Reinforcement Learning Path Planning Method with Error Estimation

Zhang, Feihu; Wang, Can; Cheng, Chensheng; Yang, Dianyu; Pan, Guang

doi:10.3390/en15010247

Cited by 13 publications

(5 citation statements)

References 24 publications

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“…While previous studies have focused on improving navigation accuracy, planning strategies are often neglected. New planning strategies have been designed to facilitate error reduction (Bing et al, 2022;Zhang et al, 2022) but have not been given much attention. Though acoustic-based navigation has achieved significant accuracy improvements, limitations in operating conditions could still hinder optimal outcomes.…”

Section: Underwater Navigation and Acoustic Calibrationmentioning

confidence: 99%

AUV planning and calibration method considering concealment in uncertain environments

et al. 2023

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IntroductionAutonomous underwater vehicles (AUVs) are required to thoroughly scan designated areas during underwater missions. They typically follow a zig-zag trajectory to achieve full coverage. However, effective coverage can be challenging in complex environments due to the accumulation and drift of navigation errors. Possible solutions include surfacing for satellite positioning or underwater acoustic positioning using transponders on other vehicles. Nevertheless, surfacing or active acoustics can compromise stealth during reconnaissance missions in hostile areas by revealing the vehicle’s location.MethodsWe propose calibration and planning strategies based on error models and acoustic positioning to address this challenge. Acoustic markers are deployed via surface ships to minimize navigation errors while maintaining stealth. And a new path planning method using a traceless Kalman filter and acoustic localization is proposed to achieve full-area coverage of AUVs. By analyzing the statistics of accumulated sensor errors, we optimize the positions of acoustic markers to communicate with AUVs and achieve better coverage. AUV trajectory concealment is achieved during detection by randomizing the USV navigation trajectory and irregularizing the locations of acoustic marker.ResultsThe proposed method enables the cumulative determination of the absolute position of a target with low localization error in a side-scan sonar-based search task. Simulations based on large-scale maps demonstrate the effectiveness and robustness of the proposed algorithm.DiscussionSolving the problem of accumulating underwater localization errors based on inertial navigation by error modeling and acoustic calibration is a typical way. In this paper, we have implemented a method to solve the localization error in a search scenario where stealth is considered.

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Section: Underwater Navigation and Acoustic Calibrationmentioning

confidence: 99%

AUV planning and calibration method considering concealment in uncertain environments

et al. 2023

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“…The future work will deal with dynamic obstacles. An improved path planning algorithm based on Q-Learning is proposed in [51]. Deep reinforcement learning (DRL) for autonomous self-learning robot navigation in unknown and indoor environments without using a map or planner is designed [52].…”

Section: Neural Network Algorithmsmentioning

confidence: 99%

Thorough Review Analysis of Safe Control of Autonomous Vehicles: Path Planning and Navigation Techniques

2022

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Mobile robot path planning has passed through multiple phases of development and took up several challenges. Up to now and with the new technology in hands, it becomes less complicated to conduct path planning for mobile robots and avoid both static and dynamic obstacles, so that collision-free navigation is ensured. Thorough state of the art review analysis with critical scrutiny of both safe and optimal paths for autonomous vehicles is addressed in this study. Emphasis is given to several developed techniques based using sampling algorithms, node-based optimal algorithms, mathematic model-based algorithms, bio-inspired algorithms, which includes neural network algorithms, and then multi-fusion-based algorithms, which combine different methods to overcome the drawbacks of each. All of these approaches consider different conditions and they are used for multiple domains.

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“…Depending on the different application scenarios of drones, path-planning algorithms for drones can be divided into static global algorithms and dynamic local algorithms. Common global algorithms include graph-based search algorithms such as Dijkstra's algorithm [4] and A* algorithm [5][6]; sampling-based algorithms such as Rapidly-exploring Random Trees (RRT) [7] and Probabilistic Roadmaps (PRM) [8]; intelligent algorithms such as reinforcement learning [9], deep reinforcement learning [10], etc. Static global path planning involves finding an optimal path for a drone in a known environment, with the A* algorithm widely applied due to its advantages in speed and optimal path generation [11].…”

Section: Introductionmentioning

confidence: 99%

A new method for unmanned aerial vehicle path planning in complex environments

He,

Hou,

Wang

2024

Preprint

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In response to challenges in unmanned aerial vehicle (UAV) path planning, such as low search efficiency, non-smooth paths, and the inability to adapt to unknown environments, this paper proposes a novel UAV path planning method integrating a dual-layer optimization of A* and the dynamic window approach(DWA). Firstly, neighbor node pruning rules are defined to optimize the node expansion process of the A* algorithm. The heuristic function of the A* algorithm is dynamically adjusted based on obstacle coverage, and key path nodes are extracted using the Bresenham algorithm. Secondly, a deviation sub-function is introduced to enhance local path planning coherence, and an adaptive weighting method for the evaluation function is designed. Lastly, the optimized key nodes are utilized as sub-goals for local path planning, integrating the dynamic window approach to enhance real-time obstacle avoidance and adaptability in unknown environments. Simulation results demonstrate that the proposed method significantly improves the efficiency, smoothness, and obstacle avoidance performance of mobile robot path planning, meeting the requirements of complex planning tasks more effectively.

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Reinforcement Learning Path Planning Method with Error Estimation

Cited by 13 publications

References 24 publications

AUV planning and calibration method considering concealment in uncertain environments

AUV planning and calibration method considering concealment in uncertain environments

Thorough Review Analysis of Safe Control of Autonomous Vehicles: Path Planning and Navigation Techniques

A new method for unmanned aerial vehicle path planning in complex environments

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