Recent progress, challenges and future prospects of applied deep reinforcement learning : A practical perspective in path planning

Neurocomputing, Год журнала: 2024, Номер 608, С. 128423 - 128423

Опубликована: Авг. 26, 2024

Язык: Английский

---

Biohybrid Behavior-Based Navigation with Obstacle Avoidance for Cyborg Insect in Complex Environment

Soft Robotics, Год журнала: 2025, Номер unknown

Опубликована: Фев. 11, 2025

Autonomous navigation of cyborg insects in complex environments remains a challenging issue. Cyborg insects, which combine biological organisms with electronic components, offer unique approach to tackle such challenges. This study presents biohybrid behavior-based (BIOBBN) system that enables cockroaches navigate autonomously. Two algorithms were developed: reach-avoid for less and adaptive more scenarios. algorithm, especially the second one, leveraged cockroaches' natural behaviors, as wall-following climbing, around over obstacles. Experiments simulated environments, including sand rock-covered surfaces, demonstrate effectiveness BIOBBN enabling reach target locations. The denser scenario required time due increased obstacle avoidance climbing behavior. Overall performance was promising, highlighting potential autonomous navigating environments.

Язык: Английский

---

Optimized TD3 Algorithm for Robust Autonomous Navigation in Crowded and Dynamic Human-Interaction Environments

Results in Engineering, Год журнала: 2024, Номер unknown, С. 102874 - 102874

Опубликована: Сен. 1, 2024

Язык: Английский

---

Semantic-Aware Motion Planning via Building Digital Twin Data

Springer proceedings in advanced robotics, Год журнала: 2025, Номер unknown, С. 84 - 98

Опубликована: Янв. 1, 2025

Язык: Английский

---

Integrated Rover Path Planning and Validation on Real Outdoor Terrain Scenarios Using Satellite Information to Conduct a Real Achievable Trajectory

Electronics, Год журнала: 2025, Номер 14(5), С. 921 - 921

Опубликована: Фев. 26, 2025

The reliable and efficient navigation for mobile robots across challenging outdoor terrains is critical autonomous robotics. Traditional methods planning the path of such often emphasize minimizing travel distance but do not accommodate terrain stability, variability, or energy efficiency. study proposes an integrated approach between satellite-driven geolocation data terrain-specific features that enhance strategies in complex environments. Our method a controller uses search-based algorithms to generate energy-efficient dynamically stable trajectories incorporating surface characteristics environmental from satellite imagery. By integrating our method, proposed framework identifies safer more routes, achieving significant 32% improvement traction compared conventional models path-finding approaches. method’s benefits over traditional approaches include improved safety, extended operational efficiency, ability navigate unpredictable dynamic This makes it ideal planetary exploration, disaster response landslide-prone areas, agricultural automation precision farming rough terrains, search rescue operations earthquake-affected delivery systems into rural unstructured landscapes. It redefines through terrain-aware delivers robust performance

Язык: Английский

---

A plug-and-play fully on-the-job real-time reinforcement learning algorithm for a direct-drive tandem-wing experiment platforms under multiple random operating conditions

Engineering Applications of Artificial Intelligence, Год журнала: 2025, Номер 148, С. 110373 - 110373

Опубликована: Март 11, 2025

Язык: Английский

---

SAC-alpha: dynamic entropy adjustment for enhanced autonomous exploration in unknown environments

International Journal of Intelligent Robotics and Applications, Год журнала: 2025, Номер unknown

Опубликована: Март 23, 2025

Язык: Английский

---

Optical Flow-based Markov Decision Process Control Strategies for Autonomous Mobile Robots in Dynamic Environments

Expert Systems with Applications, Год журнала: 2025, Номер unknown, С. 128087 - 128087

Опубликована: Май 1, 2025

Язык: Английский

---

Optimizing Robotic Arm Control Using Deep Q-Learning and Artificial Neural Networks Through Demonstration-Based Methodologies: A Case Study of Dynamic and Static Conditions

Robotics and Autonomous Systems, Год журнала: 2024, Номер 181, С. 104771 - 104771

Опубликована: Авг. 3, 2024

Язык: Английский

---

A Comprehensive Systematic Review on Machine Learning Application in the 5G-RAN Architecture: Issues, Challenges, and Future Directions

Journal of Network and Computer Applications, Год журнала: 2024, Номер 233, С. 104041 - 104041

Опубликована: Окт. 9, 2024

Язык: Английский

---