must maintain proper spacing between itself and the others. A drone flying in an incorrect direction is sufficient to bring about a risk of collision with one of its neighbors, and if the distance between the drones is too large, one or more of the drones could leave the group due to communication failure.…”

Section: Introductionmentioning

confidence: 99%

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Mathematical modeling for flocking flight of autonomous multi-UAV system, including environmental factors

2020

KSII TIIS

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In this study, we propose a decentralized mathematical model for predictive control of a system of multi-autonomous unmanned aerial vehicles (UAVs), also known as drones. Being decentralized and autonomous implies that all members make their own decisions and fly depending on the dynamic information received from other unmanned aircraft in the area. We consider a variety of realistic characteristics, including time delay and communication locality. For this flocking flight, we do not possess control for central data processing or control over each UAV, as each UAV runs its collision avoidance algorithm by itself. The main contribution of this work is a mathematical model for stable group flight even in adverse weather conditions (e.g., heavy wind, rain, etc.) by adding Gaussian noise. Two of our proposed variance control algorithms are presented in this work. One is based on a simple biological imitation from statistical physical modeling, which mimics animal group behavior; the other is an algorithm for cooperatively tracking an object, which aligns the velocities of neighboring agents corresponding to each other. We demonstrate the stability of the control algorithm and its applicability in autonomous multi-drone systems using numerical simulations.

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Section: Numerical Results and Analysismentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Mathematical modeling for flocking flight of autonomous multi-UAV system, including environmental factors

2020

KSII TIIS

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“…Similarly, Paley and Peterson assume an all-to-all communication topology and develop distributed control laws in [5] that synchronize the group's collective motion, i.e., align the agent headings, in a known flowfield, e.g., constantvelocity wind. While these two works consider unit-speed nonholonomic vehicles with first-order rotational (heading) dynamics, Mellish and Paley expand upon the work in [5] by using backstepping techniques to synchronize the collective motion of unit-speed nonholonomic vehicles with secondorder rotational dynamics in a uniform flowfield [6].…”

Section: Introductionmentioning

confidence: 99%

Flocking with fixed-wing UAVs for distributed sensing: A stochastic optimal control approach

Quintero

Collins

Hespanha

2013

2013 American Control Conference

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Abstract-This work focuses on enabling multiple UAVs to flock together in order to distribute and collectively perform a given sensing task. Flocking is performed in a leader-follower fashion, and the leader is assumed to already have an effective control policy for the particular task. The UAVs are small fixedwing aircraft cruising at a constant speed and fixed altitude, but experience stochasticity in their dynamics. Accordingly, the control problem for each follower is addressed in the context of stochastic optimal control, wherein the cost is a function of distance and heading with respect to the leader. The problem is solved offline via dynamic programming to minimize the expected cost over a finite horizon and generate a receding horizon optimal control policy. This flocking algorithm was successfully applied in the field, where three camera-equipped UAVs flocked together to perform vision-based target tracking. The experimental results verify the efficacy of the approach and show the benefits of flocking with multiple UAVs to distribute sensing tasks, which include a dramatic reduction in overall sensing error and robustness to individual sensor faults.

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Cooperative control of autonomous systems

Holsapple

Kingston

2011

Intl J Robust & Nonlinear

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This special issue contains a collection of papers on task assignment problems, path planning problems, cooperative control problems and coordinated control problems. Each of the authors has concentrated his or her efforts on both theory development and application to enhance the overall academic contribution of each paper. The theme of this special issue was motivated by the many collaborations of the Cooperative and Intelligent Control of Unmanned Aerial Vehicles (UAV) group in the Control Science Center of Excellence of the Air Force Research Laboratory (AFRL). Published 2011. This article is a US Government work and is in the public domain in the USA.

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Backstepping control design for motion coordination of self-propelled vehicles

Cited by 7 publications

References 15 publications

Mathematical modeling for flocking flight of autonomous multi-UAV system, including environmental factors

Mathematical modeling for flocking flight of autonomous multi-UAV system, including environmental factors

Flocking with fixed-wing UAVs for distributed sensing: A stochastic optimal control approach

Cooperative control of autonomous systems

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