scholarly journals Trajectory Planning Algorithm of UAV Based on System Positioning Accuracy Constraints

Electronics ◽  
2020 ◽  
Vol 9 (2) ◽  
pp. 250 ◽  
Author(s):  
Hao Zhou ◽  
Hai-Ling Xiong ◽  
Yun Liu ◽  
Nong-Die Tan ◽  
Lei Chen
Keyword(s):  
Trajectory Planning ◽  
System Structure ◽  
Improved Genetic Algorithm ◽  
A Algorithm ◽  
Positioning Accuracy ◽  
Error Corrections ◽  
Starting Point ◽  
Planning Algorithm ◽  
Aerial Vehicle ◽  
Accuracy Constraints

This paper describes a novel trajectory planning algorithm for an unmanned aerial vehicle (UAV) under the constraints of system positioning accuracy. Due to the limitation of the system structure, a UAV cannot accurately locate itself. Once the positioning error accumulates to a certain degree, the mission may fail. This method focuses on correcting the error during the flight process of a UAV. The improved genetic algorithm (GA) and A* algorithm are used in trajectory planning to ensure the UAV has the shortest trajectory length from the starting point to the ending point under multiple constraints and the least number of error corrections.

scholarly journals Control system of 4-DOF palletizing robot based on improved R control multi-objective trajectory planning

2021 ◽  
Vol 13 (4) ◽  
pp. 168781402110027
Author(s):  
Jianqiang Wang ◽  
Yanmin Zhang ◽  
Xintong Liu
Keyword(s):  
Control System ◽  
Trajectory Planning ◽  
Robot Control ◽  
Machining Process ◽  
Planning System ◽  
Positioning Accuracy ◽  
Multi Objective ◽  
Planning Algorithm ◽  
Function Testing ◽  
Robot Control System

To realize efficient palletizing robot trajectory planning and ensure ultimate robot control system universality and extensibility, the B-spline trajectory planning algorithm is used to establish a palletizing robot control system and the system is tested and analyzed. Simultaneously, to improve trajectory planning speeds, R control trajectory planning is used. Through improved algorithm design, a trajectory interpolation algorithm is established. The robot control system is based on R-dominated multi-objective trajectory planning. System stack function testing and system accuracy testing are conducted in a production environment. During palletizing function testing, the system’s single-step code packet time is stable at approximately 5.8 s and the average evolutionary algebra for each layer ranges between 32.49 and 45.66, which can save trajectory planning time. During system accuracy testing, the palletizing robot system’s repeated positioning accuracy is tested. The repeated positioning accuracy error is currently 10−1 mm and is mainly caused by friction and the machining process. By studying the control system of a four-degrees-of-freedom (4-DOF) palletizing robot based on the trajectory planning algorithm, the design predictions and effects are realized, thus providing a reference for more efficient future palletizing robot design. Although the working process still has some shortcomings, the research has major practical significance.

A Proposed Trajectory Planning Algorithm for Mobile Robot Navigation Based on A* Algorithm

2020 ◽  
pp. 361-376
Author(s):  
Şahin Yıldırım ◽  
Sertaç Savaş
Keyword(s):  
Mobile Robot ◽  
Trajectory Planning ◽  
Heuristic Method ◽  
Working Environment ◽  
Mobile Robot Navigation ◽  
A Algorithm ◽  
Grid Map ◽  
Planning Approach ◽  
Planning Algorithm ◽  
Path Planning Algorithm

This chapter proposes a new trajectory planning approach by improving A* algorithm, which is a widely-used, path-planning algorithm. This algorithm is a heuristic method used in maps such as the occupancy grid map. As the resolution increases in these maps, obstacles can be defined more precisely. However, the cell/grid size must be larger than the size of the mobile robot to prevent the robot from crashing into the borders of the working environment or obstacles. The second constraint of the algorithm is that it does not provide continuous headings. In this study, an avoidance area is calculated on the map for the mobile robot to avoid collisions. Then curve-fitting methods, general polynomial and b-spline, are applied to the path calculated by traditional A* algorithm to obtain smooth rotations and continuous headings by staying faithful to the original path calculated. Performance of the proposed trajectory planning method is compared to others for different target points on the grid map by using a software developed in Labview Environment.

scholarly journals UAV Trajectory Planning in a Port Environment

2020 ◽  
Vol 8 (8) ◽  
pp. 592 ◽  
Author(s):  
Gang Tang ◽  
Zhipeng Hou ◽  
Christophe Claramunt ◽  
Xiong Hu
Keyword(s):  
Trajectory Planning ◽  
Qualitative And Quantitative Analysis ◽  
Simulation Environment ◽  
Average Deviation ◽  
Dynamic Constraints ◽  
Planning Algorithm ◽  
Aerial Vehicle ◽  
Trajectory Method ◽  
Series Of Experiments ◽  
Path Planning Algorithm

In many situations, the trajectory of an unmanned aerial vehicle (UAV) is very likely to deviate from the initial path generated by a path planning algorithm. This is in fact due to the existence of dynamic constraints of the UAV. In order to reduce the degree of such a deviation, this research introduces a trajectory planning algorithm, the objective of which is to minimize distance while maintaining security. The algorithm first develops preprocess trajectory points by constructing isosceles triangles then, on the basis of a minimum snap trajectory method, it applies a corridor constraint to an optimization objective function, while the deviation evaluation function is established to quantitatively evaluate the deviation distance. A series of experiments were carried out in a simulation environment with a simplified quay crane model. The results show that the proposed method not only optimizes the time and length of the generated trajectory, but also reduces the average deviation distance by 88.7%. Moreover, the generated trajectory can be well tracked by the UAV through qualitative and quantitative analysis. Overall, the experiments show that the proposed method can generate a higher UAV trajectory quality.

An Evolutionary Trajectory Planning Algorithm for Multi-UAV-Assisted MEC System

2021 ◽  
Author(s):  
Muhammad Asim ◽  
Wali Khan ◽  
Samir Brahim Belhaouari
Keyword(s):  
Energy Consumption ◽  
Unmanned Aerial Vehicle ◽  
Trajectory Planning ◽  
Mixed Integer ◽  
Phase Variable ◽  
Mobile Edge Computing ◽  
Evolutionary Trajectory ◽  
Multiple Uavs ◽  
Planning Algorithm ◽  
Aerial Vehicle

Abstract This paper presents a multi-unmanned aerial vehicle (UAV)-assisted mobile edge computing (MEC) system, where multiple UAVs are used to serve mobile users (MUs). We aim to minimize the overall energy consumption of the system by planning the trajectories of UAVs. To plan the trajectories of UAVs, we need to consider the deployment of hovering points (HPs) of UAVs, their association with UAVs, and their order for each UAV. Therefore, the problem is very complicated, as it is non-convex, nonlinear, NP-hard, and mixed-integer. To solve the problem, this paper proposed an evolutionary trajectory planning algorithm (ETPA), which comprises three phases. In the first phase, variable-length GA is adopted to update the deployments of HPs for UAVs. Accordingly, redundant HPs are removed by the remove operator. Subsequently, differential evolution clustering is adopted to cluster HPs into different clusters without knowing the number of HPs in advance. Finally, a GA is proposed to construct the order of HPs for UAVs. The experimental results on a set of eight instances show that the proposed ETPA outperforms other compared algorithms in terms of the energy consumption of the system.This paper presents a multi-unmanned aerial vehicle (UAV)-assisted mobile edge computing (MEC) system, where multiple UAVs are used to serve mobile users (MUs). We aim to minimize the overall energy consumption of the system by planning the trajectories of UAVs. To plan the trajectories of UAVs, we need to consider the deployment of hovering points (HPs) of UAVs, their association with UAVs, and their order for each UAV. Therefore, the problem is very complicated, as it is non-convex, nonlinear, NP-hard, and mixed-integer. To solve the problem, this paper proposed an evolutionary trajectory planning algorithm (ETPA), which comprises three phases. In the first phase, variable-length GA is adopted to update the deployments of HPs for UAVs. Accordingly, redundant HPs are removed by the remove operator. Subsequently, differential evolution clustering is adopted to cluster HPs into different clusters without knowing the number of HPs in advance. Finally, a GA is proposed to construct the order of HPs for UAVs. The experimental results on a set of eight instances show that the proposed ETPA outperforms other compared algorithms in terms of the energy consumption of the system.

Cuckoo search-based method for trajectory planning of quadrotor in an urban environment

2019 ◽  
Vol 233 (12) ◽  
pp. 4571-4582 ◽  
Author(s):  
Hanjie Hu ◽  
Yu Wu ◽  
Jinfa Xu ◽  
Qingyun Sun
Keyword(s):  
Urban Environment ◽  
Trajectory Planning ◽  
Cuckoo Search ◽  
General Idea ◽  
Planning Problem ◽  
Safety Level ◽  
Planning Algorithm ◽  
Aerial Vehicle ◽  
Express Industry ◽  
Easy Operation

Express by micro aerial vehicle (MAV) becomes more and more popular because it can avoid the influence of terrain and save more space for taking-off and landing of aircraft. At present, quadrotor is often used in the express industry due to its flexibility and easy operation, and the flight trajectory plays an important role in the efficiency and safety level of express service. In this paper, the trajectory planning problem is studied for quadrotor delivering goods in urban environment with the purpose of avoiding the heavy ground traffic, and a cuckoo search (CS)-based trajectory planning method is proposed to solve the problem. First, a conceptual model containing all the key elements of the delivery task is developed, which presents a general idea of solving the problem. Some characteristics of the urban environment and the delivery task, such as the wind field, dense buildings and inclination of shipped goods, are taken into account in the trajectory planning model. The goal of the delivery task is to make the goods reach the destination accurately. When designing the CS-based trajectory planning algorithm, the basics of CS algorithm are explained, and then it is integrated into the trajectory planning problem. Comparative experiments are carried out to investigate the superiority of the proposed method, and the influences of parameters in CS algorithm are also discussed to conclude its performance in trajectory planning problem.

Trajectory Planning for a Articulated Robot

2015 ◽  
Vol 742 ◽  
pp. 576-581
Author(s):  
Kai Kai Wang ◽  
Jia Qi Li ◽  
Yuan Wei Zou ◽  
Cheng Li Wang ◽  
Liu Bo Liang
Keyword(s):  
Trajectory Planning ◽  
Reference Value ◽  
Matlab Simulation ◽  
Robot Trajectory ◽  
Starting Point ◽  
Depth Study ◽  
Planning Algorithm ◽  
Articulated Robot ◽  
Path Planning Algorithm ◽  
Destination Point

This paper focuses on the robot trajectory planning algorithm in-depth study, we propose a new path planning algorithm to ensure that the velocity and the acceleration of the starting point and the destination point are zero at the same time, and there are continuity of the intermediate points also.Give the matlab simulation waveform diagram and the algorithm at last,and this trajectory planning provides a good reference value for robot trajectory studies of future.

Constrained optimal terrain following/threat avoidance trajectory planning using network flow

2014 ◽  
Vol 118 (1203) ◽  
pp. 523-539 ◽  
Author(s):  
R. Zardashti ◽  
A. A. Nikkhah ◽  
M. J. Yazdanpanah
Keyword(s):  
Cost Function ◽  
Trajectory Planning ◽  
Network Flow ◽  
Equations Of Motion ◽  
Minimum Cost ◽  
Masking Effect ◽  
Search Problem ◽  
Starting Point ◽  
Terrain Following ◽  
Threat Avoidance

AbstractThis paper focuses on the trajectory planning for a UAV on a low altitude terrain following/threat avoidance (TF/TA) mission. Using a grid-based approximated discretisation scheme, the continuous constrained optimisation problem into a search problem is transformed over a finite network. A variant of the Minimum Cost Network Flow (MCNF) to this problem is then applied. Based on using the Digital Terrain Elevation Data (DTED) and discrete dynamic equations of motion, the four-dimensional (4D) trajectory (three spatial and one time dimensions) from a starting point to an end point is obtained by minimising a cost function subject to dynamic and mission constraints of the UAV. For each arc in the grid, a cost function is considered as the combination of the arc length, fuel consumption and flight time. The proposed algorithm which considers dynamic and altitude constraints of the UAV explicitly is then used to obtain the feasible trajectory. The resultant trajectory can increase the survivability of the UAV using the threat region avoidance and the terrain masking effect. After obtaining the feasible trajectory, an improved algorithm is proposed to smooth the trajectory. The numeric results are presented to verify the capability of the proposed approach to generate admissible trajectory in minimum possible time in comparison to the previous works.

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