Motion Control and Trajectory Planning for Obstacle Avoidance of the Mobile Parallel Robot Driven by Three Tracked Vehicles

Shentu, Shuzhan; Xie, Fugui; Liu, Xin-Jun; Gong, Zhao

doi:10.1017/s0263574720000880

Cited by 15 publications

(7 citation statements)

References 32 publications

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“…e literature [10] built a palletizing robot motion control system based on ARM and Linux systems. e literature [11] designed a palletizing robot motion control system with ARM microprocessor as the core, which can achieve high precision computing in operation, high efficiency of system motion control, and good stability performance.…”

Section: Introductionmentioning

confidence: 99%

Application of Fuzzy PID Position Control Algorithm in Motion Control System Design of Palletizing Robot

Xiao

Lin

et al. 2022

Security and Communication Networks

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In view of the low efficiency in traditional palletizing robot problem of poor control precision, this paper introduces fuzzy PID position control algorithm, based on the actual operation situation of palletizing robot; determined as palletizing robot FPGA hardware platform, hardware platform based on this fuzzy PID position control algorithm is applied to implement palletizing robot motion control system design. The simulation model of fuzzy PID motion control was established by MATLAB software for testing to determine that the fuzzy PID position control algorithm reflects the time quickly in the motion control of palletizing robot, and the actual overshooting is small, which is more suitable for the motion control algorithm of palletizing robot. Under this condition, the modular method is adopted to complete the system application design on the FPGA hardware platform.

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Section: Introductionmentioning

confidence: 99%

Application of Fuzzy PID Position Control Algorithm in Motion Control System Design of Palletizing Robot

Xiao

Lin

et al. 2022

Security and Communication Networks

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“…Using the construction method of the Lyapunov function proposed in Ref. [27] and given input velocity v > 0 , the motion controller can be written as…”

Section: Motion Controller Designmentioning

confidence: 99%

Hybrid Navigation System Based Autonomous Positioning and Path Planning for Mobile Robots

Shentu

Gong

Liu

et al. 2022

Chin. J. Mech. Eng.

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Positioning and navigation technology is a new trend of research in mobile robot area. Existing researches focus on the indoor industrial problems, while many application fields are in the outdoor environment, which put forward higher requirements for sensor selection and navigation scheme. In this paper, a complete hybrid navigation system for a class of mobile robots with load tasks and docking tasks is presented. The work can realize large-range autonomous positioning and path planning for mobile robots in unstructured scenarios. The autonomous positioning is achieved by adopting suitable guidance methods to meet different application requirements and accuracy requirements in conditions of different distances. Based on the Bezier curve, a path planning scheme is proposed and a motion controller is designed to make the mobile robot follow the target path. The Kalman filter is established to process the guidance signals and control outputs of the motion controller. Finally, the autonomous positioning and docking experiment are carried out. The results of the research verify the effectiveness of the hybrid navigation, which can be used in autonomous warehousing logistics and multi-mobile robot system.

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“…Tracked mobile robot with a unique passive suspension that is built utilizing a bio-inspired animal-limb-like strategy to ensure both loading capacity and vibration isolation performance was proposed in [22]. A mobile parallel robot (MPR), driven by three tracked vehicles was designed in [23]. A genetic algorithm (GA) was applied to overcome the complex coupling of the robot.…”

Section: Tracked Mobile Robotsmentioning

confidence: 99%

Construction and benchmark of an autonomous tracked mobile robot system

Ārents¹,

Ahluwalia²,

Oraby³

et al. 2022

Robot. syst., appl.

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Robots require a certain set of skills to perceive and analyse the environment and act accordingly. For tracked mobile robots getting good odometry data from sensory information is a challenging key prerequisite to perform in an unstructured dynamic environment, thus an essential issue in the tracked mobile robotics domain. In this article, we construct a ROS-based tracked mobile robot system taking the Jaguar V4 mobile robot as the base platform. On which several visual odometry solutions based on different cameras and methods (Intel RealSense T265, Zed camera, RTAB-Map RGBD) are integrated and benchmark comparison is performed. Analysis of new challenges faced by different methods while applied on a tracked vehicle as well as recommendations and conclusions are presented. Intel RealSense T265 solution proved to perform well in uncertain conditions which involves bounded vibrations and low lighting conditions with low latency, which result in good map generation. Further evaluations with a path planning algorithm and Intel RealSense T265 were conducted to test the effect of the robot's motion profiles on odometry data accuracy.

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Motion Control and Trajectory Planning for Obstacle Avoidance of the Mobile Parallel Robot Driven by Three Tracked Vehicles

Cited by 15 publications

References 32 publications

Application of Fuzzy PID Position Control Algorithm in Motion Control System Design of Palletizing Robot

Application of Fuzzy PID Position Control Algorithm in Motion Control System Design of Palletizing Robot

Hybrid Navigation System Based Autonomous Positioning and Path Planning for Mobile Robots

Construction and benchmark of an autonomous tracked mobile robot system

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