SMURF: A Fully Autonomous Water Surface Cleaning Robot with A Novel Coverage Path Planning Method
Abstract
:1. Introduction
- We design a new robot that achieves fully autonomous water surface cleaning and significantly increases cleaning efficiency.
- We propose a novel CPP method for water surface cleaning and design an improved NMPC for water surface cleaning robots.
- We conduct real-world experiments in various water bodies to test the cleaning performance of SMURF.
2. Related Work
2.1. Water Surface Robot
2.2. Coverage Path Planning
3. Water Surface Cleaning Robot Design
3.1. Hull Design
3.2. Hardware
3.3. Working Procedure
4. Autonomous System
4.1. Autonomous System Framework
4.2. Water Surface Coverage Path Planning
Algorithm 1 The WSCPP algorithm (for regular region). | |
Input: Regular region with coordinates of boundary points in the region , width of single effective cleaning d | |
Output: Optimal trajectory | |
1: | Define set of trajectories |
2: | for do |
3: | Compute the scan direction parallel to edge line |
4: | Generate multiple lines by translating line by space d until there are no more parallel line intersecting with the region edges. The generated set of lines |
5: | Define back-and-forth trajectory line set |
6: | for do |
7: | Calculate the intersection point set between and region edges |
8: | if then |
9: | Inverse points sequence |
10: | end if |
11: | Add into |
12: | end for |
13: | Add into W |
14: | end for |
15: |
Algorithm 2 The WSCPP algorithm (for irregular region). | |
Input: Irregular region with coordinates of boundary points in the region } width of single effective cleaning d, obstacle set | |
Output: Optimal trajectory | |
1: | |
2: | Combine the points in with points in as point set P |
3: | Apply triangulation to P and get triangle set [35]. Delete the triangles that are contained in the obstacles, and get the final triangle set T |
4: | Combine triangles in set T into multiple convex polygons, and get the sub-region set |
5: | Choose the sub-region that contains original boundary points and closest to the start point of the robot as the first sub-region , and . |
6: | while do |
7: | Delete from B |
8: | Generate coverage planning path using Algorithm 1. |
9: | if then |
10: | Define line x as the line connecting the end point of to the first point of . |
11: | Get all the obstacles that have intersection points with x, and sort the obstacles from the shortest to the longest according to their distances to the end point of , and obstacles set is . |
12: | Define the path bypassing the obstacles as u |
13: | for do |
14: | Calculate the intersection points of x and |
15: | Divide the boundary of into two parts by points |
16: | if both and are inside of the region then |
17: | Add the shorter one of and into u |
18: | else |
19: | Add one of and that is inside of the region into u |
20: | end if |
21: | Add u into |
22: | end for |
23: | end if |
24: | Add trajectory point set into |
25: | if there are sub-regions adjacent in B then |
26: | Set as the sub-region adjacent and closest to the end point of . |
27: | else |
28: | Set as the sub-region closest to the end point of . |
29: | end if |
30: | end while |
4.3. Improved NMPC
- In the process of operation, the mass of SMURF increases when collecting garbage, namely, , , , are time-varying matrix.
- In the garbage collection process, the distribution of garbage in the trash container is unknown. The nonlinear resistance matrix will change, which will cause an obvious deviation.
- Over time, the motor will age and wear out, which will result in a lower control input force .
5. Experiment and Evaluation
5.1. Water Surface Coverage Path Planning
5.2. Trajectory Tracking Evaluation
5.3. Water Surface Cleaning Performance Evaluation
6. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Abbreviations
CPP | Coverage Path Planning |
GNSS | Global Navigation Satellite System |
IMU | Inertial Measurement Unit |
mmWave | Millimeter Wave Radar |
NMPC | Nonlinear Model Predictive Controller |
USV | Unmanned Surface Vehicle |
UAV | Unmanned Aerial Vehicles |
WSCPP | Water Surface Coverage Path Planning |
RTK | Real-Time Kinematic |
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Items | Characteristics |
---|---|
Length × Height × Width | 2.5 m × 1.6 m × 0.8 m |
Weight | 100 kg |
Trash Payload | 40 kg |
Maximum Speed | 1.6 m/s |
Height of Center of Gravity | 0.25 m |
Items | Characteristics |
---|---|
Main processor | Nvidia Xavier NX |
Sensor | RGB camera, mmWave radar, GNSS, IMU |
Power Supply | 24 V 140 AH lithium battery |
Control Mode | Automatic / 2.4 G Wireless / 4 G Network |
Running Time | 8 h |
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Zhu, J.; Yang, Y.; Cheng, Y. SMURF: A Fully Autonomous Water Surface Cleaning Robot with A Novel Coverage Path Planning Method. J. Mar. Sci. Eng. 2022, 10, 1620. https://doi.org/10.3390/jmse10111620
Zhu J, Yang Y, Cheng Y. SMURF: A Fully Autonomous Water Surface Cleaning Robot with A Novel Coverage Path Planning Method. Journal of Marine Science and Engineering. 2022; 10(11):1620. https://doi.org/10.3390/jmse10111620
Chicago/Turabian StyleZhu, Jiannan, Yixin Yang, and Yuwei Cheng. 2022. "SMURF: A Fully Autonomous Water Surface Cleaning Robot with A Novel Coverage Path Planning Method" Journal of Marine Science and Engineering 10, no. 11: 1620. https://doi.org/10.3390/jmse10111620