|Optimal Formation of Multirobot Systems Based on a Recurrent Neural Network|
|Wang, Yunpeng; Cheng, Long; Hou, ZengGuang; Yu, Junzhi; Tan, Min
|Source Publication||IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
|Abstract||The optimal formation problem of multirobot systems is solved by a recurrent neural network in this paper. The desired formation is described by the shape theory. This theory can generate a set of feasible formations that share the same relative relation among robots. An optimal formationmeans that finding one formation from the feasible formation set, which has the minimum distance to the initial formation of the multirobot system. Then, the formation problem is transformed into an optimization problem. In addition, the orientation, scale, and admissible range of the formationcan also be considered as the constraints in the optimization problem. Furthermore, if all robots are identical, their positions in the system are exchangeable. Then, each robot does not necessarily move to one specific position in the formation. In this case, the optimal formation problem becomes a combinational optimization problem, whose optimal solution is very hard to obtain. Inspired by the penalty method, this combinational optimization problem can be approximately transformed into a convex optimization problem. Due to the involvement of the Euclidean norm in the distance, the objective function of these optimization problems are nonsmooth. To solve these nonsmooth optimization problems efficiently, a recurrent neural network approach is employed, owing to its parallel computation ability. Finally, some simulations and experiments are given to validate the effectiveness and efficiency of the proposed optimal formation approach.|
|Keyword||Combinational Optimization Problem
Recurrent Neural Network
|WOS Headings||Science & Technology
|WOS Keyword||NONLINEAR VARIATIONAL-INEQUALITIES
; CONVEX-OPTIMIZATION PROBLEMS
; NONHOLONOMIC MOBILE ROBOTS
; CONSTRAINED OPTIMIZATION
; COOPERATIVE CONTROL
; PREDICTIVE CONTROL
|Funding Organization||National Natural Science Foundation of China(61370032
; Beijing Nova Program(Z121101002512066)
|WOS Research Area||Computer Science
|WOS Subject||Computer Science, Artificial Intelligence
; Computer Science, Hardware & Architecture
; Computer Science, Theory & Methods
; Engineering, Electrical & Electronic
|Corresponding Author||Cheng, Long|
|Affiliation||State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, China|
Wang, Yunpeng,Cheng, Long,Hou, ZengGuang,et al. Optimal Formation of Multirobot Systems Based on a Recurrent Neural Network[J]. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,2016,27(2):322-333.
Wang, Yunpeng,Cheng, Long,Hou, ZengGuang,Yu, Junzhi,&Tan, Min.(2016).Optimal Formation of Multirobot Systems Based on a Recurrent Neural Network.IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,27(2),322-333.
Wang, Yunpeng,et al."Optimal Formation of Multirobot Systems Based on a Recurrent Neural Network".IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 27.2(2016):322-333.
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