Path planning for nuclear radiation environments based on an improved artificial potential field A* algorithm

Mengwen QIU; Hua ZHANG; Huaifang ZHOU

doi:10.11889/j.1000-3436.2022-0054

Journals >Journal of Radiation Research and Radiation Processing >Volume 40 >Issue 6 >Page 060601 > Article

Journal of Radiation Research and Radiation Processing
Vol. 40, Issue 6, 060601 (2022)

Path planning for nuclear radiation environments based on an improved artificial potential field A* algorithm

Mengwen QIU^1、2, Hua ZHANG^1、2、*, and Huaifang ZHOU^1、2

Author Affiliations

¹Southwest University of Science & Technology, Mianyang 621010, China

²Sichuan Key Laboratory of Special Enviromental Robotics, Mianyang 621010, China

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DOI: 10.11889/j.1000-3436.2022-0054 Cite this Article

Mengwen QIU, Hua ZHANG, Huaifang ZHOU. Path planning for nuclear radiation environments based on an improved artificial potential field A* algorithm[J]. Journal of Radiation Research and Radiation Processing, 2022, 40(6): 060601 Copy Citation Text

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Fig. 1. Environmental modelling: (a) mode of search; (b) raster maps and risk points

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Fig. 2. Heuristic function selection of A* algorithm: (a) dose rate selection of adjacent grids; (b) selection of estimated path dose rate

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Fig. 3. Artificial potential field forces

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Fig. 4. Comparison with the cited literature algorithm after improving the evaluation function in this paper:(a) comparison of the number of search nodes; (b) total dose comparison (color online)

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Fig. 5. Comparison diagram of the algorithm after the introduction of artificial potential field influence:(a) 30×30 map; (b) 50×50 map

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Fig. 6. Comparison diagram of algorithm after introducing dynamic coefficient: (a) 30×30 map; (b) 50×50 map

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Algorithm 1: Improve A*

Input: Obstacle_map, Nuclear_map, Map_size, Start_point, Goal_point, Open_list, Closed_list

1 For i←1 to (Map_size)

2 For j←1 to (Map_size)

3 do if Nodes(i,j)≠Obstacle

4 then I(i,j)←artificial_potential_field + Nuclear_radiation_dose

5 end

6 end

7 end

……

8 Openlist = Start_point

9 while Current_node ≠ Goal_point

10 do Min F’(n) ← Cost of surrounding expandable nodes

11 next_nodes ← Min F’(n) corresponding node

12 Closed_list = [Closed_list; next_nodes]

13 Closed_cost = [Closed_cost; Min F’(n)]

14 end

Output: Path, Cumulative nuclear radiation dose, Search nodes

Table 0. [in Chinese]

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路径编号

Path

number

移动距离 / m

Moving

distance

搜索耗时 / s

Search time

搜索节点数 / Piece

Number of search

nodes

高风险点数 / Point

High risk points

低风险点数 / Point Low risk points

总剂量代价 / μSv

Total dose cost

路径1

Path1

8.47

0.51

784

77.78

路径2

Path2

路径3

Path3

路径4

Path4

路径5

Path5

路径6

Path6

10.41

9.71

11.24

12.97

14.84

0.58

0.56

0.59

0.69

0.67

1 800

1 576

808

5 456

3 984

32.49

37.17

96.66

40.48

46.29

Table 1. Comparison of experimental data of each algorithm

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路径编号

Path

number

移动距离 / m

Moving

distance

搜索耗时 / s

The search

time

搜索节点数 / Piece

Number of search

nodes

高风险点数

/ Point

High risk points

低风险点数

/ Point

Low risk points

总剂量代价

/ μSv

Total dose cost

路径1

Path1

8.47

0.51

784

77.78

路径2

Path2

路径3

Path3

路径4

Path4

路径5

Path5

路径6

Path6

10.41

11.15

11.24

12.97

14.84

0.58

0.54

0.59

0.69

0.63

1 800

1 176

808

5 456

2 936

32.49

34.77

96.66

40.48

46.29

Table 2. Comparison of experimental data of each algorithm

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