Control of Trajectory Tracking for Mobile Manipulator Robot with Kinematic Limitations and Self-Collision Avoidance

Qiao, Lijun; Luo, Xiwen; Qing-sheng, Luo

doi:10.3390/machines10121232

Cited by 3 publications

(1 citation statement)

References 30 publications

(38 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In terms of operational challenges, safety in warehouse environments is a paramount concern. Qiao et al [13] highlight the need for an optimized differential evolution algorithm based on kinematic limitations and structural complexity constraints to solve the trajectory tracking problem for a mobile manipulator robot. Avoiding the singularity position and/or self-collision phenomena is a challenge when using traditional methods to obtain the speed of the control variable.…”

Section: Existing Challenges and Limitationmentioning

confidence: 99%

Development and analysis of autonomous driving technology for wheeled robots

Chen

2024

ACE

View full text Add to dashboard Cite

A The remarkable progress achieved in the evolution and utilization of autonomous driving technology in wheeled robots is thoroughly analysed in this extensive review article. It begins by stressing the significance of autonomous driving technology in the functioning of wheeled robots. The article proceeds to explore the advancement and application of the core components of autonomous driving technology, namely perception, decision-making, and control, in wheeled robots. Moreover, the comprehensive examination sheds light on the main stumbling blocks and restrictions faced in the current implementation of self-driving technology in wheeled robots, including the need for advanced perception systems and decision-making abilities. Touching on future directions in this domain and offering suggestions for further investigation, the review ultimately emphasizes the capacity of self-driving technology to augment the efficiency and security of wheeled robots, while stressing the inevitable requirement for continuous research and innovation to surmount existing hurdles and actualize this potential in its entirety.

show abstract

Section: Existing Challenges and Limitationmentioning

confidence: 99%

Development and analysis of autonomous driving technology for wheeled robots

Chen

2024

ACE

View full text Add to dashboard Cite

show abstract

Manipulator Trajectory Optimization Using Reinforcement Learning on a Reduced-Order Dynamic Model with Deep Neural Network Compensation

et al. 2023

View full text Add to dashboard Cite

This article reports the construction of an articulated manipulator’s hybrid dynamic model and trajectory planning and optimization of the manipulator using deep reinforcement learning (RL) on the dynamic model. The hybrid model was composed of a physical-based reduced-order dynamic model, linear friction and damping terms, and a deep neural network model to compensate for the nonlinear characteristics of the manipulator. The hybrid model then served as the digital twin of the manipulator for trajectory planning to optimize energy efficiency and operation speed by using RL while taking obstacle avoidance into consideration. The proposed strategy was simulated and experimentally validated. The energy consumption along paths was reduced and the speed was increased so the manipulator could achieve more efficient motion.

show abstract

Research on Trajectory Planning and Tracking Algorithm of Crawler Paver

Zhan,

Li,

Wang

et al. 2024

Machines

View full text Add to dashboard Cite

The implementation of unmanned intelligent construction on the concrete surfaces of an airport effectively improves construction accuracy and reduces personnel investment. On the basis of three known common tracked vehicle dynamics models, reference trajectory planning and trajectory tracking controller algorithms need to be designed. In this paper, based on the driving characteristics of the tracked vehicle and the requirements of the stepping trajectory, a quartic polynomial trajectory planning algorithm was selected with the stability of the curve as a whole and the end point as the optimization goal, combining the tracked vehicle dynamics model, collision constraints, start–stop constraints and other boundary conditions. The objective function of trajectory planning was designed to effectively plan the reference trajectory of the tracked vehicle’s step-by-step travel. In order to realize accurate trajectory tracking control, a nonlinear model predictive controller with transverse-longitudinal integrated control was designed. To improve the real-time performance of the controller, a linear model predictive controller with horizontal and longitudinal decoupling was designed. MATLAB 2023A and CoppeliaSim V4.5.1 were used to co-simulate the two controller models. The experimental results show that the advantages and disadvantages of the tracked vehicle dynamics model and controller design are verified.

show abstract

Control of Trajectory Tracking for Mobile Manipulator Robot with Kinematic Limitations and Self-Collision Avoidance

Cited by 3 publications

References 30 publications

Development and analysis of autonomous driving technology for wheeled robots

Development and analysis of autonomous driving technology for wheeled robots

Manipulator Trajectory Optimization Using Reinforcement Learning on a Reduced-Order Dynamic Model with Deep Neural Network Compensation

Research on Trajectory Planning and Tracking Algorithm of Crawler Paver

Contact Info

Product

Resources

About