Reinforcement Learning-Based Continuous Action Space Path Planning Method for Mobile Robots

Zhang, Weimin; Guo-yong, Wang

doi:10.1155/2022/9069283

Cited by 5 publications

(3 citation statements)

References 22 publications

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“…The speed of the robot can be set prior to the computation of the trajectory, which provides a great advantage in time-constrained applications. Zhang et al [46] adopted trajectories from a practical life motion planner and previously executed trajectories as feedback observations. This reinforcement algorithm model helped the mobile robot with motion adaptation behavior concerning environmental changes.…”

Section: Motion Control and Adaptation With Rlmentioning

confidence: 99%

Dynamic Obstacle Avoidance and Path Planning through Reinforcement Learning

2023

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The use of reinforcement learning (RL) for dynamic obstacle avoidance (DOA) algorithms and path planning (PP) has become increasingly popular in recent years. Despite the importance of RL in this growing technological era, few studies have systematically reviewed this research concept. Therefore, this study provides a comprehensive review of the literature on dynamic reinforcement learning-based path planning and obstacle avoidance. Furthermore, this research reviews publications from the last 5 years (2018–2022) to include 34 studies to evaluate the latest trends in autonomous mobile robot development with RL. In the end, this review shed light on dynamic obstacle avoidance in reinforcement learning. Likewise, the propagation model and performance evaluation metrics and approaches that have been employed in previous research were synthesized by this study. Ultimately, this article’s major objective is to aid scholars in their understanding of the present and future applications of deep reinforcement learning for dynamic obstacle avoidance.

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Section: Motion Control and Adaptation With Rlmentioning

confidence: 99%

Dynamic Obstacle Avoidance and Path Planning through Reinforcement Learning

2023

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“…Table 8 lists several RL techniques tailored for different optimization facets, specifically equipment, operation, and control, across diverse processes. polymerization DDPG [204] robot path planning DDPG [205] automotive hot sheet metal forming Q-learning [206] heating energy consumption DQN, PPO, AC [207] operation construction tunnel excavation DQN [208] manufacturing injection molding AC [209] RL holds significant promise for smart manufacturing, facilitating continuous refinement and optimization of production processes. By leveraging RL, industries can curtail costs, augment efficiency, and elevate product quality, concurrently reducing the product development cycle.…”

Section: Reinforcement Learningmentioning

confidence: 99%

A Review on Intelligent Control Theory and Applications in Process Optimization and Smart Manufacturing

Lee

2023

Processes

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In the evolving landscape of manufacturing, the integration of intelligent control theory stands as a pivotal advancement, driving both process optimization and the paradigm of smart manufacturing. This review delves into the multifaceted applications of intelligent control theory, emphasizing its role in equipment, operations, and controls optimization. With a focus on three primary methodologies—fuzzy logic, neural networks, and genetic algorithms—the paper elucidates their biological parallels and their significance in simulation, modeling, and optimization. The transformative potential of smart manufacturing, synonymous with Industry 4.0, is also explored, highlighting its foundation in data, automation, and artificial intelligence. Drawing from a comprehensive analysis of recent literature, the review underscores the growing interest in this domain, as evidenced by the surge in publications and citations over the past decade. The overarching aim is to provide contemporary discourse on the applications and implications of intelligent control theory in the realms of process optimization and smart manufacturing.

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“…With the development of computer science and artificial intelligence, intelligent algorithms have received widespread attention due to vast database and powerful computing capability to perform various tasks. Reinforcement Learning algorithm [11][12] is a typical example. It learns the optimal policy through interaction with the environment, thus can overcome the difficulties associated with map modeling.…”

Section: Introductionmentioning

confidence: 99%

Local Path Planning of Mobile Robots Based on the Improved SAC Algorithm

Zhou,

Li,

Zhang

et al. 2024

ijacsa

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This paper proposes a new EP-PER-SAC algorithm to solve the problems of slow training speed and low learning efficiency of the SAC (Soft Actor Critic) algorithm in the local path planning of mobile robots by introducing the Priority Experience Replay (PER) strategy and Experience Pool (EP) adjustment technique. This algorithm replaces equal probability random sampling with sampling based on the priority experience to increase the frequency of extracting important samples, thereby improves the stability and convergence speed of model training. On this basis, it requires to continuously monitor the learning progress and exploration rate changes of the robot to dynamically adjust the experience pool, so the robot can adapt effectively to the environment changes and the storage requirements and learning efficiency of the algorithm are balanced. Then, the algorithm's reward and punishment function is improved to reduce the blindness of algorithm training. Finally, experiments are conducted under different obstacle environments to verify the feasibility of the algorithm based on ROS (Robot Operating System) simulation platform and real environment. The results show that the improved EP-PER-SAC algorithm has a shorter path length and faster model convergence speed than the original SAC algorithm and PER-SAC algorithm.

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Reinforcement Learning-Based Continuous Action Space Path Planning Method for Mobile Robots

Cited by 5 publications

References 22 publications

Dynamic Obstacle Avoidance and Path Planning through Reinforcement Learning

Dynamic Obstacle Avoidance and Path Planning through Reinforcement Learning

A Review on Intelligent Control Theory and Applications in Process Optimization and Smart Manufacturing

Local Path Planning of Mobile Robots Based on the Improved SAC Algorithm

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