Navigation system with SLAM-based trajectory topological map and reinforcement learning-based local planner

Xue, Wuyang; Liu, Peilin; Miao, Ruihang; Gong, Zheng; Wen, Fei; Ying, Rendong

doi:10.1080/01691864.2021.1938671

Cited by 8 publications

(2 citation statements)

References 39 publications

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“…Map construction and localization are usually solved as a SLAM problem (Xue 2021). The map is constructed by calculating the distance of the robot's poses from the environmental data obtained from the sensor scans to the obstacles.…”

Section: Related Workmentioning

confidence: 99%

Autonomous navigation system for exhibition hall service robots via laser SLAM

Cai

Zhu

et al. 2022

Preprint

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As an important tool for guiding and introducing visitors to the exhibition hall, the service robot needs to confront the complex environment existed and the constantly moving visitors. It is needed to have the ability to enable accurate positioning and navigation as well as dynamic obstacle avoidance. In recent years, simultaneous localization and mapping technology has been widely used to address localization and navigation issues in unknown indoor environments. Combining the service robot’s inertial measurement unit with other sensors to map the exhibition hall environment by the Cartographer SLAM algorithm, environmental information is collected by two-dimensional laser lidar. The path planning and dynamic obstacle are also achieved by the Cartographer simultaneous localization and mapping algorithm. The \({A^*}\) algorithm is selected for global path planning and the dynamic window approach algorithm for local path planning. The environment map construction, path planning and dynamic obstacle avoidance functions are verified by simulation and experiments in real environment. The average position deviation and standard deviation of the robot are less than 7cm and 3cm, respectively, when the robot is set to move at 0.5m/s. The average heading deviation is less than \({9^ \circ }\)and standard deviation is less than \({3^ \circ }\). The robot's positioning and navigation can well meet the working requirements of the pavilion service robot.

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Section: Related Workmentioning

confidence: 99%

Autonomous navigation system for exhibition hall service robots via laser SLAM

Cai

Zhu

et al. 2022

Preprint

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“…RL provides a framework to solve complex problems that are challenging to address with conventional methods. Recent advancements in RL have extended its applicability in various fields, such as gaming [1][2][3], recommendation systems [4], robotics [5][6][7][8], computer systems [9,10], and autonomous navigation [11][12][13]. Additionally, a number of advanced RL algorithms have outperformed human-level performance in diverse gaming environments [3,14], revealing the effectiveness of RL-based learning algorithms in addressing complex problems.…”

Section: Introductionmentioning

confidence: 99%

An Advisor-Based Architecture for a Sample-Efficient Training of Autonomous Navigation Agents with Reinforcement Learning

Wijesinghe,

Tissera,

Vithanage

et al. 2023

Robotics

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Recent advancements in artificial intelligence have enabled reinforcement learning (RL) agents to exceed human-level performance in various gaming tasks. However, despite the state-of-the-art performance demonstrated by model-free RL algorithms, they suffer from high sample complexity. Hence, it is uncommon to find their applications in robotics, autonomous navigation, and self-driving, as gathering many samples is impractical in real-world hardware systems. Therefore, developing sample-efficient learning algorithms for RL agents is crucial in deploying them in real-world tasks without sacrificing performance. This paper presents an advisor-based learning algorithm, incorporating prior knowledge into the training by modifying the deep deterministic policy gradient algorithm to reduce the sample complexity. Also, we propose an effective method of employing an advisor in data collection to train autonomous navigation agents to maneuver physical platforms, minimizing the risk of collision. We analyze the performance of our methods with the support of simulation and physical experimental setups. Experiments reveal that incorporating an advisor into the training phase significantly reduces the sample complexity without compromising the agent’s performance compared to various benchmark approaches. Also, they show that the advisor’s constant involvement in the data collection process diminishes the agent’s performance, while the limited involvement makes training more effective.

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A Comprehensive Review of Mobile Robot Navigation Using Deep Reinforcement Learning Algorithms in Crowded Environments

Le,

Saeedvand,

Hsu

2024

J Intell Robot Syst

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Navigation is a crucial challenge for mobile robots. Currently, deep reinforcement learning has attracted considerable attention and has witnessed substantial development owing to its robust performance and learning capabilities in real-world scenarios. Scientists leverage the advantages of deep neural networks, such as long short-term memory, recurrent neural networks, and convolutional neural networks, to integrate them into mobile robot navigation based on deep reinforcement learning. This integration aims to enhance the robot's motion control performance in both static and dynamic environments. This paper illustrates a comprehensive survey of deep reinforcement learning methods applied to mobile robot navigation systems in crowded environments, exploring various navigation frameworks based on deep reinforcement learning and their benefits over traditional simultaneous localization and mapping-based frameworks. Subsequently, we comprehensively compare and analyze the relationships and differences among three types of navigation: autonomous-based navigation, navigation based on simultaneous localization and mapping, and planning-based navigation. Moreover, the crowded environment includes static, dynamic, and a combination of obstacles in different typical application scenarios. Finally, we offer insights into the evolution of navigation based on deep reinforcement learning, addressing the problems and providing potential solutions associated with this emerging field.

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Navigation system with SLAM-based trajectory topological map and reinforcement learning-based local planner

Cited by 8 publications

References 39 publications

Autonomous navigation system for exhibition hall service robots via laser SLAM

Autonomous navigation system for exhibition hall service robots via laser SLAM

An Advisor-Based Architecture for a Sample-Efficient Training of Autonomous Navigation Agents with Reinforcement Learning

A Comprehensive Review of Mobile Robot Navigation Using Deep Reinforcement Learning Algorithms in Crowded Environments

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