Direct dynamic-simulation approach to trajectory optimization

Hur, Sung Wook; Lee, Seong Han; Nam, Yong Hyeon; Kim, Chang-Joo

doi:10.1016/j.cja.2021.01.019

Cited by 7 publications

(3 citation statements)

References 34 publications

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“…Robots with high motion accuracy can improve efficiency and avoid wasting resources when moving with high accuracy requirements, especially when completing transportation tasks in high-risk production areas ( Ma et al, 2020 ; Sun et al, 2020a ; Liu et al, 2021a ). HUR Sung Wook et al propose a new approach to efficient trajectory optimization that exploits the fact that the dynamics of a deterministic system is uniquely determined by the initial state and control over the time horizon of interest ( Hur et al, 2021 ). Hu proposed a method based on content image retrieval to identify obstacles on the robot’s running path ( Hu, 2021 ).…”

Section: Related Workmentioning

confidence: 99%

Genetic Algorithm-Based Trajectory Optimization for Digital Twin Robots

Liu

Tao

et al. 2022

Front. Bioeng. Biotechnol.

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Mobile robots have an important role in material handling in manufacturing and can be used for a variety of automated tasks. The accuracy of the robot’s moving trajectory has become a key issue affecting its work efficiency. This paper presents a method for optimizing the trajectory of the mobile robot based on the digital twin of the robot. The digital twin of the mobile robot is created by Unity, and the trajectory of the mobile robot is trained in the virtual environment and applied to the physical space. The simulation training in the virtual environment provides schemes for the actual movement of the robot. Based on the actual movement data returned by the physical robot, the preset trajectory of the virtual robot is dynamically adjusted, which in turn enables the correction of the movement trajectory of the physical robot. The contribution of this work is the use of genetic algorithms for path planning of robots, which enables trajectory optimization of mobile robots by reducing the error in the movement trajectory of physical robots through the interaction of virtual and real data. It provides a method to map learning in the virtual domain to the physical robot.

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Section: Related Workmentioning

confidence: 99%

Genetic Algorithm-Based Trajectory Optimization for Digital Twin Robots

Liu

Tao

et al. 2022

Front. Bioeng. Biotechnol.

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“…It discretizes the problem, which makes the procedure for solving the problem simple and avoids the inaccuracy of the initial value solution in indirect methods. It has been widely applied to the solution of optimal trajectory problems [7][8][9][10]. Especially in recent years, with the significant improvement in scientific computing hardware capabilities, the pseudospectral method, as a typical method of point collocation in direct methods, has been widely applied by researchers to solve trajectory planning problems, and its theory has been improved and extended [11,12].…”

Section: Introductionmentioning

confidence: 99%

DDPG-Based Convex Programming Algorithm for the Midcourse Guidance Trajectory of Interceptor

Li,

et al. 2024

Aerospace

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To address the problem of low accuracy and efficiency in trajectory planning algorithms for interceptors facing multiple constraints during the midcourse guidance phase, an improved trajectory convex programming method based on the lateral distance domain is proposed. This algorithm can achieve fast trajectory planning, reduce the approximation error of the planned trajectory, and improve the accuracy of trajectory guidance. First, the concept of lateral distance domain is proposed, and the motion model of the midcourse guidance segment in the interceptor is converted from the time domain to the lateral distance domain. Second, the motion model and multiple constraints are convexly and discretely transformed, and the discrete trajectory convex model is established in the lateral distance domain. Third, the deep reinforcement learning algorithm is used to learn and train the initial solution of trajectory convex programming, and a high-quality initial solution trajectory is obtained. Finally, a dynamic adjustment method based on the distribution of approximate solution errors is designed to achieve efficient dynamic adjustment of grid points in iterative solving. The simulation experiments show that the improved trajectory convex programming algorithm proposed in this paper not only improves the accuracy and efficiency of the algorithm but also has good optimization performance.

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“…With the rapid development of trajectory optimization theory [2][3][4], trajectory planning methods are mainly divided into methods based on optimal control principle, optimization theory, and artificial intelligence technology. Among them, the methods based on optimization theory have advantages in flexibility, but the online computing performance under complex constraints needs to be improved; the methods based on artificial intelligence technology have advantages in efficiency and adaptability [5].…”

Section: Introductionmentioning

confidence: 99%

Boost-Phase Trajectory Planning with the Nonregular Reachable Area Constraints

Diao

Guan

et al. 2022

International Journal of Aerospace Engineering

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During emergency return or mission change, the boost-glide vehicle needs to meet the nonregular reachable area constraints (NRACs). Therefore, this paper introduced NRACs into boost-phase trajectory planning to extend the maneuvering range and enhance the mission adaptability of boost-glide vehicle. Firstly, the library of the reachable area boundaries was constructed with the fast computation method under the deviations of boost terminal states, and the polynomials were used to fit the boundary parameters. Secondly, the nonlinear mapping relationship between the reachable area boundary parameters and the deviations of boost terminal states was obtained by the deep neural networks (DNNs). Then, the new boundary parameters were obtained by the constraint transformation rules under NRACs and the separation window constraints and transformed into the constraints of the boost-phase terminal states by DNNs. Finally, the hp-adaptive pseudospectral method (hpPM) was adopted to complete the trajectory planning considering the path and terminal constraints. The simulation results showed that the proposed trajectory planning method considering NRACs had high trajectory planning accuracy and good deviation adaptability and exhibited excellent performance in the adjustment of reachable area. This study provides theoretical support for integrate mission decisions and trajectory planning of boost-glide vehicles.

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Direct dynamic-simulation approach to trajectory optimization

Cited by 7 publications

References 34 publications

Genetic Algorithm-Based Trajectory Optimization for Digital Twin Robots

Genetic Algorithm-Based Trajectory Optimization for Digital Twin Robots

DDPG-Based Convex Programming Algorithm for the Midcourse Guidance Trajectory of Interceptor

Boost-Phase Trajectory Planning with the Nonregular Reachable Area Constraints

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