Autonomous dispatch trajectory planning on flight deck: A search-resampling-optimization framework

Wang, Xinwei; Li, Bai; Su, Xichao; Peng, Haijun; Wang, Lei; Lu, Chen; Wang, Chao

doi:10.1016/j.engappai.2022.105792

Cited by 32 publications

(22 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Another potential avenue for future work is to develop a corridor-based method to increase the robustness of the algorithm, especially for the cases where narrow tunnels exist. Several works in literature implement corridor-based methods for docking applications of USVs [28] and safe trajectory planning for autonomous dispatch on flight deck [29] which show promising results and can be further implemented for safe trajectory planning for USVs.…”

Section: Discussionmentioning

confidence: 99%

Trajectory-Free Motion Planning of an Unmanned Surface Vehicle Based on MPC and Sparse Neighborhood Graph

2023

View full text Add to dashboard Cite

Unmanned Surface Vehicles (USV) have gained significant attention in military, science, and research applications in recent years. The development of new USV systems and increased application domain of these platforms has necessitated the development of new motion planning methods to improve the autonomy level of USVs and provide safe and robust navigation across unpredictable marine environments. This study proposes a feedback motion planning and control methodology for dynamic fully-and underactuated USV models built on the recently introduced sparse random neighborhood graphs and constrained nonlinear Model Predictive Control (MPC). This approach employs a feedback motion planning strategy based on sparsely connected obstacle-free regions and the sequential composition of MPC policies. The algorithm generates a sparse neighborhood graph consisting of connected rectangular zones in the discrete planning phase. Inside each node (rectangular region), an MPC-based online feedback control policy funnels the USV with nonlinear dynamics from one rectangle to the other in the network, ensuring no constraint violation on state and input variables occurs. We systematically test the proposed algorithms in different simulation scenarios, including an extreme actuator noise scenario, to test the algorithm's validity, effectiveness, and robustness.INDEX TERMS Nonlinear model predictive control, feedback motion planning, sampling-based motion planning, unmanned surface vehicles.

show abstract

Section: Discussionmentioning

confidence: 99%

Trajectory-Free Motion Planning of an Unmanned Surface Vehicle Based on MPC and Sparse Neighborhood Graph

2023

View full text Add to dashboard Cite

show abstract

“…Thanks very much to Professor Li Bai's research team of Hunan University. The research achievements, literature [37][38][39] provide great inspiration for the study of our research and the literature [26][27][28][29][30][31] have very high academic guidance significance for our subsequent research.…”

Section: Acknowledgementmentioning

confidence: 99%

“…In terms of numerical optimization methods, a search-resampling-optimization (SRO) framework is proposed in literature. 26 A hybrid A* algorithm is employed to generate a coarse path and a resampling process is implemented to pave a series of safe dispatch corridors (SDCs) along the coarse path. The resampled result is further fed into the optimization stage to facilitate the numerical solution.…”

Section: Introductionmentioning

confidence: 99%

Time-optimal trajectory planning for autonomous parking of tractor-semitrailer vehicle based on piecewise Gauss pseudospectral method

Qiu,

Cheng,

Yang

et al. 2023

Advances in Mechanical Engineering

View full text Add to dashboard Cite

For the autonomous parking problem of the tractor-semitrailer vehicle, a piecewise Gauss pseudospectral method is proposed as a trajectory planning scheme in this paper. Firstly, the trajectory planning problem is transformed into an optimal control problem by establishing a vehicle kinematics model for the low-velocity scene, the dynamic constraints including the physical constraints, the collision avoidance constraints, and the endpoint constraints. The optimal control problem are discretized into a nonlinear programming problem by piecewise Gauss pseudospectral method according to the movement area during parking process. The shortest time to complete parking is taken as the objective function. The Gauss pseudospectral method is further adopted to solve the nonlinear programming problem in the two parking stages. Three parallel parking conditions with different lengths of parking spaces and three vertical parking conditions with different widths of parking spaces are set for simulation verification based on Matlab/Simulink. The simulation results show that the proposed method can uniformly and effectively solve the trajectory planning problem of the tractor-semitrailer vehicle. Compared with the traditional pseudospectral method, a shorter parking time and a significantly higher convergence rate can be got with the method, which further indicates that the proposed method has good effectiveness and robustness.

show abstract

“…Both the reference tracking guidance and predictor-corrector guidance have advantages and disadvantages. If there is no deviation or disturbance, the reentry problem can be solved under the framework of trajectory planning, which is widely studied [30][31][32][33]. However, the complex online deviation and rapidity requirement call for an intelligent online method.…”

Section: Introductionmentioning

confidence: 99%

Intelligent Online Multiconstrained Reentry Guidance Based on Hindsight Experience Replay

Jiang

Wang

Bai

et al. 2023

International Journal of Aerospace Engineering

View full text Add to dashboard Cite

Traditional guidance algorithms for hypersonic glide vehicles face the challenge of real-time requirements and robustness to multiple deviations or tasks. In this paper, an intelligent online multiconstrained reentry guidance is proposed to strikingly reduce computational burden and enhance the effectiveness with multiple constraints. First, the simulation environment of reentry including dynamics, multiconstraints, and control variables is built. Different from traditional decoupling methods, the bank angle command including its magnitude and sign is designed as the sole guidance variable. Secondly, a policy neural network is designed to output end-to-end guidance commands. By transforming the reentry process into a Markov Decision Process (MDP), the policy network can be trained by deep reinforcement learning (DRL). To address the sparse reward issue caused by multiconstraints, the improved Hindsight Experience Replay (HER) method is adaptively combined with Deep Deterministic Policy Gradient (DDPG) algorithm by transforming multiconstraints into multigoals. As a result, the novel training algorithm can realize higher utilization of failed data and improve the rate of convergence. Finally, simulations for typical scenes show that the policy network in the proposed guidance can output effective commands in much less time than the traditional method. The guidance is robust to initial bias, different targets, and online aerodynamic deviation.

show abstract

Autonomous dispatch trajectory planning on flight deck: A search-resampling-optimization framework

Cited by 32 publications

References 27 publications

Trajectory-Free Motion Planning of an Unmanned Surface Vehicle Based on MPC and Sparse Neighborhood Graph

Trajectory-Free Motion Planning of an Unmanned Surface Vehicle Based on MPC and Sparse Neighborhood Graph

Time-optimal trajectory planning for autonomous parking of tractor-semitrailer vehicle based on piecewise Gauss pseudospectral method

Intelligent Online Multiconstrained Reentry Guidance Based on Hindsight Experience Replay

Contact Info

Product

Resources

About