Human and environmental feature-driven neural network for path-constrained robot navigation using deep reinforcement learning

Engineering Science and Technology an International Journal, Год журнала: 2025, Номер 64, С. 101993 - 101993

Опубликована: Фев. 25, 2025

Язык: Английский

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Informed sampling space driven robot informative path planning

Robotics and Autonomous Systems, Год журнала: 2024, Номер 175, С. 104656 - 104656

Опубликована: Фев. 3, 2024

Язык: Английский

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Memory-driven deep-reinforcement learning for autonomous robot navigation in partially observable environments

Engineering Science and Technology an International Journal, Год журнала: 2025, Номер 62, С. 101942 - 101942

Опубликована: Янв. 24, 2025

Язык: Английский

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Integrating Radar-Based Obstacle Detection with Deep Reinforcement Learning for Robust Autonomous Navigation

Applied Sciences, Год журнала: 2024, Номер 15(1), С. 295 - 295

Опубликована: Дек. 31, 2024

This study presents an approach to autonomous navigation for wheeled robots, combining radar-based dynamic obstacle detection with a BiGRU-based deep reinforcement learning (DRL) framework. Using filtering and tracking algorithms, the proposed system leverages radar sensors cluster object points track obstacles, enhancing precision by reducing noise fluctuations. A BiGRU-enabled DRL model is introduced, allowing robot process sequential environmental data make informed decisions in unpredictable environments, achieving collision-free paths reaching goal. Simulation experimental results validate method’s efficiency adaptability, highlighting its potential real-world applications scenarios.

Язык: Английский

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Autonomous navigation and visual navigation in robot mission execution

Image and Vision Computing, Год журнала: 2025, Номер unknown, С. 105516 - 105516

Опубликована: Март 1, 2025

Язык: Английский

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Reinforcement Learning Algorithm for Two-Leg Robot with DDPG and TD3

Lecture notes in networks and systems, Год журнала: 2025, Номер unknown, С. 10 - 23

Опубликована: Янв. 1, 2025

Язык: Английский

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Transformable Gaussian Reward Function for Socially Aware Navigation Using Deep Reinforcement Learning

Sensors, Год журнала: 2024, Номер 24(14), С. 4540 - 4540

Опубликована: Июль 13, 2024

Robot navigation has transitioned from avoiding static obstacles to adopting socially aware strategies for coexisting with humans. Consequently, in dynamic, human-centric environments gained prominence the field of robotics. One methods navigation, reinforcement learning technique, fostered its advancement. However, defining appropriate reward functions, particularly congested environments, holds a significant challenge. These crucial guiding robot actions, necessitate intricate human-crafted design due their complex nature and inability be set automatically. The multitude manually designed functions contains issues such as hyperparameter redundancy, imbalance, inadequate representation unique object characteristics. To address these challenges, we introduce transformable Gaussian function (TGRF). TGRF possesses two main features. First, it reduces burden tuning by utilizing small number hyperparameters that independently. Second, enables application various through transformability. exhibits high performance accelerated rates within deep (DRL) framework. We also validated simulations experiments.

Язык: Английский

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Enhancing Autonomous Robot Navigation Based on Deep Reinforcement Learning: Comparative Analysis of Reward Functions in Diverse Environments

Опубликована: Окт. 17, 2023

Autonomous robot navigation in complex environments presents a significant challenge due to efficient decision-making for reaching goals and avoiding obstacles. This paper addresses this issue through the use of deep reinforcement learning techniques comprehensive analysis reward functions their impact on autonomous navigation. The study emphasizes importance selecting most effective achieve maximum performance variety scenarios. Moreover, we propose new mechanism that enables avoid collisions when objects move faster than robot, resulting halting its motion allow object pass before resuming course. effectiveness these is validated simulations, providing valuable insights into robustness Further details simulations can be found following link: https://youtu.be/pPQDc25vj1U

Язык: Английский

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Privacy-preserving bipartite output consensus for continuous-time heterogeneous multi-agent systems via event-triggered impulsive control

Complex & Intelligent Systems, Год журнала: 2024, Номер 10(4), С. 5127 - 5137

Опубликована: Апрель 17, 2024

Abstract This paper studies the problem of differentially private bipartite output consensus in continuous-time heterogeneous multi-agent systems (MASs) characterized by antagonistic interactions. A novel hybrid privacy-preserving event-triggered impulsive protocol is introduced to protect agent’s initial information from disclosure, which involves a discrete-time transmission based on an event-triggering mechanism. Using stochastic Lyapunov method, sufficient conditions have been obtained achieve mean square with guaranteed level privacy. Furthermore, differential privacy competitive agent pairs exclusively secured proposed control scheme injecting Laplace noise. The also effectively prevents Zeno behavior imposing lower bound for intervals under all conditions. simulation example provided validate effectiveness theoretical result.

Язык: Английский

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RL-Based Sim2Real Enhancements for Autonomous Beach-Cleaning Agents

Applied Sciences, Год журнала: 2024, Номер 14(11), С. 4602 - 4602

Опубликована: Май 27, 2024

This paper explores the application of Deep Reinforcement Learning (DRL) and Sim2Real strategies to enhance autonomy beach-cleaning robots. Experiments demonstrate that DRL agents, initially refined in simulations, effectively transfer their navigation skills real-world scenarios, achieving precise efficient operation complex natural environments. method provides a scalable effective solution for beach conservation, establishing significant precedent use autonomous robots environmental management. The key advancements include ability adhere predefined routes dynamically avoid obstacles. Additionally, newly developed platform validates strategy, proving its capability bridge gap between simulated training practical application, thus offering robust methodology addressing real-life challenges.

Язык: Английский

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