Dynamic task allocation in multi-robot coordination for moving target tracking: A distributed approach

Jin, Long; Li, Shuai; La, Hung Manh; Zhang, Xin; Hu, Bin

doi:10.1016/j.automatica.2018.11.001

Cited by 123 publications

(32 citation statements)

References 28 publications

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“…5) Join the path rationality check. Calculate the coordinate difference between the previous path point and the next path point (Y i − Y i−1 ) 2 , compare the coordinate difference wether satisfied the reasonable evaluation step θ t , if it is not satisfied, the following replacement is required, where i∈ [2,21],…”

Section: A Local Fast Searchmentioning

confidence: 99%

“…Path planning [1] or dynamical motion tracking [2]- [4] have received more and more attentions of researchers and achieved rapid development due to its wide applications [5], [6] in recent decades, by following new intelligent optimization algorithms. Path planning in three-dimensional…”

Section: Introductionmentioning

confidence: 99%

“…Algorithm 2 ACO-BAS Algorithm for 3D Path PlanningInput: Establish the fitness function f (x t ), wherevariable x t =[x 1 , • • • , x 2i ], i∈[2,21], initialize the parameters x 0 , δ 0 , c, θ 0 , n. Output:x bst , f bst . while (t<T max ) do , Z 1 , • • • , Y 21 , Z 21 ].…”

mentioning

confidence: 99%

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Optimal Path Finding With Beetle Antennae Search Algorithm by Using Ant Colony Optimization Initialization and Different Searching Strategies

Jiang

Lin

et al. 2020

IEEE Access

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Intelligent algorithm acts as one of the most important solutions to path planning problem. In order to solve the problems of poor real-time and low accuracy of the heuristic optimization algorithm in 3D path planning, this paper proposes a novel heuristic intelligent algorithm derived from the Beetle Antennae Search (BAS) algorithm. The algorithm proposed in this paper has the advantages of wide search range and high search accuracy, and can still maintain a low time complexity when multiple mechanisms are introduced. This paper combines the BAS algorithm with three non-trivial mechanisms proposed to solve the problems of low search efficiency and poor convergence accuracy in 3D path planning. The algorithm contains three non-trivial mechanisms, including local fast search, aco initial path generation, and searching information orientation. At first, local fast search mechanism presents a specific bounded area and add fast iterative exploration to speed up the convergence of path finding. Then aco initial path generation mechanism is initialized by Ant Colony Optimization (ACO) as a pruning basis. The initialization of the ACO algorithm can quickly obtain an effective path. Using the exploration trend of this path, the algorithm can quickly obtain a locally optimal path. Thirdly, searching information orientation mechanism is employed for BAS algorithm to guarantee the stability of the path finding, thereby avoiding blind exploration and reducing wasted computing resources. Simulation results show that the algorithm proposed in this paper has higher search accuracy and exploration speed than other intelligent algorithms, and improves the adaptability of the path planning algorithms in different environments. The effectiveness of the proposed algorithm is verified in simulation. INDEX TERMS Beetle antennae search, optimal path finding, bio-inspired optimization, search orientation.

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Section: A Local Fast Searchmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Optimal Path Finding With Beetle Antennae Search Algorithm by Using Ant Colony Optimization Initialization and Different Searching Strategies

Jiang

Lin

et al. 2020

IEEE Access

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“…In addition, another neural network in the continuous form is developed in [37] for robots to realize repetitive motion generation with equality and inequality constraints but the perturbations are ignored. In [38], [39], a distributed coordination model is developed for multiple robots for competitive target tracking, where sufficient theoretical analysis and simulations are given to demonstrate its feasibility but the continuous-time system is unstable and fragile. As exploited in [40], a dual neural network is designed for manipulator redundancy resolution subject to both joint and joint speed constraints, presenting exponential convergence but disturbance is overlooked.…”

Section: Introductionmentioning

confidence: 99%

Discrete Perturbation-Immunity Neural Network for Dynamic Constrained Redundant Robot Control

Yang

Chen

et al. 2020

IEEE Access

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Considering the necessity of merging the physical constraints in joint space for redundant robot motion control in practice by regarding the kinematics of robots, a discrete perturbation-immunity neural network (DPINN) model with high robustness and predominant convergence is proposed in this article for managing the problem. It is worth emphasizing that this proposed neural network is developed to provide a solution algorithm to dispose of the practical applications in robot motion control that is investigated by reforming it into perturbed dynamic quadratic programming (QP) with equality and inequality constraints, which remedies the lack and drawbacks of existing methodologies. Moreover, theoretical verifications and results verify the convergence performance and noise suppression ability of the proposed neural network, which is further verified by simulation examples. In addition, the simulations on robot motion control embody its superiority and powerful versatility compared to the existing methods. In summary, the proposed neural network extends the sphere of application in perturbed dynamic QP with double-bound constraints and offers a feasible scheme capable of controlling robots in a neural-network thought. INDEX TERMS Discrete perturbation-immunity neural network (DPINN), physical constraints, robots, theoretical verifications.

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“…Currently, robotics is playing an indispensable role in modern society as well as academic researches and industrial applications [21][22][23][24][25][26][27][28][29][30][31][32][33][34]. Yang et al introduce physical haptic feedback mechanism in robots to result in muscle activity, in order to achieve intuitive human impedance transfer through a designed coupling interface in [21].…”

Section: Introductionmentioning

confidence: 99%

Intelligent Controllers for Multirobot Competitive and Dynamic Tracking

Liu

et al. 2018

Complexity

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This paper focuses on the problem of target tracking using k fittest robots in a group of n mobile robots with n>k. We present centralized and distributed coordination models with all-to-all and limited communications, respectively. For the case of all-to-all communication between robots, theoretical analysis is presented to prove the exponential stability of the whole system. In real applications of robotic networks, a robot may only be allowed to exchange information with a limited number of neighbors. In such a limited communication case where centralized quantity is not available, a consensus filter is used to estimate the centralized quantities in a distributed way, and a distributed competitive target tracking is thus achieved. The stability of the distributed control is also proved in theory. Finally, illustrative examples are provided and analyzed to substantiate the efficacy of the proposed models for tracking moving target in a competition manner with all-to-all communications and limited communications.

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Dynamic task allocation in multi-robot coordination for moving target tracking: A distributed approach

Cited by 123 publications

References 28 publications

Optimal Path Finding With Beetle Antennae Search Algorithm by Using Ant Colony Optimization Initialization and Different Searching Strategies

Optimal Path Finding With Beetle Antennae Search Algorithm by Using Ant Colony Optimization Initialization and Different Searching Strategies

Discrete Perturbation-Immunity Neural Network for Dynamic Constrained Redundant Robot Control

Intelligent Controllers for Multirobot Competitive and Dynamic Tracking

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