A Probabilistic Target Search Algorithm Based on Hierarchical Collaboration for Improving Rapidity of Drones
Abstract
:1. Introduction
- First, an improved hierarchical probabilistic target search algorithm based on the collaboration of drones at different altitudes is proposed. This is a method for reducing the search time and search distance by improving the information transfer methods between high-altitude and low-altitude drones. Specifically, to improve the speed of target detection, a high-altitude drone performs a preliminary search of a wide area.
- Second, this study suggests a method of using thresholds for information transfer between high altitude and low altitude to improve the efficiency of a search, i.e., to reduce the search time and search travel distance. In this method, when the probability of the existence of a target at a high altitude is higher than a certain threshold, the search information is transmitted to a low-altitude drone.
- Third, several drone collaboration scenarios that can be performed by two drones at different altitudes are introduced and compared to the proposed algorithm. These methods are hierarchical cooperation methods of drones that can be used in an actual search. Through simulations, it is demonstrated that methods utilizing hierarchical searches with drones are comparatively excellent and that the proposed algorithm has better performance compared to other scenarios.
2. Related Works
2.1. Target Detection Method
2.2. Target Detection Based on Probabilistic Search
2.3. Altitude Control Strategies
3. Advanced Hierarchical Probabilistic Search Algorithm
3.1. Improvement of the Probabilistic Search
Algorithm 1 Basic Outline of Proposed Algorithm | |
1: | while Maxb < Thb |
2: | //high-altitude search |
3: | high-altitude drone searches for the target sequentially in four search areas |
4: | and computes a belief value for each cell in the search areas, |
5: | it then selects an Areah |
6: | if Maxb > Thn then |
7: | //low-altitude search |
8: | send search information to the low-altitude drone, |
9: | low-altitude drone searches for the target sequentially in the Areah |
10: | and computes Maxb |
11: | end |
12: | end |
13: | stop searching |
3.2. Altitude Control Strategy
3.3. Advanced Hierarchical Probabilistic Search Algorithm
Algorithm 2 Downward Delay Search | |
1: | //Initialize |
2: | found = 0; |
3: | roundh = 0; |
4: | roundl = 0; |
5: | while found ! = 1 |
6: | //High-altitude search |
7: | if roundh < round_limit then |
8: | the drone searches four Areahs at high altitude |
9: | select the Areah containing the cell with the highest probability |
10: | if HPh ≥ Thsp then |
11: | found = 1 |
12: | stop searching |
13: | break |
14: | else |
15: | if HPh > Thlp then |
16: | //Low-altitude search |
17: | if roundl < round_limit then |
18: | send information to low-altitude drone |
19: | low-altitude drone searches four Areals at low altitude |
20: | if HPl ≥ Thsp then |
21: | found = 1 |
22: | break |
23: | end |
24: | else |
25: | change altitude upward |
26: | end |
27: | end |
28: | end |
29: | else |
30: | move to the next area for another high-altitude search |
31: | end |
32: | roundh = roundh + 1 |
33: | end |
4. Simulation
4.1. Simulation Environment
4.2. Search Scenarios
- Scenario 1This scenario is the initial version of the proposed algorithm. In this method, the first drone searches four high-altitude search areas for quick navigation. Then, the area with the highest probability is selected and searched more precisely by a drone at a low altitude. In this method, control is transferred from the high-altitude drone to the low-altitude drone without verifying control transfer. This method operates based on a hierarchical control of drones.
- Scenario 2In the second scenario, a low-altitude drone performs a linear probability search. Specifically, the drone searches each low-altitude search area linearly. The low-altitude drone moves linearly in the direction in which the values of x and y increase and searches for the target. The values of α and β of the low-altitude drone are applied to obtain the target existence probability. The low-altitude drone continues searching based on the α and β values. The probability of the existence of a target in each cell is calculated recursively utilizing Equation (4).
- Scenario 3The third scenario utilizes another altitude-control strategy to detect a target in the search area. The search scenario is as follows. Unlike Scenario 1, the drone searches only one high-altitude search area. The drone selects a low-altitude search area (2 × 2) within the high-altitude search area and sends the search information to the low-altitude drone for more precise searching of the low-altitude search area. This drone then searches the low-altitude search area in detail.
- Scenario 4In this scenario, a high-altitude drone performs a linear probability search. This scenario is very similar to the second scenario. The only difference is that the drone is at a high altitude. In addition, since it has a higher altitude, different α and β values for high altitude are utilized to calculate the probability of each cell.
- Scenario 5This scenario represents the full method proposed in this study. The high-altitude drone sequentially searches an area corresponding to four times its search range from a high altitude. The search range containing the cell with the highest probability of existence of the target is selected. The drone then checks if the highest probability of target existence is greater than or equal to a threshold value to determine if the search control should be transferred to the low-altitude drone. If the value is above the threshold, the search information is transmitted to the low-altitude drone, which then performs a more precise search at a low altitude.
4.3. Simulation Results and Analysis
5. Conclusions
- This study proposed an improved hierarchical probabilistic target search algorithm based on the collaboration of drones at different altitudes. This method reduced the search time and search travel distance by improving the information transfer between high-altitude and low-altitude drones. In addition, the information transfer method increased the efficiency of the proposed algorithm by using thresholds in the information transmission process.
- This study introduced several drone collaboration scenarios performed by two drones at different altitudes and compared the scenarios to the proposed algorithm. Through simulations, the performance of the proposed algorithm and the cooperation scenarios were analyzed. It was demonstrated that methods utilizing hierarchical searches with drones are comparatively excellent and that the proposed algorithm is approximately 13% more effective than a previous method with much better performance compared to other scenarios.
Funding
Acknowledgments
Conflicts of Interest
References
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Variable | Definition |
---|---|
Areah | search area of drone at high altitude |
Areal | search area of drone at low altitude |
Thsp | threshold probability value for search success |
Thlp | threshold probability value for low-altitude search |
HPh | highest probability among cells at high altitude |
HPl | highest probability among cells at low altitude |
found | binary variable for found alarm |
roundh | number of rounds executed at high altitude |
roundl | number of rounds executed at low altitude |
Category | Value |
---|---|
Size of search area | 8 × 8 units |
Number of drones | 2 |
Average drone speed | 15 km/h |
High-altitude search area of drone | 4 × 4 units (altitude: 20 m) |
Low-altitude search area of drone | 2 × 2 units (altitude: 10 m) |
Threshold probability1 (THsp) | 0.95 |
Threshold probability2 (THlp) | 0.75 |
Length of a side of one unit | 7.592 m |
Probability variables for high-altitude drone | α = 0.00130, β = 0.34593 [17] |
Probability variables for low-altitude drone | α = 0.06286, β = 0.20000 [17] |
Algorithm | Search Method | Total Time (sec) | Total Distance (m) | Comparison (%) |
---|---|---|---|---|
High + Low | After high-altitude search, low-altitude search | 1,099,028 | 4,579,283 | 100 |
Low Linear | Search linearly at low altitude | 2,893,980 | 12,058,251 | 263 |
High + Low2 | After high-altitude search, low-altitude search (search by one high-altitude area) | 1,673,795 | 6,974,146 | 152 |
High Linear | Search linearly at high altitude | 1,139,097 | 4,746,240 | 104 |
High + Low3 (Proposed) | After high-altitude search with threshold value, low-altitude search | 959,327 | 3,997,199 | 87 |
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Ha, I.-K.; Cho, Y.-Z. A Probabilistic Target Search Algorithm Based on Hierarchical Collaboration for Improving Rapidity of Drones. Sensors 2018, 18, 2535. https://doi.org/10.3390/s18082535
Ha I-K, Cho Y-Z. A Probabilistic Target Search Algorithm Based on Hierarchical Collaboration for Improving Rapidity of Drones. Sensors. 2018; 18(8):2535. https://doi.org/10.3390/s18082535
Chicago/Turabian StyleHa, Il-Kyu, and You-Ze Cho. 2018. "A Probabilistic Target Search Algorithm Based on Hierarchical Collaboration for Improving Rapidity of Drones" Sensors 18, no. 8: 2535. https://doi.org/10.3390/s18082535