RUDE-AL: Roped UGV Deployment Algorithm of an MCDPR for Sinkhole Exploration
Abstract
:1. Introduction
2. Related Work
2.1. Mobile CDPRs
2.2. Multi-Objective Optimization
3. Methodology
3.1. Problem Statement
3.2. RUDE-AL Algorithm
- Phase 1. Node-edge fleet configuration
- -
- Map, obstacle, and start selection for offline planning
- -
- Definition, sorting, and choosing of feasible goal candidates based on area coverage and distance cost of fleet deployment
- Phase 2. Roped individual configuration
- -
- Goal local planning and establishment of deployment configuration based on rope restrictions
3.2.1. Phase 1. Node-Edge Fleet Configuration
Map, Obstacle, and Start Selection for Offline Planning
- Erode (line 12), Dilate (line 13), Range (line 14), Contours (line 16) and Centroids (line 18): are OpenCV functions used to inflate regions of images, define masks, and get characteristics of contours.
- Remove_dup (line 28): is a function to delete the multiple occurrences of an object in a list.
- Line(line 35): is a function that returns the slope and the constant “b” for a y = mx + b line between two input points
- Check_click (line 51): is a function that determines where the user clicks and returns the selected zone to explore (options are 1 or 2).
- Route (line 51) is a function that performs the prm path planning between two points. Returns the feasible path between them.
Algorithm 1 Fleet Planning |
Input: map,start Output:
|
Definition, Sorting, and Choice of Feasible Goal Candidates Based on Area Coverage and Distance Cost of Fleet Deployment
- : covered area by points
- : vector between points 1–4
- : vector between points 1–3
- : vector between points 1–2
- : accumulated distance cost of route
- d: euclidean distance between points
- : fitness function
- : weight applied for area coverage
- : weight applied for path distance cost
- : covered by 4-candidate points combination
- : area of obstacle obtained from contours
- : vector of inverse normalized route cost for candidate points
- Get_area (line 13) is a function that gets the area of the contour made by a list of points. The output is the area of the contour.
- Route_cost (line 14) is a function that calculates the accumulated individual distance cost for a list of paths.
- Index_range (line 15) is a function that defines the lower and upper limit indexes for each candidate point according to a minimum distance between points. The output is a range of index for each of the four candidate points.
- Get_candidates (line 16) is a function that defines independent lists of candidate points of a main list according to the given limits. The output is four lists of points.
- Fitness_function (line 17) is a function that iterates testing different combinations according to Ga_instance parameters. It includes the Area_calc() in order to calculate area for every iteration different combination of points.
- Area_calc (line 17) is a function that calculates the area described between four points according to cross product and sum of areas described in Equation (1).
- Ga_instance (line 18) is pygad instance to configure the genetic algorithm that include the parameters described in Table 2. The instance must run using Ga_instance.run. The output of Ga_instance.best_solution() is the best combination of four points according to the fitness function.
Algorithm 2 Points Selection |
Input: Output: sol_points,fleet_path
|
3.2.2. Phase 2. Roped Individual Configuration
Goal Local Planning and Establishment of Deployment Configuration Based on Rope Restrictions
- Route (line: 10) is a function that performs the PRM path planning between two points. It returns the feasible path between them.
- Route_check (line: 14) is a function that creates an interpolated line between input point and every interpolated point of the input route, and checks if there are collisions between the interpolated line (rope) and a positive obstacle. Output is a Boolean true if there is collision, and false if there is no collisions.
- GetRobotPaths (line: 35) is a function in which input is feasibility of each path of the roped configuration deployment check, and it returns the path for each robot according to Table 5.
Algorithm 3 Roped deployment |
Input: Output:
|
4. Experiments
- Maps: The range of maps that are auto-generated with the indicated characteristics.
- : number of random points to connect to generate the Bezier curve.
- : radius around the points where control points are. A larger radius means a sharper feature.
- Smoothness: Parameter to define the smoothness of the curve.
- Scale: X and Y pixel rectangle size where the random points will be generated.
5. Results
5.1. Fitness
5.2. Algorithm Complexity
5.3. Test on Gazebo Simulator
6. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Abbreviations
ANOVA | Analysis of Variance |
ASR | Air Sea Rescue |
CDPR | Cable Driven Parallel Robot |
CPRMC | Cable Parallel robot for multiple mobile cranes |
DARPA | Defense Advanced Research Project Agency |
EA | Evolutionary Algorithms |
GA | Genetic Algorithms |
MCDPR | Mobile Cable-Driven Parallel Robot |
PRM | Probabilistic Roadmap |
ROI | Region of Interest |
RTS | Robotic Total Station |
RUDE-AL | Roped UGV fleet Deployment ALgorithm |
SAR | Search and Rescue |
TS | Total Station |
UGV | Unmanned Ground Vehicle |
USAR | Urban Search and Rescue |
WiSAR | Wilderness SAR |
Appendix A
Appendix B
References
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Reference | Robot Name | Mobile Bases | Application | Environment | Physical Implementation | Number of Robotic Platforms | Degrees of Freedom |
---|---|---|---|---|---|---|---|
[25] | Robocrane | YES | Research | Indoor/Outdoor | YES | 3 | 6 |
[26] | IPAnema | NO | Industry | Indoor | YES | 1 | 6 |
[27] | CoGiro | NO | Research | Indoor | YES | 1 | 6 |
[28] | CableRobot | NO | Research | Indoor | YES | 1 | 6 |
[29] | - | NO | Research | Indoor | YES | 1 | 6 |
[30] | CABLAR | NO | Logistics | Indoor | YES | 1 | 6 |
[31] | FASTKIT | YES | Logistics | Indoor | YES | 2 | 6 |
[32] | MARIONET-CRANE | YES | Rescue | Outdoor | YES | 1 | 6 |
[33] | MoPick | YES | Logistics | Indoor | YES | 4 | 3 |
[34] | - | YES | Research | Outdoor | NO | 4 | 4 |
[35] | - | YES | Research | Outdoor | NO | 3 | 3 |
[36] | - | YES | Research | Outdoor | NO | 3 | 3 |
Tipo | RGB Color | UGVs Navigation Allowed | Cable Cross Allowed |
---|---|---|---|
Free space | (255, 255, 255) | YES | YES |
Positive obstacle | (136, 138, 133) | NO | NO |
Negative obstacle | (0, 0, 0) | NO | YES |
Parameter | Definition | Value |
---|---|---|
num_generations | Number of generations | 1200 |
mutation_num_genes | Number of genes por instance random_mutation() | 4 |
num_parents_mating | Number of solutions to be selected as parents | 2 |
sol_per_pop | Number of solutions in the population | 70 |
num_genes | Number of genes in each solution | 4 |
fitness_function | Fitness function | |
init_range_low | Lower value of random range where initial population is selected. | 0 |
init_range_high | Upper value of random range where initial population is selected. | len(candidate1)/2 |
crossover_type | Type of crossover operation. | “single_point” |
random_mutation_min_val | Start value of the range from which a random value is selected to be added to the gene. | 1 |
random_mutation_max_val | End value of the range from which a random value is selected to be added to the gene. | 100 |
mutation_type | Type of mutation operation | “random” |
gene_space | Specify the posible values for each gene in order to restrict the gene values. | [range (0, len(candidate1)), range (0, len(candidate2)), range (0, len(candidate3)), range (0, len(candidate4)) ] |
gene_type | Gene type (numeric data type) | int |
Point | 1st Feasible Path | 2nd Feasible Path | 3rd Feasible Path | Feasible Configuration |
---|---|---|---|---|
A | A →(,,) | |||
A →(,,) | ||||
A →(,,) | ||||
A →(,,) | ||||
B | B →(,,) | |||
B →(,,) | ||||
B →(,,) | ||||
B →(,,) | ||||
C | C →(,,) | |||
C →(,,) | ||||
C →(,,) | ||||
C →(,,) | ||||
D | D →(,,) | |||
D →(,,) | ||||
D →(,,) | ||||
D →(,,) |
Principal Node Point | Feasible Configuration | Robot 1 Path | Robot 2 Path | Robot 3 Path | Robot 4 Path |
---|---|---|---|---|---|
A | type 1 | FP+ADCB | FP + ADC | FP + AD | FP |
type 2 | FP + ABCD | FP + ABC | FP + AB | FP | |
type 3 | FP + ADC | FP + AD | FP + AB | FP | |
type 4 | FP + ABC | FP + AB | FP + AD | FP | |
B | type 1 | FP + BADC | FP + BAD | FP + BA | FP |
type 2 | FP + BCDA | FP + BCD | FP + BC | FP | |
type 3 | FP + BAD | FP + BA | FP + BC | FP | |
type 4 | FP + BCD | FP + BC | FP + BA | FP | |
C | type 1 | FP + CBAD | FP + CBA | FP + CB | FP |
type 2 | FP + CDAB | FP + CDA | FP + CD | FP | |
type 3 | FP + CBA | FP + CB | FP + CD | FP | |
type 4 | FP + CDA | FP + CD | FP + CB | FP | |
D | type 1 | FP + DCBA | FP + DCB | FP + DC | FP |
type 2 | FP + DABC | FP + DAB | FP + DA | FP | |
type 3 | FP + DCB | FP + DC | FP + DA | FP | |
type 4 | FP + DAB | FP + DA | FP + DC | FP |
Map | Number of Positive Obstacles | Number of Negative Obstacles | Number of Positive Obstacles around ROI | Shape of Negative Obstacle |
---|---|---|---|---|
1 | 0 | 1 | 0 | Irregular |
2 | 4 | 1 | 1 | Irregular |
3 | 5 | 1 | 2 | Irregular |
4 | 8 | 1 | 0 | Ellipse |
5 | 12 | 6 | 0 | Ellipse |
6 | 17 | 34 | 0 | Irregular ellipse |
Maps | Number of Positive Obstacles around ROI | Smoothness | Scale | ||
---|---|---|---|---|---|
1–10 | 0–2 | 0.2 | 0.05 | 6 | 500 |
11–20 | 0–2 | 0.3 | 0.08 | 7 | 250 |
21–30 | 0–2 | 0.1 | 0.1 | 8 | 300 |
Experiment | Number of Maps | Number of Experiments | Number of Successful Experiments | Successful Rate |
---|---|---|---|---|
Global TEST | 6 | 24 | 24 | 100% |
SHAPE TEST (feasible paths) | 90 | 360 | 357 | 99.16% |
SHAPE TEST (feasible paths with workspace limitation) | 90 | 360 | 352 | 97.78% |
SHAPE TEST (feasible paths with collision risk) | 90 | 360 | 350 | 97.2% |
SHAPE TEST (feasible paths with exceptions) | 90 | 360 | 354 | 98.33% |
SHAPE TEST | 90 | 360 | 333 | 92.5% |
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Orbea, D.; Cruz Ulloa, C.; Del Cerro, J.; Barrientos, A. RUDE-AL: Roped UGV Deployment Algorithm of an MCDPR for Sinkhole Exploration. Sensors 2023, 23, 6487. https://doi.org/10.3390/s23146487
Orbea D, Cruz Ulloa C, Del Cerro J, Barrientos A. RUDE-AL: Roped UGV Deployment Algorithm of an MCDPR for Sinkhole Exploration. Sensors. 2023; 23(14):6487. https://doi.org/10.3390/s23146487
Chicago/Turabian StyleOrbea, David, Christyan Cruz Ulloa, Jaime Del Cerro, and Antonio Barrientos. 2023. "RUDE-AL: Roped UGV Deployment Algorithm of an MCDPR for Sinkhole Exploration" Sensors 23, no. 14: 6487. https://doi.org/10.3390/s23146487