Figure 1.
Examples of diverse challenges of the decision-making algorithm, classified into logistical, traffic management, and safety.
Figure 1.
Examples of diverse challenges of the decision-making algorithm, classified into logistical, traffic management, and safety.
Figure 2.
Drone airline scenario with three components: base station, numerous drones, and a communication network.
Figure 2.
Drone airline scenario with three components: base station, numerous drones, and a communication network.
Figure 3.
The safety zones of a drone during flight comprise three levels. The first level A (red) and third level C (green) zones remain constant for simplicity, while the second zone B (orange) is adaptable and varies based on the drone’s flying behavior. Note that the schematic representation of the zones has been intentionally sketched at a reduced scale for clarity.
Figure 3.
The safety zones of a drone during flight comprise three levels. The first level A (red) and third level C (green) zones remain constant for simplicity, while the second zone B (orange) is adaptable and varies based on the drone’s flying behavior. Note that the schematic representation of the zones has been intentionally sketched at a reduced scale for clarity.
Figure 4.
Two drones in a state of conflict, wherein one drone has high velocity, thereby requiring an expanded safety zone. The blue priority is assigned to the faster drone on the right, while the red priority is assigned to the slower drone on the left.
Figure 4.
Two drones in a state of conflict, wherein one drone has high velocity, thereby requiring an expanded safety zone. The blue priority is assigned to the faster drone on the right, while the red priority is assigned to the slower drone on the left.
Figure 5.
Fuzzy logic framework for score calculation using multiple parameters [
56].
Figure 5.
Fuzzy logic framework for score calculation using multiple parameters [
56].
Figure 6.
Constructing the scoring algorithm based on the configuration that serves as the fundamental element for decision making.
Figure 6.
Constructing the scoring algorithm based on the configuration that serves as the fundamental element for decision making.
Figure 7.
This flowchart depicts various decision-making processes. The boxes represent the decision-making steps, and the inputs and outputs represent the parameters. The circles inside the boxes indicate whether the decision is event-based (E) or time/frequency-based (T).
Figure 7.
This flowchart depicts various decision-making processes. The boxes represent the decision-making steps, and the inputs and outputs represent the parameters. The circles inside the boxes indicate whether the decision is event-based (E) or time/frequency-based (T).
Figure 8.
The membership functions for the three parameters are depicted: velocity, connection, and payload. The membership function in blue represents the correspondence to the linguistic variable low, orange the correspondence to mid, and green the correspondence to high.
Figure 8.
The membership functions for the three parameters are depicted: velocity, connection, and payload. The membership function in blue represents the correspondence to the linguistic variable low, orange the correspondence to mid, and green the correspondence to high.
Figure 9.
The influence (green, orange, and blue) of applying two different implication methods on the output fuzzy set (dashed).
Figure 9.
The influence (green, orange, and blue) of applying two different implication methods on the output fuzzy set (dashed).
Figure 10.
The aggregation of the resulting membership function of the implication into one membership function using three different methods.
Figure 10.
The aggregation of the resulting membership function of the implication into one membership function using three different methods.
Figure 11.
Evaluation of the safety zone scoring depending on different parameters using the product method for implication, conjunction method for aggregation, and center of gravity method for defuzzification.
Figure 11.
Evaluation of the safety zone scoring depending on different parameters using the product method for implication, conjunction method for aggregation, and center of gravity method for defuzzification.
Figure 12.
The membership functions for the three parameters: battery, importance, and payload. The membership function in blue represents the correspondence to the linguistic variable low, orange the correspondence to mid, and green the correspondence to high.
Figure 12.
The membership functions for the three parameters: battery, importance, and payload. The membership function in blue represents the correspondence to the linguistic variable low, orange the correspondence to mid, and green the correspondence to high.
Figure 13.
The impact (green, orange, and blue) of utilizing two implication methods on the output fuzzy set (dashed).
Figure 13.
The impact (green, orange, and blue) of utilizing two implication methods on the output fuzzy set (dashed).
Figure 14.
Combining the resulting membership function of the implication into a single membership function using three different methods.
Figure 14.
Combining the resulting membership function of the implication into a single membership function using three different methods.
Figure 15.
Evaluation of the priorities depending on different parameters using the product method for implication, conjunction method for aggregation, and center of gravity method for defuzzification.
Figure 15.
Evaluation of the priorities depending on different parameters using the product method for implication, conjunction method for aggregation, and center of gravity method for defuzzification.
Figure 16.
The membership functions for the three parameters: density, distance, and battery. The membership function in blue represents the correspondence to the linguistic variable low, orange the correspondence to mid, and green the correspondence to high.
Figure 16.
The membership functions for the three parameters: density, distance, and battery. The membership function in blue represents the correspondence to the linguistic variable low, orange the correspondence to mid, and green the correspondence to high.
Figure 17.
The influence of using two implication methods is represented by distinct colors, each corresponding to one membership on the original output fuzzy set (dashed).
Figure 17.
The influence of using two implication methods is represented by distinct colors, each corresponding to one membership on the original output fuzzy set (dashed).
Figure 18.
The consolidation of the resulting membership function from the implication into a single membership function using three distinct methods.
Figure 18.
The consolidation of the resulting membership function from the implication into a single membership function using three distinct methods.
Figure 19.
Evaluation of the route selection scoring depending on different parameters using the product method for implication, conjunction method for aggregation, and center of gravity method for defuzzification.
Figure 19.
Evaluation of the route selection scoring depending on different parameters using the product method for implication, conjunction method for aggregation, and center of gravity method for defuzzification.
Figure 20.
A drone conflict scenario depicted in 2D, illustrating varying sizes of safety zones. The path marked in red corresponds to drone 1, while the one in blue corresponds to drone 2.
Figure 20.
A drone conflict scenario depicted in 2D, illustrating varying sizes of safety zones. The path marked in red corresponds to drone 1, while the one in blue corresponds to drone 2.
Figure 21.
Five distinct route suggestions are proposed for a delivery drone, each differing in safety levels and distance to the target. The starting location is denoted by A, while B represents the target.
Figure 21.
Five distinct route suggestions are proposed for a delivery drone, each differing in safety levels and distance to the target. The starting location is denoted by A, while B represents the target.
Figure 22.
Evaluation of the two algorithms in 100 scenarios using a battery level of 50% and randomly sampled values of the route parameters (urban density, distance) for each route. (a) has been zoomed in to detail the characteristics of the selected routes in (b).
Figure 22.
Evaluation of the two algorithms in 100 scenarios using a battery level of 50% and randomly sampled values of the route parameters (urban density, distance) for each route. (a) has been zoomed in to detail the characteristics of the selected routes in (b).
Figure 23.
Boxplot evaluation of the two algorithms in 100 scenarios. We sample the route parameters (urban density, distance) from a uniform distribution on the interval [0,100) and sweep the parameter battery level. Circles represent outliers.
Figure 23.
Boxplot evaluation of the two algorithms in 100 scenarios. We sample the route parameters (urban density, distance) from a uniform distribution on the interval [0,100) and sweep the parameter battery level. Circles represent outliers.
Table 1.
Classification of several drone parameters: categorization into predefined, externally influenced, and sensor-derived internal metrics crucial for the optimization of drone operations.
Table 1.
Classification of several drone parameters: categorization into predefined, externally influenced, and sensor-derived internal metrics crucial for the optimization of drone operations.
| Parameter | Domain | Unit | Note |
---|
Pre-defined | Payload importance | | % | Indicates significance. |
Task complexity | | % | Level of difficulty of task. |
Payload sensitivity | | % | Susceptibility to external factors. |
Battery life thresholds | | % | Thresholds for charging. |
Task duration | | minutes | Time to complete mission. |
External | Air traffic density | | - | Volume of air traffic. |
Weather conditions | | - | Current atmospheric state. |
Urban density | | - | Population concentration. |
Communication SINR | | dB | Signal-to-noise ratio. |
Communication RSRQ | | dB | Reference signal received quality. |
Communication RSRP | | dB | Reference signal received power. |
Internal | Longitude | | degrees | Precise location data. |
Latitude | | degrees | Precise location data. |
Height | | m | Height of the drone. |
Obstacle distance | | m | Distance from obstacles. |
Battery life | | % | Remaining battery life. |
Lidar data | | m | Scanning information. |
Flight speed | | | Current velocity of drone. |
Table 2.
Examples of fuzzy membership functions. The functions utilize the variable
x to denote the input, while the remaining parameters primarily control the function’s center and width [
57].
Table 2.
Examples of fuzzy membership functions. The functions utilize the variable
x to denote the input, while the remaining parameters primarily control the function’s center and width [
57].
Function | Definition |
---|
Sigmoid | |
Gaussian | |
Linear | |
Triangular | |
Trapezoidal | |
Table 3.
Examples of defuzzification functions that determine the crisp output .
Table 3.
Examples of defuzzification functions that determine the crisp output .
Function | Definition |
---|
Centroid | |
Mean-of-Maximum | with |
Center-of-Area | |
Table 4.
Evaluation of the conflicting scenario using the safety zone and the priority calculated by fuzzy-logic-based decision making. Here, five parameters are used: connection quality, velocity, battery level, importance of cargo, and payload.
Table 4.
Evaluation of the conflicting scenario using the safety zone and the priority calculated by fuzzy-logic-based decision making. Here, five parameters are used: connection quality, velocity, battery level, importance of cargo, and payload.
Time | Drone | Parameter Values | Scores |
---|
Conn. | Velocity | Payload | Battery | Cargo | Zone | Priority |
---|
| Drone 1 | 18.864 | 58.242 | 38.026 | 81.661 | 50.00 | 57.545 | 30.508 |
Drone 2 | 10.074 | 57.242 | 89.755 | 57.102 | 90.00 | 74.129 | 74.821 |
| Drone 1 | 16.634 | 56.312 | 38.026 | 76.121 | 50.00 | 61.223 | 38.275 |
Drone 2 | 17.139 | 58.560 | 89.755 | 52.672 | 90.00 | 73.88 | 75.056 |
| Drone 1 | 29.023 | 0.000 | 38.026 | 71.837 | 50.00 | 31.302 | 42.981 |
Drone 2 | 29.023 | 58.352 | 89.755 | 47.550 | 90.00 | 73.877 | 75.111 |
| Drone 1 | 18.023 | 59.739 | 38.026 | 66.503 | 50.00 | 58.867 | 44.178 |
Drone 2 | 17.189 | 58.691 | 89.755 | 42.312 | 90.00 | 73.881 | 75.118 |
Table 5.
Evaluation of the priority using the baseline and fuzzy logic scoring algorithm (FLSA) with normalized input parameters. Here, three parameters are used: battery level, importance of cargo, and payload. First, we compare the behavior of prioritization with similar parameter values and then with strongly different parameters.
Table 5.
Evaluation of the priority using the baseline and fuzzy logic scoring algorithm (FLSA) with normalized input parameters. Here, three parameters are used: battery level, importance of cargo, and payload. First, we compare the behavior of prioritization with similar parameter values and then with strongly different parameters.
Index | Similar Parameter Values | Scores |
Battery | Importance | Payload | Baseline | FLSA |
1 | 28.622 | 73.720 | 82.345 | 83.667 | 59.799 |
23.622 | 73.720 | 82.345 | 83.667 | 65.499 |
2 | 38.237 | 64.273 | 48.379 | 51.905 | 51.629 |
38.237 | 59.273 | 48.379 | 51.905 | 51.725 |
3 | 51.601 | 22.634 | 19.328 | 14.279 | 50.686 |
51.601 | 22.634 | 14.328 | 14.279 | 50.427 |
Index | Diverse Parameter Values | Scores |
Battery | Importance | Payload | Baseline | FLSA |
4 | 18.622 | 58.622 | 96.498 | 83.667 | 65.854 |
48.622 | 58.622 | 96.498 | 0.0 | 50.291 |
5 | 80.238 | 54.273 | 16.723 | 14.279 | 48.528 |
80.238 | 84.273 | 16.723 | 14.279 | 50.533 |
6 | 94.494 | 36.767 | 9.328 | 14.279 | 31.976 |
94.494 | 36.767 | 39.328 | 51.905 | 47.239 |
Table 6.
Evaluation of the route selection using the baseline and FLSA with normalized input parameters. Route 1: red; route 2: orange; route 3: blue; route 4: green; and route 5: yellow. The highest scores, highlighted in bold, represent the selected route.
Table 6.
Evaluation of the route selection using the baseline and FLSA with normalized input parameters. Route 1: red; route 2: orange; route 3: blue; route 4: green; and route 5: yellow. The highest scores, highlighted in bold, represent the selected route.
Index | Parameters | Scores (b = 5%) | Scores (b = 20%) | Scores (b = 35%) |
Density | Distance | Baseline | FLSA | Baseline | FLSA | Baseline | FLSA |
1 | 100 | 19.20 | 47.5 | 39.295 | 47.5 | 38.747 | 43.784 | 46.963 |
2 | 71 | 34.96 | 0.0 | 37.532 | 0.0 | 37.532 | 0.0 | 54.755 |
3 | 49 | 48.26 | 6.333 | 7.8 | 6.333 | 8.516 | 93.0 | 42.561 |
4 | 28 | 72.82 | 6.333 | 6.333 | 6.333 | 6.333 | 30.0 | 24.89 |
5 | 8 | 100 | 6.333 | 6.333 | 6.333 | 6.333 | 30.0 | 21.901 |
Index | Parameters | Scores (b = 50%) | Scores (b = 65%) | Scores (b = 80%) |
Density | Distance | Baseline | FLSA | Baseline | FLSA | Baseline | FLSA |
1 | 100 | 19.20 | 43.784 | 46.905 | 43.784 | 36.53 | 6.333 | 6.333 |
2 | 71 | 34.96 | 0.0 | 45.09 | 0.0 | 41.395 | 70.0 | 14.022 |
3 | 49 | 48.26 | 93.0 | 71.543 | 93.0 | 46.72 | 43.784 | 30.0 |
4 | 28 | 72.82 | 30.0 | 44.522 | 30.0 | 54.063 | 66.673 | 65.616 |
5 | 8 | 100 | 30.0 | 36.783 | 30.0 | 52.858 | 66.673 | 71.667 |