TY - GEN
T1 - Deep Continuum Deformation Coordination and Optimization with Safety Guarantees
AU - Uppaluru, Harshvardhan
AU - Rastgoftar, Hossein
N1 - Funding Information: *This work has been supported by the National Science Foundation under Award Nos. 2133690 and 1914581. Publisher Copyright: © 2023 American Automatic Control Council.
PY - 2023
Y1 - 2023
N2 - In this paper, we develop and present a novel strategy for safe coordination of a large-scale multi-agent team with "local deformation"capabilities. Multi-agent coordination is defined by our proposed method as a multi-layer deformation problem specified as a Deep Neural Network (DNN) optimization problem. The proposed DNN consists of p hidden layers, each of which contains artificial neurons representing unique agents. Furthermore, based on the desired positions of the agents of hidden layer k (k = 1,⋯, p-1), the desired deformation of the agents of hidden• layer k + 1 is planned. In contrast to the available neural network learning problems, our proposed neural network optimization receives time-invariant reference positions of the boundary agents as inputs and trains the weights based on the desired trajectory of the agent team configuration, where the weights are constrained by certain lower and upper bounds to ensure inter-agent collision avoidance. We simulate and provide the results of a large-scale quadcopter team coordination tracking a desired elliptical trajectory to validate the proposed approach.
AB - In this paper, we develop and present a novel strategy for safe coordination of a large-scale multi-agent team with "local deformation"capabilities. Multi-agent coordination is defined by our proposed method as a multi-layer deformation problem specified as a Deep Neural Network (DNN) optimization problem. The proposed DNN consists of p hidden layers, each of which contains artificial neurons representing unique agents. Furthermore, based on the desired positions of the agents of hidden layer k (k = 1,⋯, p-1), the desired deformation of the agents of hidden• layer k + 1 is planned. In contrast to the available neural network learning problems, our proposed neural network optimization receives time-invariant reference positions of the boundary agents as inputs and trains the weights based on the desired trajectory of the agent team configuration, where the weights are constrained by certain lower and upper bounds to ensure inter-agent collision avoidance. We simulate and provide the results of a large-scale quadcopter team coordination tracking a desired elliptical trajectory to validate the proposed approach.
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U2 - 10.23919/ACC55779.2023.10156302
DO - 10.23919/ACC55779.2023.10156302
M3 - Conference contribution
T3 - Proceedings of the American Control Conference
SP - 1353
EP - 1358
BT - 2023 American Control Conference, ACC 2023
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2023 American Control Conference, ACC 2023
Y2 - 31 May 2023 through 2 June 2023
ER -