Formation control with collision avoidance for a multi-UAV system using decentralized MPC and consensus-based control

Kuriki, Yasuhiro; Namerikawa, Toru

doi:10.1109/ecc.2015.7331006

Cited by 46 publications

(33 citation statements)

References 15 publications

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“…In particular, Keviczky et al (2008) computed a set of inputs for a team of aerial vehicles navigating in, or towards, a given formation, and Kuriki and Namerikawa (2015) relied on a leader to compute the formation configuration. Our approach is leaderless and can adjust the size of the formation to avoid obstacles.…”

Section: Related Workmentioning

confidence: 99%

Distributed multi-robot formation control in dynamic environments

et al. 2018

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This paper presents a distributed method for formation control of a homogeneous team of aerial or ground mobile robots navigating in environments with static and dynamic obstacles. Each robot in the team has a finite communication and visibility radius and shares information with its neighbors to coordinate. Our approach leverages both constrained optimization and multi-robot consensus to compute the parameters of the multi-robot formation. This ensures that the robots make progress and avoid collisions with static and moving obstacles. In particular, via distributed consensus, the robots compute (a) the convex hull of the robot positions, (b) the desired direction of movement and (c) a large convex region embedded in the four dimensional position-time free space. The robots then compute, via sequential convex programming, the locally optimal parameters for the formation to remain within the convex neighborhood of the robots. The method allows for reconfiguration. Each robot then navigates towards its assigned position in the target collision-free formation via an individual controller that accounts for its dynamics. This approach is efficient and scalable with the number of robots. We present an extensive evaluation of the communication requirements and verify the method in simulations with up to sixteen quadrotors. Lastly, we present experiments with four real quadrotors flying in formation in an environment with one moving human.

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Section: Related Workmentioning

confidence: 99%

Distributed multi-robot formation control in dynamic environments

et al. 2018

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“…The paper by Kuriki, Y.; et al [5] presents a cooperative control strategy for collision avoidance in a multi-UAV system by using decentralized predictive and consensus-based control. Liu, W.; et al develop a novel path planner to control multiple UAVs synchronously based on distributed path generation and a bi-level programming technique.…”

Section: Related Workmentioning

confidence: 99%

“…Some UAV monitoring and control systems focus on wireless radio as the most crucial aspect of the control unit [3,4]. Other research groups have concentrated on UAV collision avoidance methods, developing complex algorithms for UAVs to avoid each other, obstacles or intruders [5,6,7,8,9,10,11]. Moreover, remote measurement and control technologies for UAVs present certain problems [12,13].…”

Section: Introductionmentioning

confidence: 99%

Development of Cloud-Based UAV Monitoring and Management System

Itkin

Kim

Park

2016

Sensors

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Unmanned aerial vehicles (UAVs) are an emerging technology with the potential to revolutionize commercial industries and the public domain outside of the military. UAVs would be able to speed up rescue and recovery operations from natural disasters and can be used for autonomous delivery systems (e.g., Amazon Prime Air). An increase in the number of active UAV systems in dense urban areas is attributed to an influx of UAV hobbyists and commercial multi-UAV systems. As airspace for UAV flight becomes more limited, it is important to monitor and manage many UAV systems using modern collision avoidance techniques. In this paper, we propose a cloud-based web application that provides real-time flight monitoring and management for UAVs. For each connected UAV, detailed UAV sensor readings from the accelerometer, GPS sensor, ultrasonic sensor and visual position cameras are provided along with status reports from the smaller internal components of UAVs (i.e., motor and battery). The dynamic map overlay visualizes active flight paths and current UAV locations, allowing the user to monitor all aircrafts easily. Our system detects and prevents potential collisions by automatically adjusting UAV flight paths and then alerting users to the change. We develop our proposed system and demonstrate its feasibility and performances through simulation.

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“…Their proposed solution was fully decentralized and was able to take into account the saturation constraints for the control inputs as well as the collision avoidance constraints. Furthermore, in [46], the authors proposed a decentralized MPC and a consensus based control for cooperative formation control strategy for multiple UAVs. In this case and for a flying formation in a three dimensional space, consensus-based control algorithms were applied, while for avoiding collisions, an optimization problem with coupled constraints was solved.…”

Section: A Related Workmentioning

confidence: 99%

“…Few of them utilized a three dimensional space but the authors used a simple linear model or did not consider the attitude problem of the UAV ( [44], [46], [48]). In general, only few works considered the nonlinear model in a three dimensional space but still the authors, in this approaches have not considered cooperation strategies and focused only on one UAV control problem ( [41], [42], [43]and [45]).…”

Section: A Related Workmentioning

confidence: 99%

Distributed model predictive control for unmanned aerial vehicles

Mansouri

Nikolakopoulos

Gustafsson

2015

2015 Workshop on Research, Education and Development of Unmanned Aerial Systems (RED-UAS)

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Abstract-In this article a distributed model predictive control scheme, for the cooperative motion control of Unmanned Aerial Vehicles (UAVs) is being presented. The UAVs are modeled by a 6-DOF nonlinear kinematic model. Two di↵erent control architectures: a centralized and a distributed MPC, are studied and evaluated in simulation experiments. In the centralized approach, one central MPC controller is responsible for the movement coordination of all the UAVs, while in the distributed approach each aerial vehicle plans only for its own actions, while the objective function is coupled with the behavior of the rest of the team members and the constraints are decoupled. In this approach, each agent only shares the future position of itself with the other agents to avoid collisions. For reducing the computation time and complexity, only one step ahead prediction in the corresponding MPC schemes have been considered without a loss of generality. Finally, the e ciency of the overall suggested decentralized MPC scheme, as well as it comparison with the centralized approach, is being evaluated through the utilization of multiple simulation scenarios.

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Formation control with collision avoidance for a multi-UAV system using decentralized MPC and consensus-based control

Cited by 46 publications

References 15 publications

Distributed multi-robot formation control in dynamic environments

Distributed multi-robot formation control in dynamic environments

Development of Cloud-Based UAV Monitoring and Management System

Distributed model predictive control for unmanned aerial vehicles

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