Deep Reinforcement Learning for Mobile Robot Navigation

Gromniak, Martin; Stenzel, Jonas

doi:10.1109/acirs.2019.8935944

Cited by 5 publications

(6 citation statements)

References 4 publications

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“…The robot finally gets the optimal policy to achieve the goal by repeating the learning process. In order to use DRL in an E2E robot navigation context, the whole problem setting must be stated and translated into an RL framework [94]. For instance, to avoid collisions, Long et al [93] proposed directly mapping raw 2D laser measurements to desired motion commands using a 4-hidden-layer neural network.…”

Section: Deep Reinforcement Learning and End-to-end Approachesmentioning

confidence: 99%

Evolution of Socially-Aware Robot Navigation

et al. 2023

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In recent years, commercial and research interest in service robots working in everyday environments has grown. These devices are expected to move autonomously in crowded environments, maximizing not only movement efficiency and safety parameters, but also social acceptability. Extending traditional path planning modules with socially aware criteria, while maintaining fast algorithms capable of reacting to human behavior without causing discomfort, can be a complex challenge. Solving this challenge has involved the development of proactive systems that take into account cooperation (and not only interaction) with the people around them, the determined incorporation of approaches based on Deep Learning, or the recent fusion with skills coming from the field of human–robot interaction (speech, touch). This review analyzes approaches to socially aware navigation and classifies them according to the strategies followed by the robot to manage interaction (or cooperation) with humans.

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Section: Deep Reinforcement Learning and End-to-end Approachesmentioning

confidence: 99%

Evolution of Socially-Aware Robot Navigation

et al. 2023

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“…Indeed, DRL shows its powerful abilities in the field of autonomous driving, as discussed in [18] and [19]. It has also brought new and promising solutions to traffic control issues [20], [21]. More details about the DRL controller design are given in Section III.…”

Section: B Practical Considerationsmentioning

confidence: 99%

Coordination Between Connected Automated Vehicles and Pedestrians to Improve Traffic Safety and Efficiency at Industrial Sites

et al. 2022

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Transportation in controlled industrial sites provides a conducive environment for technologies of Connected Automated Vehicles (CAV). Recent studies show that safe and efficient road sharing between CAVs and pedestrians is challenging. Besides safety issues, a significant loss of time occurs when pedestrians cross a stream of CAVs. Currently, many techniques have been employed to improve the coordination between CAVs and pedestrians. They focus on pedestrian detection, display of the intention of CAVs, and cooperative collision avoidance. However, one of the most significant sources of information that the pedestrian uses for her/his decision-making is the speed profile of CAVs. This paper aims to provide a safe and efficient pedestrian crossing at industrial sites through communicative crossing behavior. To this end, a suitable speed profile of the CAV is designed by assuming that pedestrians and CAV play a cooperative game to move as close as possible to their desired speed. First, a system analysis is proposed to derive the optimal decision and trajectory for each agent. Then, Deep Reinforcement Learning (DRL) is used to control the longitudinal speed of the CAVs. Compared with Model Predictive Control approach, DRL allows coping with unforeseeable pedestrian behaviors (e.g. long reaction time, varying ideal speed, stop in the middle, etc.). Simulations and experiments with real human testers based on immersive hamlet are performed. Results show that the proposed speed profile outperforms significantly the collision avoidance approach.

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“…Also, we can explain the chattering upon arriving at the equilibrium of USMC-RL. is is because of the continuity of the model-it cannot jump from a very high output (20) to a very small output (2.5). erefore, when it should produce a small output, it is still on its way down.…”

Section: Constant Value Target Figures 8-19 Show the Simulation Resul...mentioning

confidence: 99%

“…Reinforcement learning (RL) is a model-free methodology that optimizes its action on large-scale, complex problems through exploration and exploitation without explicit models [19]. Recently, with the development of deep learning, RL has been combined with deep neural networks to solve many control problems [20][21][22]. Actorcritic learning is one popular framework of RL.…”

Section: Introductionmentioning

confidence: 99%

U-Model-Based Adaptive Sliding Mode Control Using a Deep Deterministic Policy Gradient

Lei

Zhu

2022

Mathematical Problems in Engineering

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This paper presents a U-model-based adaptive sliding mode control (SMC) using a deep deterministic policy gradient (DDPG) for uncertain nonlinear systems. The configuration of the proposed methodology consisted of a U-model framework and an SMC with a variable boundary layer. The U-model framework forms the outer feedback loop that adjusts the overall performance of the nonlinear system, while SMC serves as a robust dynamic inverter that cancels the nonlinearity of the original plant. Besides, to alleviate the chattering problem while maintaining the intrinsic advantages of SMC, a DDPG network is designed to adaptively tune the boundary and switching gain. From the control perspective, this controller combines the interpretability of the U-model and the robustness of the SMC. From the deep reinforcement learning (DRL) point of view, the DDPG calculates nearly optimal parameters for SMC based on current states and maximizes its favourable features while minimizing the unfavourable ones. The simulation results of the single-pendulum system are compared with those of a U-model-based SMC optimized by the particle swarm optimization (PSO) algorithm. The comparison, as well as model visualization, demonstrates the superiority of the proposed methodology.

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Deep Reinforcement Learning for Mobile Robot Navigation

Cited by 5 publications

References 4 publications

Evolution of Socially-Aware Robot Navigation

Evolution of Socially-Aware Robot Navigation

Coordination Between Connected Automated Vehicles and Pedestrians to Improve Traffic Safety and Efficiency at Industrial Sites

U-Model-Based Adaptive Sliding Mode Control Using a Deep Deterministic Policy Gradient

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