Pheromone Positive Incentive Grid Method for Multi-unmanned Platform Regional Surveillance Task

doi:10.12382/bgxb.2022.0537

Abstract

Abstract:

There is a common problem of regional surveillance in operations in densely populated urban areas. In order to evacuate our side from the highest risk area to reduce damage or manpower consumption, the use of unmanned systems to carry out reconnaissance and surveillance tasks is of great military significance and application value. Aiming at collaborative monitoring tasks with complex and ever-changing environments and multiple unmanned platforms with adjacent initial positions, in response to the shortcomings of existing control strategies that are prone to conflicts when traversing the target space and the lack of research on the proximity of initial positions of multiple unmanned platforms, based on semi heuristic control strategies and grid methods, the objective function is improved by introducing pheromones and developing conflict resolution rules, a pheromone positive incentive grid method is constructed. Experimental results showed that the proposed method performed better in conflict resolution than existing control strategies, its overall performance was better, and the global average idle time was better especially when there were many obstacles. The effectiveness and rationality of the proposed method had been verified.

Key words: multi unmanned platforms, cooperative surveillance, semi-heuristic control, pheromone

CLC Number:

TP242

CHEN Yaping, WANG Nan, HONG Huajie, LIU Zhaoyang, YAN Xiangda. Pheromone Positive Incentive Grid Method for Multi-unmanned Platform Regional Surveillance Task[J]. Acta Armamentarii, 2023, 44(9): 2859-2870.

Figures/Tables 15

Fig.1 Environment model in grids

Fig.2 Schematic diagram of position exchange of adjacent unmanned platforms

Fig.3 Comparison diagram before and after grid method included

Fig.4 Conflict resolution schematic diagram

Fig.5 Self-loop judgement diagram

Fig.6 Schematic diagram of the continuous monitoring process

Fig.7 Scenario 1 with different numbers of obstacles

Fig.8 Scenario 2 with different numbers of obstacles

Fig.9 Diagram of planning trajectory of unmanned platform

Table 1 Sum of conflict shares when OBS takes different values in Scenario 1 %

算法	OBS=19	OBS=13	OBS=6
CR	100.7	100.1	100.1
HPCC	102.3	102.9	102.9
EVAP	34.9	51.6	41.3
CLInG	48.5	74.4	46.5
SGM	11.8	21.2	25.7
PPIGM	0.1	0.1	0.1

Fig.10 Comparison diagram of global average idle time of different algorithms in Scenario 1

Fig.11 Box diagram of different algorithms of global average idle time in Scenario 1

Table 2 Sum of conflict shares when OBS takes different values in Scenario 2 %

算法	OBS=19	OBS=13	OBS=6
CR	100	100.2	100
HPCC	100.4	105.2	100.4
EVAP	70.8	46.8	36.8
CLInG	61.2	62.5	57.7
SGM	8.2	22.3	24.2
PPIGM	0.1	0	0.1

Fig.12 Comparison diagram of global average idle time of different algorithms in Scenario 2

Fig.13 Box diagram of different algorithms of global average idle time in Scenario 2

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