A path planning algorithm based on RRT and SARSA (λ) in unknown and complex conditions

Zou, Qijie; Zhang, Yue

doi:10.1109/ccdc49329.2020.9163927

Cited by 9 publications

(13 citation statements)

References 10 publications

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“…For known environments where the terrain and • Advanced driving assistant system (ADAS) + neuroscience for enhanced vehicle control [59] • Path planning application in unknown and complex environments [2] • Autonomous driving applications [21], [33], [37], [60]- [66] • Intelligent Transportation Systems (ITS) [67] • Gesture recognition for human-vehicle interaction [68] • All-terrain vehicle (ATV) with autonomous navigation and teleoperation [69] • Robust localisation of Autonomous Cars [70] • Socially aware robot navigation [34], [71], [72]…”

Section: W H Eel Ed M Obi L E Robotmentioning

confidence: 99%

“…By implementing various AI techniques, mobile robots can collect environmental data, enhance their autonomy, and successfully complete complex tasks. They can improve their performance in various control tasks such as path and motion planning, as well as perception tasks such as object detection, collision avoidance, mapping, and localisation [2], [8]- [37]. An extensive review of numerous simulation platforms reveals the diverse capabilities and features critical to AI-centric research for mobile robots.…”

Section: Introductionmentioning

confidence: 99%

“…However, the deployment of this kind of robot also brings about new and unexpected challenges, especially when operating under extreme conditions such as earthquakes. Despite these challenges, WMRs can be instrumental in scenarios that demand risk reduction or potential life-saving interventions [2]. Their capability to traverse various terrains, from smooth flat surfaces to uneven landscapes, grants them a significant degree of adaptability.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Emerging Trends in Realistic Robotic Simulations: A Comprehensive Systematic Literature Review

Kargar,

Yordanov,

Harvey

et al. 2024

IEEE Access

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Simulation plays a pivotal role in providing safely reproducible scenarios to evaluate the ever-advancing domain of computer science and robotics. It was an essential part of the pandemic when no access to physical spaces was available. The advent of AI-powered platforms in conjunction with enhanced graphics, physics and other sensory engines attracts a new breed of interdisciplinary researchers to enter the robotic field, most notably from computer science, engineering and social sciences. Integration of ROS as a uniform middleware to deploy achieved outcomes in real practice provides an opportunity to move one step closer to the sim-to-real experiences that enables researchers to test ideas beyond the close laboratory spaces. There is a lack of comprehensive evaluation of ROS-enabled simulators, and the integration of advanced AI techniques for realistic scenario replication. This paper addresses this challenge by evaluating ROSenabled simulators in the design and implementation of AI techniques through an in-depth systematic literature review (SLR). This SLR is guided by the research and commercial market demands, employing Population, Intervention, Comparison, Outcome, and Context (PICOC) and Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) frameworks with a major focus on Wheeled Mobile Robots (WMRs). We also highlight the increasing importance of game engines like Unity and Unreal in future of robotic simulations, especially under modelling close to real experiences. By comparing simulation platform features and capabilities, this paper offers guidance to developers and researchers, enabling them to select the most suitable platform for their projects efficiently that contradicts the commonly in use "one size fits all" approach. Finally, based on the thorough insights from the review, we identify and suggest some key future research directions in AI-enhanced realistic robotic simulations.

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Section: W H Eel Ed M Obi L E Robotmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Emerging Trends in Realistic Robotic Simulations: A Comprehensive Systematic Literature Review

Kargar,

Yordanov,

Harvey

et al. 2024

IEEE Access

View full text Add to dashboard Cite

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“…The approach (with application to mobile robots), presented in [ 62 ], uses machine learning techniques to improve the connection between low-level and high-level representations of sensing and planning, respectively. Qijie et al, propose [ 21 ] a planning path algorithm for mobile robots in unknown and uncertain environments based on rapidly exploring random trees and reinforcement learning SARSA (λ). The article [ 63 ] concerns exoskeleton robot applications and presents various data modes as input parameters to models of machine learning to increase the timeliness, motion accuracy, and safety of gait planning.…”

Section: Related Work and Problem Statementmentioning

confidence: 99%

“…The efficiency of executing an intelligent robot’s mission in many cases depends on the properties of the robot’s sensor and control systems in providing the trajectory planning, recognition of the manipulated objects, adaptation of the desired clamping force of the gripper, obstacle avoidance, and so on (drones, unmanned underwater robots, etc.) [ 17 , 18 , 19 , 20 , 21 ].…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Techniques for Increasing Efficiency of the Robot’s Sensor and Control Information Processing

Kondratenko

Atamanyuk

Sidenko

et al. 2022

Sensors

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Real-time systems are widely used in industry, including technological process control systems, industrial automation systems, SCADA systems, testing, and measuring equipment, and robotics. The efficiency of executing an intelligent robot’s mission in many cases depends on the properties of the robot’s sensor and control systems in providing the trajectory planning, recognition of the manipulated objects, adaptation of the desired clamping force of the gripper, obstacle avoidance, and so on. This paper provides an analysis of the approaches and methods for real-time sensor and control information processing with the application of machine learning, as well as successful cases of machine learning application in the synthesis of a robot’s sensor and control systems. Among the robotic systems under investigation are (a) adaptive robots with slip displacement sensors and fuzzy logic implementation for sensor data processing, (b) magnetically controlled mobile robots for moving on inclined and ceiling surfaces with neuro-fuzzy observers and neuro controllers, and (c) robots that are functioning in unknown environments with the prediction of the control system state using statistical learning theory. All obtained results concern the main elements of the two-component robotic system with the mobile robot and adaptive manipulation robot on a fixed base for executing complex missions in non-stationary or uncertain conditions. The design and software implementation stage involves the creation of a structural diagram and description of the selected technologies, training a neural network for recognition and classification of geometric objects, and software implementation of control system components. The Swift programming language is used for the control system design and the CreateML framework is used for creating a neural network. Among the main results are: (a) expanding the capabilities of the intelligent control system by increasing the number of classes for recognition from three (cube, cylinder, and sphere) to five (cube, cylinder, sphere, pyramid, and cone); (b) increasing the validation accuracy (to 100%) for recognition of five different classes using CreateML (YOLOv2 architecture); (c) increasing the training accuracy (to 98.02%) and testing accuracy (to 98.0%) for recognition of five different classes using Torch library (ResNet34 architecture) in less time and number of epochs compared with Create ML (YOLOv2 architecture); (d) increasing the training accuracy (to 99.75%) and testing accuracy (to 99.2%) for recognition of five different classes using Torch library (ResNet34 architecture) and fine-tuning technology; and (e) analyzing the effect of dataset size impact on recognition accuracy with ResNet34 architecture and fine-tuning technology. The results can help to choose efficient (a) design approaches for control robotic devices, (b) machine-learning methods for performing pattern recognition and classification, and (c) computer technologies for designing control systems and simulating robotic devices.

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A Review of Quadruped Robots: Structure, Control, and Autonomous Motion

Fan,

Pei,

Wang

et al. 2024

Advanced Intelligent Systems

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With their unique point‐contact ability with the ground and exceptional adaptability to complex terrains, quadruped robots have become a focal point in the fields of automation and robotic engineering. Significant research progress has been made in aspects such as structural design, motion planning, and balance control of these robots. However, a primary challenge in current research lies in further enhancing the dynamic performance, environmental adaptability, and payload capacity. In this article, research achievements in key technical areas of quadruped robots, encompassing structural design, gait planning, traditional control strategies, intelligent control strategies, and autonomous movement, are comprehensively discussed. The focus of the article is on analyzing trends in intelligence and technological innovation within the aforementioned areas, aiming to provide robust theoretical support and forward‐looking technical guidance for quadruped robots research. Additionally, it offers valuable references for scholars engaged in this field.

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A path planning algorithm based on RRT and SARSA (λ) in unknown and complex conditions

Cited by 9 publications

References 10 publications

Emerging Trends in Realistic Robotic Simulations: A Comprehensive Systematic Literature Review

Emerging Trends in Realistic Robotic Simulations: A Comprehensive Systematic Literature Review

Machine Learning Techniques for Increasing Efficiency of the Robot’s Sensor and Control Information Processing

A Review of Quadruped Robots: Structure, Control, and Autonomous Motion

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