Slippage prediction for off-road mobile robots via machine learning regression and proprioceptive sensing

González, Roberto J.; Fiacchini, Mirko; Iagnemma, Karl

doi:10.1016/j.robot.2018.03.013

Cited by 45 publications

(32 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A key indicator for evaluating basketball players is shooting accuracy, which is also the primary issue for most basketball players to improve their competitive ability. If the basketball movement is quantified and the data of basketball movement is calculated from the perspective of mechanics, the athletes can be trained scientifically according to the numerical results (Gonzalez et al, 2018 ). This not only improves the training efficiency of athletes, but also avoids fatigue sports injuries caused by additional training.…”

Section: Methodsmentioning

confidence: 99%

Adoption of Machine Learning Algorithm-Based Intelligent Basketball Training Robot in Athlete Injury Prevention

Tang

2021

Front. Neurorobot.

View full text Add to dashboard Cite

In order to effectively prevent sports injuries caused by collisions in basketball training, realize efficient shooting, and reduce collisions, the machine learning algorithm was applied to intelligent robot for path planning in this study. First of all, combined with the basketball motion trajectory model, the sport recognition in basketball training was analyzed. Second, the mathematical model of the basketball motion trajectory of the shooting motion was established, and the factors affecting the shooting were analyzed. Thirdly, on this basis, the machine learning-based improved Q-Learning algorithm was proposed, the path planning of the moving robot was realized, and the obstacle avoidance behavior was accomplished effectively. In the path planning, the principle of fuzzy controller was applied, and the obstacle ultrasonic signals acquired around the robot were taken as input to effectively avoid obstacles. Finally, the robot was able to approach the target point while avoiding obstacles. The results of simulation experiment show that the obstacle avoidance path obtained by the improved Q-Learning algorithm is flatter, indicating that the algorithm is more suitable for the obstacle avoidance of the robot. Besides, it only takes about 250 s for the robot to find the obstacle avoidance path to the target state for the first time, which is far lower than the 700 s of the previous original algorithm. As a result, the fuzzy controller applied to the basketball robot can effectively avoid the obstacles in the robot movement process, and the motion trajectory curve obtained is relatively smooth. Therefore, the proposed machine learning algorithm has favorable obstacle avoidance effect when it is applied to path planning in basketball training, and can effectively prevent sports injuries in basketball activities.

show abstract

Section: Methodsmentioning

confidence: 99%

Adoption of Machine Learning Algorithm-Based Intelligent Basketball Training Robot in Athlete Injury Prevention

Tang

2021

Front. Neurorobot.

View full text Add to dashboard Cite

show abstract

“…Keser et al use ML techniques for surface roughness recognition based on fiber optic tactile sensor data [ 68 ]. In [ 69 ], ML regression algorithms are trained based on proprioceptive sensing for predicting slippage of individual wheels in off-road mobile robots. Wei et al propose a fusion method with the application of support vector machine and evidence theory for robot target detection and recognition using multi-sensor information processing [ 70 ].…”

Section: Related Work and Problem Statementmentioning

confidence: 99%

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

Kondratenko

Atamanyuk

Sidenko

et al. 2022

Sensors

View full text Add to dashboard Cite

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.

show abstract

“…Also, the selection of the learning algorithms is important. The recent advancement in machine learning tries to obtain the slippage as a regression value rather than classifying it as categories like a low slip, medium slip, and high slip [74]. The challenges involve factors such as mechanical structure and gravitational field effects, especially for planetary rovers.…”

Section: Recent Developments In Terrain Parameter Estimation Of Wheeled Robotsmentioning

confidence: 99%

Recent developments in terrain identification, classification, parameter estimation for the navigation of autonomous robots

Nampoothiri

Sunny

et al. 2021

SN Appl. Sci.

View full text Add to dashboard Cite

The work presents a review on ongoing researches in terrain-related challenges influencing the navigation of Autonomous Robots, specifically Unmanned Ground ones. The paper aims to highlight the recent developments in robot design and advanced computing techniques in terrain identification, classification, parameter estimation, and developing modern control strategies. The objective of our research is to familiarize the gaps and opportunities of the aforementioned areas to the researchers who are passionate to take up research in the field of autonomous robots. The paper brings recent works related to terrain strategies under a single platform focusing on the advancements in planetary rovers, rescue robots, military robots, agricultural robots, etc. Finally, this paper provides a comprehensive analysis of the related works which can bridge the AI techniques and advanced control strategies to improve navigation. The study focuses on various Deep Learning techniques and Fuzzy Logic Systems in detail. The work can be extended to develop new control schemes to improve multiple terrain navigation performance.

show abstract

Slippage prediction for off-road mobile robots via machine learning regression and proprioceptive sensing

Cited by 45 publications

References 18 publications

Adoption of Machine Learning Algorithm-Based Intelligent Basketball Training Robot in Athlete Injury Prevention

Adoption of Machine Learning Algorithm-Based Intelligent Basketball Training Robot in Athlete Injury Prevention

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

Recent developments in terrain identification, classification, parameter estimation for the navigation of autonomous robots

Contact Info

Product

Resources

About