Deep Reinforcement Learning Based Tracking Control of Unmanned Vehicle with Safety Guarantee

Luo, Zhongjing; Zhou, Jialing; Wen, Guanghui

doi:10.23919/ascc56756.2022.9828057

2022 13th Asian Control Conference (ASCC) 2022

DOI: 10.23919/ascc56756.2022.9828057

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Deep Reinforcement Learning Based Tracking Control of Unmanned Vehicle with Safety Guarantee

Zhongjing Luo

Jialing Zhou

Guanghui Wen

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Multi-objective reward shaping for global and local trajectory planning of wing-in-ground crafts based on deep reinforcement learning

Zhang

et al. 2023

Aeronaut. j.

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The control of a wing-in-ground craft (WIG) usually allows for many needs, like cruising, speed, survival and stealth. Various degrees of emphasis on these requirements result in different trajectories, but there has not been a way of integrating and quantifying them yet. Moreover, most previous studies on other vehicles’ multi-objective trajectory is planned globally, lacking for local planning. For the multi-objective trajectory planning of WIGs, this paper proposes a multi-objective function in a polynomial form, in which each item represents an independent requirement and is adjusted by a linear or exponential weight. It uses the magnitude of weights to demonstrate how much attention is paid relatively to the corresponding demand. Trajectories of a virtual WIG model above the wave trough terrain are planned using reward shaping based on the introduced multi-objective function and deep reinforcement learning (DRL). Two conditions are considered globally and locally: a single scheme of weights is assigned to the whole environment, and two different schemes of weights are assigned to the two parts of the environment. Effectiveness of the multi-object reward function is analysed from the local and global perspectives. The reward function provides WIGs with a universal framework for adjusting the magnitude of weights, to meet different degrees of requirements on cruising, speed, stealth and survival, and helps WIGs guide an expected trajectory in engineering.

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Multi-objective reward shaping for global and local trajectory planning of wing-in-ground crafts based on deep reinforcement learning

Zhang

et al. 2023

Aeronaut. j.

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show abstract

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Deep Reinforcement Learning Based Tracking Control of Unmanned Vehicle with Safety Guarantee

Cited by 1 publication

References 21 publications

Multi-objective reward shaping for global and local trajectory planning of wing-in-ground crafts based on deep reinforcement learning

Multi-objective reward shaping for global and local trajectory planning of wing-in-ground crafts based on deep reinforcement learning

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