Real-time trajectory optimization of an underactuated rigid spacecraft using differential flatness

2012 ◽  
Vol 23 (1) ◽  
pp. 132-139 ◽  
Author(s):  
Yufei Zhuang ◽  
Guangfu Ma ◽  
Haibin Huang ◽  
Chuanjiang Li
Keyword(s):  
Real Time ◽  
Trajectory Optimization ◽  
Differential Flatness ◽  
Rigid Spacecraft

Real-Time Onboard Trajectory Optimization Using Indirect Methods

2021 ◽  
Author(s):  
Sean M. Nolan ◽  
Clayton A. Smith ◽  
Jacob D. Wood
Keyword(s):  
Real Time ◽  
Trajectory Optimization ◽  
Indirect Methods

Real-Time Cooperative Trajectory Planning Using Differential Flatness Approach and B-Splines

2013 ◽  
Vol 333-335 ◽  
pp. 1338-1343 ◽  
Author(s):  
Xue Qiang Gu ◽  
Yu Zhang ◽  
Jing Chen ◽  
Lin Cheng Shen
Keyword(s):  
Optimal Control ◽  
Real Time ◽  
Trajectory Planning ◽  
Differential Flatness ◽  
Planning Problem ◽  
Receding Horizon ◽  
B Splines ◽  
Battlefield Environment ◽  
Planning Space ◽  
Spatial Trajectories

This paper proposed a cooperative receding horizon optimal control framework, based on differential flatness and B-splines, which was used to solve the real-time cooperative trajectory planning for multi-UCAV performing cooperative air-to-ground target attack missions. The planning problem was formulated as a cooperative receding horizon optimal control problem (CRHC-OCP), and then the differential flatness and B-splines were introduced to lower the dimension of the planning space and parameterize the spatial trajectories. Moreover, for the dynamic and uncertainty of the battlefield environment, the cooperative receding horizon control was introduced. Finally, the proposed approach is demonstrated, and the results show that this approach is feasible and effective.

scholarly journals Numerical Simulation of Real-Time Trajectory Optimization for Helicopter Noise Abatement

2010 ◽  
Vol 3 (2) ◽  
pp. 415-430 ◽  
Author(s):  
Hiroshi IKAIDA ◽  
Takeshi TSUCHIYA ◽  
Hirokazu ISHII ◽  
Hiromi GOMI ◽  
Yoshinori OKUNO
Keyword(s):  
Numerical Simulation ◽  
Real Time ◽  
Trajectory Optimization ◽  
Noise Abatement ◽  
Helicopter Noise

scholarly journals A Hierarchical Autonomous Driver for a Racing Car: Real-Time Planning and Tracking of the Trajectory

Energies ◽  
2021 ◽  
Vol 14 (19) ◽  
pp. 6008
Author(s):  
Margherita Montani ◽  
Leandro Ronchi ◽  
Renzo Capitani ◽  
Claudio Annicchiarico
Keyword(s):  
Real Time ◽  
Trajectory Planning ◽  
Vehicle Dynamics ◽  
Trajectory Optimization ◽  
Computational Cost ◽  
Path Tracking ◽  
Driver Model ◽  
Current Scenario ◽  
Optimum Model ◽  
Time Planning

The aim of this study was to develop trajectory planning that would allow an autonomous racing car to be driven as close as possible to what a driver would do, defining the most appropriate inputs for the current scenario. The search for the optimal trajectory in terms of lap time reduction involves the modeling of all the non-linearities of the vehicle dynamics with the disadvantage of being a time-consuming problem and not being able to be implemented in real-time. However, to improve the vehicle performances, the trajectory needs to be optimized online with the knowledge of the actual vehicle dynamics and path conditions. Therefore, this study involved the development of an architecture that allows an autonomous racing car to have an optimal online trajectory planning and path tracking ensuring professional driver performances. The real-time trajectory optimization can also ensure a possible future implementation in the urban area where obstacles and dynamic scenarios could be faced. It was chosen to implement a local trajectory planning based on the Model Predictive Control(MPC) logic and solved as Linear Programming (LP) by Sequential Convex Programming (SCP). The idea was to achieve a computational cost, 0.1 s, using a point mass vehicle model constrained by experimental definition and approximation of the car’s GG-V, and developing an optimum model-based path tracking to define the driver model that allows A car to follow the trajectory defined by the planner ensuring a signal input every 0.001 s. To validate the algorithm, two types of tests were carried out: a Matlab-Simulink, Vi-Grade co-simulation test, comparing the proposed algorithm with the performance of an offline motion planning, and a real-time simulator test, comparing the proposed algorithm with the performance of a professional driver. The results obtained showed that the computational cost of the optimization algorithm developed is below the limit of 0.1 s, and the architecture showed a reduction of the lap time of about 1 s compared to the offline optimizer and reproducibility of the performance obtained by the driver.

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