Vector trajectory method for obstacle avoidance constrained planetary landing trajectory optimization

2022 ◽  
pp. 1-1
Author(s):  
Jiateng Long ◽  
Pingyuan Cui ◽  
Shengying Zhu
Keyword(s):  
Obstacle Avoidance ◽  
Trajectory Optimization ◽  
Planetary Landing ◽  
Trajectory Method ◽  
Landing Trajectory

scholarly journals Trajectory Optimization of Pickup Manipulator in Obstacle Environment Based on Improved Artificial Potential Field Method

2020 ◽  
Vol 10 (3) ◽  
pp. 935 ◽  
Author(s):  
Haibo Zhou ◽  
Shun Zhou ◽  
Jia Yu ◽  
Zhongdang Zhang ◽  
Zhenzhong Liu
Keyword(s):  
Genetic Algorithm ◽  
Obstacle Avoidance ◽  
Potential Field ◽  
Trajectory Optimization ◽  
Field Method ◽  
Artificial Potential Field ◽  
Adaptive Genetic Algorithm ◽  
Potential Field Method ◽  
Kinematics Modeling ◽  
Artificial Potential Field Method

In order to realize the technique of quick picking and obstacle avoidance, this work proposes a trajectory optimization method for the pickup manipulator under the obstacle condition. The proposed method is based on the improved artificial potential field method and the cosine adaptive genetic algorithm. Firstly, the Denavit–Hartenberg (D-H) method is used to carry out the kinematics modeling of the pickup manipulator. Taking into account the motion constraints, the cosine adaptive genetic algorithm is utilized to complete the time-optimal trajectory planning. Then, for the collision problem in the obstacle environment, the artificial potential field method is used to establish the attraction, repulsion, and resultant potential field functions. By improving the repulsion potential field function and increasing the sub-target point, obstacle avoidance planning of the improved artificial potential field method is completed. Finally, combined with the improved artificial potential field method and cosine adaptive genetic algorithm, the movement simulation analysis of the five-Degree-of-Freedom pickup manipulator is carried out. The trajectory optimization under the obstacle environment is realized, and the picking efficiency is improved.

Category A Takeoff and Landing Trajectory Optimization for Transport Category Rotorcraft Certification

2019 ◽  
Vol 25 (12) ◽  
pp. 1048-1056
Author(s):  
Jun Young An ◽  
Chang-joo Kim ◽  
Sung wook Hur ◽  
Seong han Lee
Keyword(s):  
Trajectory Optimization ◽  
Landing Trajectory

Suboptimal trajectory programming for unmanned aerial vehicles with dynamic obstacle avoidance

2018 ◽  
Vol 233 (10) ◽  
pp. 3857-3869
Author(s):  
Hang Guo ◽  
Wen-xing Fu ◽  
Bin Fu ◽  
Kang Chen ◽  
Jie Yan
Keyword(s):  
Unmanned Aerial Vehicles ◽  
Obstacle Avoidance ◽  
Unmanned Aerial Vehicle ◽  
Trajectory Optimization ◽  
Sliding Mode ◽  
Aerial Vehicles ◽  
Slack Variables ◽  
Dynamic Obstacle Avoidance ◽  
Aerial Vehicle ◽  
Dynamic Obstacle

With regard to the dynamic obstacles current unmanned aerial vehicles encountered in practical applications, an integral suboptimal trajectory programming method was proposed. It tackled with multiple constraints simultaneously while guiding the unmanned aerial vehicle to execute autonomous avoidance maneuver. The kinetics of both unmanned aerial vehicle and dynamic obstacles were established with appropriate hypotheses. Then it was assumed that the unmanned aerial vehicle was faced with terminal constraints and control constraints in the whole duration. Meanwhile, the performance index was established as minimum control efforts. The initial trajectory was generated according to optimized model predictive static programming. Next, the slack variables were introduced to transform the inequality constraints arising from dynamic obstacle avoidance into equality constraints. In addition, sliding mode control theory was utilized to determine these slack variables' dynamics by designing the approaching law of sliding mode. Then the avoidance trajectory for single or multiple dynamic obstacles was developed by this combined method. At last, a further trajectory optimization was conducted by differential dynamic programming. Consequently, the integral problem was solved step by step and numerical simulations demonstrated that the integral method possessed high computational efficiency.

Development and Flight Test Evaluations of an Autonomous Obstacle Avoidance System for a Rotary-Wing UAV

2013 ◽  
Vol 01 (01) ◽  
pp. 3-19 ◽  
Author(s):  
Keeryun Kang ◽  
J. V. R. Prasad
Keyword(s):  
Path Planning ◽  
Obstacle Avoidance ◽  
Trajectory Optimization ◽  
Dynamic Programming Algorithm ◽  
Programming Algorithm ◽  
Georgia Tech ◽  
Test Bed ◽  
Optimization Scheme ◽  
Flight Tests ◽  
Rotary Wing

This paper presents the development and flight-testing of an obstacle avoidance system that can provide a rotary-wing unmanned aerial vehicle (UAV) the autonomous obstacle field navigation capability in uncertain environment. The system is composed of a sensor, an obstacle map generation algorithm from sensor measurements, an online path planning algorithm, and an adaptive vehicle controller. The novel approach of path planning presented in the paper is the integration of a newly developed receding horizon (RH) trajectory optimization scheme with a global path searching algorithm. The developed RH trajectory optimization scheme solves the local nonlinear trajectory optimization problem using approximated vehicle dynamics, maneuverability constraints, and terrain constraints within the finite range of the sensor. The global path searching by dynamic programming algorithm finds the shortest path to the destination to provide the initial guess to the RH trajectory optimization. The spline-based direct solver, Nonlinear Trajectory Generation (NTG), solves the RH trajectory optimization in real time and updates the solution continuously. The developed system is implemented within the Georgia Tech UAV Simulation Tool (GUST) and on the onboard computer of the Georgia Tech UAV test bed. Simulations and flight tests carried out for the benchmark scenarios and with sensor-in-the-loop flight tests demonstrated the viability of the developed system for autonomous obstacle field navigation capability of a UAV.

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