Collision Avoidance Among Dense Heterogeneous Agents Using Deep Reinforcement Learning

Zhu, Kai; Li, Bin; Zhe, Wenming; Zhang, Tao

doi:10.1109/lra.2022.3222989

Cited by 12 publications

(1 citation statement)

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“…This method shows improved performance in terms of formation error, formation convergence rate, and obstacle avoidance success rate. Considering the heterogeneity of agents cannot be overlooked in crowded scenarios, Zhu et al 17 models agents using Oriented Bounding Capsules (OBC) and transforms the interaction state space of robot-obstacle agent pairs. To address speed heterogeneity, a collision risk function related to speed is designed to shape robot behavior.…”

Section: Introductionmentioning

confidence: 99%

Adaptive collision-free control for UAVs with discrete-time system based on reinforcement learning

Huo,

Guo

2024

Advances in Mechanical Engineering

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A flexible reinforcement learning (RL) optimal collision-avoidance control formulation for unmanned aerial vehicles (UAVs) with discrete-time frameworks is revealed in this work. By utilizing the neural network (NN) estimating capacity and the actor-critic control scheme of the RL technique, an adaptive RL optimal collision-free controller with a minimal learning parameter (MLP) is formulated, which is based on a novel strategic utility function. The optimal collision-avoidance control issue, which couldn’t be addressed in the prior literature, can be resolved by the suggested approaches. Furthermore, the proposed MPL adaptive optimal control formulation allows for a reduction in the quantity of adaptive laws, leading to reduced computational complexity. Additionally, a rigorous stability analysis is provided, demonstrating that the uniform ultimate boundedness (UUB) of all signals in the closed-loop system is ensured by the proposed adaptive RL. Finally, the simulation outcomes illustrate the effectiveness of the proposed optimal RL control approaches.

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Section: Introductionmentioning

confidence: 99%