Path Planning and Obstacle Avoidance Algorithm of an Autonomous Traveling Robot Using the RRT and the SPP Path Smoothing

Park, Yeong-Sang; Lee, Young‐Sam

doi:10.5302/j.icros.2016.15.0201

Cited by 3 publications

(2 citation statements)

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“…Robots require efficient path planning and navigation abilities to choose the best path to reach their destination [13,14]. Path planning algorithms such as A*, D*, and RRT are used to identify safe and feasible paths [15][16][17][18][19][20][21][22]. Simultaneous Localization and Mapping (SLAM) technology plays a vital role in robotic autonomy by allowing robots to create maps of their surroundings and determine their position on that map at the same time.…”

Section: Related Workmentioning

confidence: 99%

Field Robot Self-Exploration Based on Deep Reinforcement Learning and Safety Control Mechanism

Kang,

Zhang,

et al. 2024

Preprint

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This paper aims to address the issues that arise when using deep reinforcement learning algorithms for reactive robotic navigation in farmland environments. These challenges include complex network models, a large number of parameters, significant computational requirements, and the possibility of being vulnerable to local optimal solutions. Moreover, the complexity of the farmland environment poses a challenge to the existing indoor reactive navigation methods, which struggle to meet the requirements of tasks such as crop protection and human cooperation.To address these issues, the paper proposes the Field Robot Autonomous Navigation System (FRANS), which utilizes Deep Reinforcement Learning (DRL) to enable farmland robots to autonomously explore unknown environments. The system consists of two modules: Global Navigation (GN) and Local Navigation (LN). The GN module establishes goal points and implements a secure control mechanism to manage the robot's actions. This approach helps to address problems like locally optimal solutions. The LN module uses a lightweight DRL framework to develop a locally navigated motion strategy that guides the robot toward waypoints. FRANS does not require any prior knowledge and is capable of adjusting the navigation strategy in real time based on the current environment. The map is generated in real time as the robot moves through the environment. Experimental results demonstrate that FRANS outperforms similar approaches.

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Section: Related Workmentioning

confidence: 99%

Field Robot Self-Exploration Based on Deep Reinforcement Learning and Safety Control Mechanism

Kang,

Zhang,

et al. 2024

Preprint

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“…Robots necessitate sophisticated path planning and navigation capabilities to select optimal routes to their destinations [13,14]. Algorithms such as A*, D*, and RRT are integral in delineating safe and feasible pathways through environments [15][16][17][18][19][20][21][22]. Concurrently, Simultaneous Localization and Mapping (SLAM) technology is crucial for enhancing robotic autonomy.…”

Section: Related Workmentioning

confidence: 99%

Field Robot Self-Exploration Based on Deep Reinforcement Learning and Obstacle Avoidance Strategy

Kang,

Zhang,

et al. 2024

Preprint

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This paper addresses the challenges posed by deep reinforcement learning (DRL) algorithms when deployed for reactive robotic navigation in agricultural settings. These challenges include the complexity of network models, extensive parameterization, substantial computational demands, and susceptibility to local optima. The intricacies of farmland environments further complicate the application of conventional indoor navigation methods, which falter in tasks such as crop monitoring and interaction with humans. To overcome these obstacles, we introduce the Field Robot Autonomous Navigation System (FRANS), which employs DRL to facilitate the autonomous exploration of unknown farmlands by robots. FRANS is composed of two primary modules: Global Navigation (GN) and Local Navigation (LN). The GN module sets destination points and implements a robust control mechanism to navigate the robot, effectively mitigating issues associated with local optima. Meanwhile, the LN module leverages a streamlined DRL framework to formulate a motion strategy that directs the robot towards these waypoints. Crucially, FRANS operates without prior environmental knowledge and dynamically adapts its navigation strategy in response to real-time environmental changes. The system continuously updates the map as the robot traverses the terrain. Experimental evaluations show that FRANS surpasses competing methodologies in performance.

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Research and Application of Obstacle Avoidance Path Planning Based on Hermite Curve Interpolation

Chen,

Li,

Zheng

et al. 2023

2023 5th International Conference on Robotics, Intelligent Control and Artificial Intelligence (RICAI)

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Path Planning and Obstacle Avoidance Algorithm of an Autonomous Traveling Robot Using the RRT and the SPP Path Smoothing

Cited by 3 publications

References 0 publications

Field Robot Self-Exploration Based on Deep Reinforcement Learning and Safety Control Mechanism

Field Robot Self-Exploration Based on Deep Reinforcement Learning and Safety Control Mechanism

Field Robot Self-Exploration Based on Deep Reinforcement Learning and Obstacle Avoidance Strategy

Research and Application of Obstacle Avoidance Path Planning Based on Hermite Curve Interpolation

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