Journal of Jilin University(Engineering and Technology Edition) ›› 2023, Vol. 53 ›› Issue (3): 781-791.doi: 10.13229/j.cnki.jdxbgxb20221026

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Trajectory planning of unmanned system based on DKRRT*⁃APF algorithm

Zhen-yu WU1(),Xiao-fei LIU2,Yi-pu WANG1   

  1. 1.School of Innovation and Entrepreneurship，Dalian University of Technology，Dalian 116024，China
    2.School of Control Science and Engineering，Dalian University of Technology，Dalian 116024，China
  • Received:2022-12-12 Online:2023-03-01 Published:2023-03-29

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Abstract:

In allusion to the disadvantages of traditional RRT algorithm such as strong randomness， slow convergence， lack of continuity and being unable to meet the dynamic constraints of most robots， and Kinodynamic RRT* algorithm which conforms to dynamic constraints but have a slow search speed， a DKRRT*-APF algorithm besed on Kinodynamic RRT* algorithm was proposed. Kinodynamic RRT* node expansion was improved from unidirectional expansion to bidirectional expansion to speed up trajectory search efficiency； meanwhile， in the sampling stage of Kinodynamic RRT*， the idea of map potential field of APF algorithm was introduced， and special gravity function and repulsion function were designed to make the target points and obstacles in the map provide some target guidance and obstacle avoidance effect for the sampling nodes， and shorten trajectory planning time. To verify the feasibility of this idea， the DKRRT*-APF algorithm was tested by using the double integral dynamic unmanned vehicle model and the linearized unmanned quadrotor model. The simulation results show that the trajectory planning by DKRRT*-APF algorithm conforms to the dynamic constraints of the robot model. When the average trajectory loss is the same， the average number of generated nodes and the average trajectory planning time of DKRRT*-APF algorithm are effectively improved compared with KRRT*.

Key words: computer application, global path planning, rapidly-exploring random trees, artificial potential field method

CLC Number: 

  • F416.67

Fig.1

Schematic diagram of node adjustment using APF algorithm"

Fig.2

Schematic diagram of double tree expansion"

Fig.3

Gravitational relation curve"

Fig.4

Repulsion curve"

Fig.5

Flow chart of DKRRT*-APF algorithm"

Fig.6

Module 1：process of KRRT* algorithm expansion and reconstruction of search tree"

Table 1

Map1：complex map obstacle information"

序号位置信息序号位置信息
1（20，80，4，7）17（50，50，3，17）
2（20，30，5，10）18（55，10，5，9）
3（60，30，6，7）19（70，20，3，13）
4（50，70，8，5）20（90，10，5，9）
5（70，60，2，8）21（85，90，5，12）
6（30，30，1，9）22（10，90，5，8）
7（25，50，3，9）23（65，93，7，9）
8（30，10，5，13）24（95，60，5，12）
9（90，80，3，11）25（80，64，3，9）
10（40，40，5，16）26（40，27，5，10）
11（78，45，6，5）27（65，50，4，15）
12（10，13，4，8）28（75，80，3，14）
13（60，70，2，9）29（10，70，3，19）
14（10，40，5，10）30（90，48，2，12）
15（20，60，5，12）31（75，10，3，13）
16（35，85，5，8）32（92，30，4，14）

Table 2

Map2：Narrow passage map obstacle information"

序号位置信息序号位置信息
1（30，95，5，20）10（70，95，5，20）
2（30，85，5，20）11（70，85，5，20）
3（30，65，5，20）12（70，65，5，20）
4（30，55，5，20）13（70，55，5，20）
5（30，45，5，20）14（70，45，5，20）
6（30，35，5，20）15（70，35，5，20）
7（30，25，5，20）16（70，25，5，20）
8（30，15，5，20）17（70，15，5，20）
9（30，5，5，20）18（70，5，5，20）

Fig.7

A double integral dynamic sweeper"

Fig.8

Effect comparison of double integral unmanned vehicle model"

Table 3

Experimental results of double integral unmanned vehicle model"

类别平均损耗平均迭代次数平均耗时/ms
KRRT*（地图一）104.9191.705718.64
KRRT*-APF（地图一）101.7754.522483.40
DKRRT*-APF（地图一）109.3527.231241.54
KRRT*（地图二）126.2645.141128.61
KRRT*-APF（地图二）123.4735.20967.80
DKRRT*-APF（地图二）124.9323.72595.86

Fig.9

A quadrotor unmanned aerial vehicle"

Fig.10

Effect comparison of linearized quadrotor model"

Table 4

Experimental results of linearizedquadrotor model"

类别平均损耗平均迭代次数平均耗时/ms
KRRT*（地图一）15.7342.05558.26
KRRT*-APF（地图一）16.7621.74252.41
DKRRT*-APF（地图一）17.1211.93155.81
KRRT*（地图二）19.0330.45542.89
KRRT*-APF（地图二）18.8326.04417.72
DKRRT*-APF（地图二）19.5720.07306.83
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