Robust adaptive formation control and collision avoidance for electrically driven non-holonomic mobile robots

Park, B.S.; Park, J.B.; Choi, Yoon Ho

doi:10.1049/iet-cta.2010.0353

Cited by 27 publications

(14 citation statements)

References 20 publications

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“…In [24,25], the cyclic pursuit control laws were proposed to steer the unicycles to move on a circle at a constant speed. Considering the dynamical model of uncertain non-holonomic robots in obstacle environment, the authors in [2] derived a robust adaptive controller to ensure the formation error uniformly ultimately bounded. For the cooperative control of general non-holonomic agents, Dong and Farrell presented systematic methods and control algorithms in [13,14] to ensure the entire state of each agent tracks a moving target point, and analysed the effects of time-delays on the algorithms.…”

Section: Introductionmentioning

confidence: 99%

Position centroid rendezvous and centroid formation of multiple unicycle agents

Xie

2014

IET Control Theory & Applications

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This work investigates the position centroid rendezvous/formation problems of multiple unicycle agents for the first time. By constructing a new output, the unicycle model is converted to canonical normal form via feedback linearisation approach. Then, we propose the centroid rendezvous and the centroid formation control laws, respectively, guaranteeing that all the agents globally meet at the common initial centroid location and the desired geometric pattern centred at the initial centroid. The proposed control laws are distributed, smooth and time-varying, ensuring the internal orientation states and the velocity inputs convergent to some fixed values and zero, respectively. All the results are proved under the communication scenarios of fixed directed balanced graph with a spanning tree. Simulation results verify the effectiveness and the robustness of the proposed control schemes.

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Section: Introductionmentioning

confidence: 99%

Position centroid rendezvous and centroid formation of multiple unicycle agents

Xie

2014

IET Control Theory & Applications

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“…Recently, leader-following formation control methods based on dynamic model have been proposed in [14][15][16][17], where the dynamics of the leader, such as the leader's pose (i.e., the position and direction angle) and velocity, becomes an important part of the formation control for its follower robots. In [14], leader-following formation was constructed based on the assumption that the leader communicates its pose, velocity vector, and the derivative of the velocity vector to its follower robot.…”

Section: Introductionmentioning

confidence: 99%

“…However, the measurements of the separation and bearing by the follower robots are very difficult. In [17], a projection algorithm was used to estimate the velocity of the leader robot and leaderfollowing formation control laws were designed for the following robots based on a dynamic surface control technique. Yet, the communicating information of the leader's dynamics in [17] is required to be as little as possible, for the existence of cost and possible information loss in the information communication between the leader and its followers.…”

Section: Introductionmentioning

confidence: 99%

“…All the above mentioned results for the formation control are about asymptotic convergence. In other words, the formations in [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17] cannot be achieved in finite time. However, finite-time formation control may be more attractive for the advantages of finite time control: higher convergence rates, higher accuracy, better disturbance rejections, and so on [18][19][20][21].…”

Section: Introductionmentioning

confidence: 99%

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Neural Network Observer-Based Finite-Time Formation Control of Mobile Robots

Zhang

Sun

2014

Mathematical Problems in Engineering

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This paper addresses the leader-following formation problem of nonholonomic mobile robots. In the formation, only the pose (i.e., the position and direction angle) of the leader robot can be obtained by the follower. First, the leader-following formation is transformed into special trajectory tracking. And then, a neural network (NN) finite-time observer of the follower robot is designed to estimate the dynamics of the leader robot. Finally, finite-time formation control laws are developed for the follower robot to track the leader robot in the desired separation and bearing in finite time. The effectiveness of the proposed NN finite-time observer and the formation control laws are illustrated by both qualitative analysis and simulation results.

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“…Coordinated control underlines the capability of vehicles to interact and communicate to accomplish the assigned mission [5][6][7]. In the frame of coordinated control of multivehicle systems (MVS), there are many approaches proposed in the literature (see [7] and references therein).…”

Section: Introductionmentioning

confidence: 99%

Task priority approach to the coordinated control of a team of flying vehicles in the presence of obstacles

Mattei

Scordamaglia

2012

IET Control Theory &Amp; Applications

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This study describes the application of the task priority null-space behavioural technique to the coordinated control of a team of flying vehicles with hovering capabilities, such as helicopters or quadrotors, in the presence of obstacles and no-fly zones. Once a flight mission is assigned to the team in terms of a target region to reach, each flying vehicle is required to accomplish four tasks with assigned priorities. Formation flight and collisions avoidance with other vehicles and unknown or moving obstacles tasks are formulated via analytical expressions as required by the classical null-space behavioural approach. Move to target and a priori known obstacle avoidance behaviours are obtained by solving a partial differential equation problem within the flight domain. The effectiveness of the proposed technique is discussed with regards to two-dimensional and three-dimensional numerical examples.

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Robust adaptive formation control and collision avoidance for electrically driven non-holonomic mobile robots

Cited by 27 publications

References 20 publications

Position centroid rendezvous and centroid formation of multiple unicycle agents

Position centroid rendezvous and centroid formation of multiple unicycle agents

Neural Network Observer-Based Finite-Time Formation Control of Mobile Robots

Task priority approach to the coordinated control of a team of flying vehicles in the presence of obstacles

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