Trajectory Tracking Control of Nonholonomic Wheeled Mobile Robots Using Model Predictive Control Subjected to Lyapunov-based Input Constraints

Zhang, Jingjun; Fang, Zuxiang; Zhang, Zhongqi; Gao, Ruizhen; Zhang, Shaobo

doi:10.1007/s12555-019-0814-x

Cited by 29 publications

(15 citation statements)

References 29 publications

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“…In this experiment, the agent was trained for 400 episodes, with a total of 240,000 training steps in the simulations. To validate the path-following capabilities of the proposed method, the MPC algorithm [35] was introduced to present its performance for path-following, and a comparison was made between the two methods. Moreover, the effectiveness of the proposed algorithm is further illustrated by showing the following effects at different training stages.…”

Section: Comparison Of Path-following Between the Proposed Methods An...mentioning

confidence: 99%

Path-Following and Obstacle Avoidance Control of Nonholonomic Wheeled Mobile Robot Based on Deep Reinforcement Learning

et al. 2022

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In this paper, a novel path-following and obstacle avoidance control method is given for nonholonomic wheeled mobile robots (NWMRs), based on deep reinforcement learning. The model for path-following is investigated first, and then applied to the proposed reinforcement learning control strategy. The proposed control method can achieve path-following control through interacting with the environment of the set path. The path-following control method is mainly based on the design of the state and reward function in the training of the reinforcement learning. For extra obstacle avoidance problems in following, the state and reward function is redesigned by utilizing both distance and directional perspective aspects, and a minimum representative value is proposed to deal with the occurrence of multiple obstacles in the path-following environment. Through the reinforcement learning algorithm deep deterministic policy gradient (DDPG), the NWMR can gradually achieve the path it is required to follow and avoid the obstacles in simulation experiments, and the effectiveness of the proposed algorithm is verified.

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Section: Comparison Of Path-following Between the Proposed Methods An...mentioning

confidence: 99%

Path-Following and Obstacle Avoidance Control of Nonholonomic Wheeled Mobile Robot Based on Deep Reinforcement Learning

et al. 2022

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“…To solve this problem, the Robot Operating System (ROS), a mobile robot development system was born, which originated from the Switchyard AI project at Stanford University and is now taken over by Willow Garage [12]. e ROS system is characterized by open source, interfaces in multiple languages, rich functional components to accomplish communication between programs, upper-layer software and underlying hardware, and various visualization tools to facilitate development [13].…”

Section: Research Backgroundmentioning

confidence: 99%

Robot Trajectory Planning Based on the Energy Management Strategy

2022

Mathematical Problems in Engineering

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With the increasing demand for automated production technology in national defense, industry, agriculture, and other fields, the status and role of mobile robots are becoming more and more pivotal. The intelligent technology of robots is becoming more and more demanding in terms of reliability, stability, efficiency, and adaptability, and the autonomous navigation technology of mobile robots is facing new challenges. This paper introduces the basic principle of traditional A∗ algorithm, points out the problems of this algorithm such as cumbersome calculation, large turning angle, and unsmooth derived trajectory planning, and proposes an improved A∗ algorithm. The improved algorithm introduces a two-way alternating search mechanism to improve the efficiency of the search path, and the improvement of the heuristic function solves the problem that the two-way alternating search A∗ algorithm takes more time when there are obstacles perpendicular to the path on the way to the encounter. Simulation experiments in different raster environments prove that compared with the traditional A∗ algorithm, genetic algorithm, and simulated annealing algorithm, the improved A∗ algorithm can significantly improve the search path speed, while overcoming the disadvantages of many path turning angles and large turning angles and is an efficient and feasible algorithm for raster map environments.

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“…Practically, the nonholonomic constraint brings difficulties to the controller design for the stabilization of NMRs, since the system does not satisfy Brockett's theorem [9]. Therefore, it cannot be stabilized to the desired state by utilizing a differentiable or continuous state-feedback controller, which attracts a great many scholars to investigate the tracking control for NMRs [10][11][12][13][14][15]. In Ref.…”

Section: Introductionmentioning

confidence: 99%

Fixed-Time Adaptive Event-Triggered Guaranteed Performance Tracking Control of Nonholonomic Mobile Robots under Asymmetric State Constraints

Chen,

Gu,

Huang

et al. 2024

Mathematics

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A fixed-time adaptive guaranteed performance tracking control is investigated for a category of nonholonomic mobile robots (NMRs) under asymmetric state constraints. For the sake of favorable transient and steady-state properties of the system, a prescribed performance function (PPF) is introduced and a transform function is further constructed. Based on the backstepping technique, an asymmetric barrier Lyapunov function is formulated to ensure the tracking errors converge within a human-specified time. On the foundation of this, the occupation of communication channel is effectively reduced by assigning an event-triggered mechanism (ETM) with relative threshold to the process of controller design. By utilizing the proposed control strategy, the NMR is capable of implementing the enemy dislodging mission while the enemy can always be caught by the NMR and the collision would never be presented. Finally, two simulation experiments are given to verify the effectiveness of the proposed scheme.

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Trajectory Tracking Control of Nonholonomic Wheeled Mobile Robots Using Model Predictive Control Subjected to Lyapunov-based Input Constraints

Cited by 29 publications

References 29 publications

Path-Following and Obstacle Avoidance Control of Nonholonomic Wheeled Mobile Robot Based on Deep Reinforcement Learning

Path-Following and Obstacle Avoidance Control of Nonholonomic Wheeled Mobile Robot Based on Deep Reinforcement Learning

Robot Trajectory Planning Based on the Energy Management Strategy

Fixed-Time Adaptive Event-Triggered Guaranteed Performance Tracking Control of Nonholonomic Mobile Robots under Asymmetric State Constraints

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