On-Line Learning and Updating Unmanned Tracked Vehicle Dynamics

Strawa, Natalia; Ignatyev, Dmitry; Zolotas, Argyrios; Tsourdos, Antonios

doi:10.3390/electronics10020187

Cited by 8 publications

(3 citation statements)

References 41 publications

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“…According to the terramechanical models [19][20][21][22], the skid and slip rates of the robot track can be expressed as follows:…”

Section: Analysis Of the Skid And Slip Ratesmentioning

confidence: 99%

Simulation and Validation of a Steering Control Strategy for Tracked Robots

Wang,

Zhang,

et al. 2023

Applied Sciences

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Tracked inspection robots have demonstrated their versatility in a wide range of applications. However, challenges arising from issues such as skidding and slipping have posed obstacles to achieving precise and efficient trajectory control. This paper introduces a method to determine the steering parameters of robot based on the surrounding obstacles and road information. The primary objective is to enhance the steering efficiency of tracked robots. The corresponding relationship between the track speed, driving force and track steering radius of the tracked robot is obtained. Considering the influence of track skid and slip, relationship models about the steering radius and traveling speed of the robot are established. The minimum and maximum steering radii in the obstacle avoidance process are analyzed, and a mathematical model of the relationship between the steering angle of the robot and the distance between the side obstacles is established. The trajectory deviation model of the tracked robot is established, and a principle analysis of the LiDAR ranging is completed. This lays the foundation for a steering measurement and control system for tracked robots. ADAMS(2020) software is used to establish the multi-body dynamics model of the tracked robot, and three different obstacle-avoiding steering control strategies are designed for the robot in a simulated environment with space obstacles. The simulation experiment demonstrates that the robot achieves more efficient obstacle avoidance steering through the use of differential steering, leading to a decrease in both track skid and slip rates. Through the simulation experiment, it can be seen that the robot uses differential steering to complete the obstacle avoidance steering movement more efficiently, and the track skid and slip rates are smaller. The simulation results are used to complete the steering control experiment of the tracked robot on different road surfaces. The results show that by adjusting the track driving parameters, the robot can effectively complete the obstacle-avoiding steering movement by using the differential steering control strategy, which verifies the accuracy of the steering control strategy.

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“…According to the terramechanical models [19][20][21][22], the skid and slip rates of the robot track can be expressed as follows:…”

Section: Analysis Of the Skid And Slip Ratesmentioning

confidence: 99%

Simulation and Validation of a Steering Control Strategy for Tracked Robots

Wang,

Zhang,

et al. 2023

Applied Sciences

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show abstract

“…The driving forces of the track interfere with each other while controlling tracked vehicles using driving force feedback. Therefore, the driving force distribution that decouples the driving forces must be considered [21,22]. During the steering process of the robot, there are many types of force on the track, and the different steering modes bear different forces.…”

Section: Traction Force Analysismentioning

confidence: 99%

Research on the Obstacle-Avoidance Steering Control Strategy of Tracked Inspection Robots

Wang

et al. 2022

Applied Sciences

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Tracked inspection robots possess prominent advantages in dealing with severe environment rescue, safety inspection, and other important tasks, and have been used widely. However, tracked robots are affected by skidding and slipping, so it is difficult to achieve accurate control. For example, the control parameters of a tracked robot are the same during driving, but the pressure, shear force and steering resistance of the robot on the road surface are different, which affects the steering characteristics of the robot on complex terrain. Based on analysis of the structural parameters and steering radius of the robot, the traction force and resistance torque models of the tracked robot were established, and the plane dynamics of the robot’s steering were analyzed and solved. The corresponding relationships between the road parameters, relative steering radius, and lateral relative offset of the robot on three typical roads were obtained. Mathematical models of the robot’s track speed and relative steering radius with and without skid and slip were established. Through simulation analysis of Matlab software, the corresponding relationship between the relative steering radius of the robot and the velocity difference of the two tracks were obtained. Taking angular obstacles as an example, three obstacle-avoidance steering control strategies, once turning in situ center, twice turning in situ center, and large-radius steering were developed. The tracked robot and obstacle multi-body dynamic simulation models were constructed using ADAMS simulation software. The simulation results show that all three methods can complete the steering tasks according to the requirements; however, under the influence of skid and slip, the trajectory of the robot deviates from the ideal trajectory, which has a great impact on large-radius steering, even though the large-radius obstacle-avoidance steering control strategy has the advantages of a smooth trajectory, fast steering speed, and high efficiency. The obstacle-avoidance steering experiments were completed by the robot prototype, which verifies the rationality of robot steering theory, which could provide the corresponding theoretical basis for autonomous obstacle-avoidance navigation control of a tracked robot.

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“…'On-Line Learning and Updating Unmanned Tracked Vehicle Dynamics' by Strawa et al [5] proposed a method by which to estimate vehicle model parameters using a compound identification scheme utilizing an exponential forgetting recursive least square, generalized Newton-Raphson (NR), and Unscented Kalman Filter methods. The proposed identification scheme facilitates adaptive capability for the control system, improves tracking performance, and contributes to an adaptive path and trajectory planning framework, which is essential for future autonomous ground vehicle missions and traversability.…”

mentioning

confidence: 99%