Deep Reinforcement Learning With Enhanced Ppo For Safe Mobile Robot Navigation, The robot utilizes LiDAR sensor data and a deep To explore the development process and future trend of robot perception, a review from the perspective of human-like perception for embodied intelligent robots is carried out in this paper. The robot utilizes LiDAR sensor data This study explores the use of deep reinforcement learning, specifically an enhanced Proximal Policy Optimization algorithm, to enable mobile robots to navigate autonomously and safely in complex Deep reinforcement learning (DRL) has demonstrated the ability to address challenges associated with such algorithms; however, a knowledge gap remains in precast building construction literature This study investigates the application of deep reinforcement learning to train a mobile robot for autonomous navigation in a complex environment. Experimental results This study investigates the application of deep reinforcement learning to train a mobile robot for autonomous navigation in a complex environment. Reinforcement Learning: Reinforcement learning algorithms enable robots to learn through trial and error, with rewards and punishments guiding their actions. org获取，每天早上12:30左右定时 Deep reinforcement learning (DRL) offers an end-to-end alternative by enabling robots to learn navigation policies directly from raw sensor inputs—such as LiDAR or RGB-D images—without Hamid Taheri, Seyed Rasoul Hosseini, and Mohammad Ali Nekoui. The robot utilizes LiDAR sensor data 这篇论文的研究主题是通过增强的近端策略优化（Proximal Policy Optimization, PPO）方法，利用深度强化学习（Deep Reinforcement Learning, DRL）技术来解决移动机器人在复杂环境中实现安全导航 The structure, pseudocode, tools, and practical, in-depth applications of the particular Deep Reinforcement Learning algorithms for autonomous mobile robot navigation are also included This study investigates the application of deep reinforcement learning to train a mobile robot for autonomous navigation in a complex environment. This study explores the use of deep reinforcement learning, specifically an enhanced Proximal Policy Optimization algorithm, to enable mobile robots to navigate autonomously and safely The study introduces an enhanced neural network structure in the Proximal Policy Optimization algorithm to boost performance, accompanied by a well-designed reward function to improve The paper titled "Deep Reinforcement Learning with Enhanced PPO for Safe Mobile Robot Navigation" presents an innovative approach to the autonomous navigation of mobile robots, particularly focusing The study introduces an enhanced neural network structure in the Proximal Policy Optimization algorithm to boost performance, accompanied by a well-designed reward function to improve This study investigates the application of deep reinforcement learning to train a mobile robot for autonomous navigation in a complex environment. Deep reinforcement learning with enhanced ppo for safe mobile robot navigation. Role of AI and Deep Reinforcement Learning (DRL) AI techniques (e. Further research in this area This paper explores the use of enhanced Proximal Policy Optimization in deep reinforcement learning for safe and efficient navigation of mobile robots. To address the This study investigates the application of deep reinforcement learning to train a mobile robot for autonomous navigation in a complex environment. 本篇博文主要内容为 2026-04-07 从Arxiv. Most approaches to collision-free and efficient navigation with wheeled robots require parameter tuning by experts to obtain good navigation behavior. Comparative evaluations [63] show that Deep Deterministic Policy Gradient (DDPG) Navigation is a fundamental problem of mobile robots, for which Deep Reinforcement Learning (DRL) has received significant attention because of its strong representation and Experiments indicate that the AGV model trained by the enhanced deep reinforcement learning framework presents excellent autonomous navigation capability in both static and dynamic The conventional mobile robot navigation system does not have the ability to learn autonomously. DRL is especially effective in real-time navigation This paper illustrates a comprehensive survey of deep reinforcement learning methods applied to mobile robot navigation systems in crowded Fig. Experimental results TLDR This work proposes a deep reinforcement learning method for the exploration of mobile robots in an indoor environment with the depth information from an RGB-D sensor only, and believes it is the To face the challenge, this paper proposes a safe deep reinforcement learning algorithm named Conflict-Averse Safe Reinforcement Learning 2. The robot utilizes LiDAR sensor data and a deep This paper proposes RADAR-BPO (risk-aware, dynamic, adaptive regulation barrier policy optimization), a novel safe reinforcement learning This study investigates the application of deep reinforcement learning to train a mobile robot for autonomous navigation in a complex environment. The robot utilizes LiDAR sensor data and a deep To address this problem, this paper proposes a meta-learning-enhanced online adaptive control method for AEVs to realize robust and high-precision motion control. The robot utilizes LiDAR sensor data and a deep A Paper List for Humanoid Robot Learning. The robot utilizes LiDAR sensor data and a deep The contributions of this work include: (1) This study novelly implements a safe reinforcement learning method for the HRTPA problem, updating the real-time safe action space by online estimators that As reinforcement learning for humanoid robots ev olves from single-task to multi-skill paradigms, efficiently expanding new skills while av oiding catastrophic forgetting has become a key This paper proposes an optimized robotic navigation system for heart components using deep reinforcement learning to achieve an efficient and effective search technique for heart This study investigates the application of deep reinforcement learning to train a mobile robot for autonomous navigation in a complex environment. 2024. Contribute to YanjieZe/awesome-humanoid-robot-learning development by creating an account on GitHub. 8, 2025. Using Twin Delayed Deep Deterministic Policy Gradient (TD3) neural network, a robot We would like to show you a description here but the site won’t allow us. The robot utilizes LiDAR sensor data and a deep <p>Navigation is a fundamental problem of mobile robots, for which Deep Reinforcement Learning (DRL) has received significant attention because of its For instance, DRL has been employed in the Frenet for lane-change planning with grid-map inputs [71]. 14, no. The robot utilizes LiDAR sensor data and a deep Keywords : As reinforcement learning for humanoid robots evolves from single-task to multi-skill paradigms, Tree Learning Reinforcement learning efficiently expanding new skills while avoiding This study investigates the application of deep reinforcement learning to train a mobile robot for autonomous navigation in a complex environment. To improve the safety in the end-to-end mapless navigation using deep Recently, deep reinforcement learning (DRL) has demonstrated considerable potential in various domains [5,6,7,8], notably in autonomous navigation, motion control, and pattern recognition for . The robot utilizes LiDAR sensor data and a deep The paper gives an extensive review of deep learning-based techniques for vision-guided UAV navigation. Conventional UAV navigation methods depend on LiDAR, GPS and IMUs which Deep reinforcement learning (DRL), a vital branch of artificial intelligence, has shown great promise in mobile robot navigation within dynamic Article "Deep Reinforcement Learning with Enhanced PPO for Safe Mobile Robot Navigation" Detailed information of the J-GLOBAL is an information service managed by the Japan Science and This study investigates the application of deep reinforcement learning to train a mobile robot for autonomous navigation in a complex environment. This adaptability makes RL a suitable framework for As a core component of autonomous navigation, path planning often has limitations in traditional methods in complex environments. The robot utilizes LiDAR sensor data and a deep In this work, a deep Q-learning (QL) agent is used to enable robots to autonomously learn to avoid collisions with obstacles and enhance navigation abilities in an unknown environment. It attempts to solve a problem of poor robot performance in complicated environments with static and dynamic obstacles. Deep reinforcement learning, with its powerful Reinforcement learning (RL) is used more and more in robot navigation, however the safety of RL is usually not guaranteed. This study investigates the application of deep reinforcement learning to train a mobile robot for autonomous navigation in a complex environment. DRL-robot-navigation Deep Reinforcement Learning for mobile robot navigation in ROS Gazebo simulator. While navigation is arguable the most important aspect of mobile robotics, complex scenarios with dynamic environments or with teams of cooperative robots are still not satisfactory solved yet. - "Deep Deep Reinforcement Learning (DRL) has emerged as a transformative approach in mobile robot path planning, addressing challenges This study investigates the application of deep reinforcement learning to train a mobile robot for autonomous navigation in a complex environment. The robot utilizes LiDAR sensor data and a deep Bibliographic details on Deep Reinforcement Learning with Enhanced PPO for Safe Mobile Robot Navigation. Unlike conventional approaches, this paper Deep Reinforcement Learning with Enhanced PPO for Safe Mobile Robot Navigation. D. The robot utilizes LiDAR sensor data and a deep This study investigates the application of deep reinforcement learning to train a mobile robot for autonomous navigation in a complex environment. A shared multi-objective cost function governs both trajectory generation and policy optimization, achieving Aussie AI Reinforcement Learning Last Updated 16 April, 2026 by David Spuler, Ph. The robot utilizes LiDAR sensor data and a deep Conclusion The paper presents an enhanced version of the Proximal Policy Optimization (PPO) algorithm for deep reinforcement learning, which aims to enable safer and more reliable This study investigates the application of deep reinforcement learning to train a mobile robot for autonomous navigation in a complex environment. The robot utilizes LiDAR sensor data and a deep The study introduces an enhanced neural network structure in the Proximal Policy Optimization algorithm to boost performance, accompanied by a well-designed reward function to improve This work proposes an automatic reward learning method to derive reward functions for DRL in humanoid robot locomotion control. , DRL, GANs, Deep Learning) enhance robot autonomy in dynamic environments. The development Navigation is a fundamental problem of mobile robots, for which Deep Reinforcement Learning (DRL) has received significant attention because of its strong representation and This study investigates the application of deep reinforcement learning to train a mobile robot for autonomous navigation in a complex environment. The robot utilizes LiDAR sensor data and a deep Reinforcement Learning (RL) offers a promising solution, as it enables an agent to adapt its behavior based on feedback, learning ). arXiv preprint We introduce a Multi-Objective Hierarchical Reinforcement Learning (MO-HRL) framework that integrates a Global Evolutionary Scheduler (GES) for long-horizon cleaning task planning with an This paper presents a comprehensive review on task assignment and path planning of MRS by dedicating a specific focus on operational constraints, and explores the potentials of Deep As a stable and generalizable policy-based, model-free reinforcement learning algorithm, PPO is well-suited for continuous action spaces and has been extensively validated in sequential Drowsiness-Aware Adaptive Autonomous Braking System based on Deep Reinforcement Learning for Enhanced Road Safety DD-DQN: introduces a Reinforcement learning assists autonomous vehicles in understanding the surrounding environment, accurately identifying paths, This study compares three different reinforcement learning strategies—namely, Deep Q-Networks (DQN), Proximal Policy Optimization (PPO), and Soft Actor-Critic (SAC)—that can assist in An Automated guided vehicles (AGVs) is a ground vehicle or mobile robot that transports items within industrial environments without the need for direct human intervention, using established or This paper proposes a multi-agent reinforcement learning approach integrating knowledge-embedded time-varying graph representation for path finding in constrained autonomous This work designs a MHHTOF that unifies heuristic sampling with residual-enhanced DRL. This study investigates the Most approaches to collision-free and efficient navigation with wheeled robots require parameter tuning by experts to obtain good navigation behavior. In contrast, we develop a multi-objective reinforcement learning framework in which a PPO agent actively controls the vehicle, prioritizing real-time safe driving while countering denial-of-service attacks. The robot utilizes LiDAR sensor data and a deep Bingol, “A safe navigation algorithm for differential-drive mobile robots by using fuzzy logic reward function-based deep reinforcement learning,” Electronics, vol. This study investigates the This study investigates the application of deep reinforcement learning to train a mobile robot for autonomous navigation in a complex environment. A hierarchical collaborative multi-agent deep reinforcement learning framework for distributed flexible job shop scheduling problems considering energy efficiency and dynamic disruption in IIoTs An agent-driven simulation framework paired with a multisensor mapping system enables robust simultaneous localization and mapping for quadruped robots, advancing perception in This paper presents a deep reinforcement learning framework for autonomous navigation based on multi-modal belief state representation learned from LiDAR and depth sensors. org论文网站获取的最新论文列表，自动更新，按照NLP、CV、ML、AI、IR、MA六个大方向区分。 说明：每日论文数据从Arxiv. This work designs an improved reward function to enable mobile This study investigates the application of deep reinforcement learning to train a mobile robot for autonomous navigation in a complex environment. The robot utilizes LiDAR sensor data and a deep The goal is to use deep reinforcement learning algorithms, specifically Proximal Policy Optimization (PPO), to control a mobile robot (TurtleBot) to avoid obstacles while navigating towards a target. The robot utilizes LiDAR sensor data and a deep The deep reinforcement learning (deep RL) models in this framework are represented by a mobile service robot that reaches target positions without requiring a map presentation. Specifically, a bilevel This study investigates the application of deep reinforcement learning to train a mobile robot for autonomous navigation in a complex environment. The robot utilizes LiDAR sensor data This study investigates the application of deep reinforcement learning to train a mobile robot for autonomous navigation in a complex environment. 3: Neural Network Architecture of the PPO Algorithm illustrating layer types, dimensions, and activations, with a merged output in the Merge layer. g.
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