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3D robotic navigation using a vision-based deep reinforcement learning model

  • Published:2021-10 Issue: Volume:110 Page:107602
  • ISSN:1568-4946
  • Container-title:Applied Soft Computing
  • language:en
  • Short-container-title:Applied Soft Computing

Author:

Zieliński P.,Markowska-Kaczmar U.

Publisher

Elsevier BV

Subject

Software

Reference43 articles.

1. A technique for autonomous underwater vehicle route planning;Warren,1990

2. Principles of Robot Motion Theory, Algorithms, and Implementations;Choset,2005

3. Path planning algorithm using the particle swarm optimization and the improved dijkstra algorithm;Kang,2008

4. Time-efficient A* algorithm for robot path planning;Guruji;Proc. Technol.,2016

5. Autonomous robot navigation using adaptive potential fields;Cosío;Math. Comput. Modelling,2004

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1. Underwater Image Enhancement via Adaptive Color Correction and Stationary Wavelet Detail Enhancement;IEEE Access;2024

2. Fast and Robust Training and Deployment of Deep Reinforcement Learning Based Navigation Policy;2023 IEEE International Conference on Unmanned Systems (ICUS);2023-10-13

3. SLAYO-RL: A Target-Driven Deep Reinforcement Learning Approach with SLAM and YoLo for an Enhanced Autonomous Agent;2023 Latin American Robotics Symposium (LARS), 2023 Brazilian Symposium on Robotics (SBR), and 2023 Workshop on Robotics in Education (WRE);2023-10-09

4. Designing INS/GNSS integrated navigation systems by using IPO algorithms;Neural Computing and Applications;2023-04-12

5. Reinforcement Learning-Based Guidance of Autonomous Vehicles;2023 24th International Symposium on Quality Electronic Design (ISQED);2023-04-05
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