Path Planning and Motion Control of Indoor Mobile Robot under Exploration-Based SLAM (e-SLAM)
Abstract
:1. Introduction
2. Exploration-Based SLAM (e-SLAM) Method
3. Path Planning
3.1. Floor Voronoi Section and Path Search
3.2. Path Design
3.3. Special Case for Path Design
- Turn-in point limit fails when track-out point limit is satisfied: As shown in Figure 5a, we may find an intermediate pose above to first achieve the G02/G03 cornering motion from to and proceed to a normal cornering motion from to decreasing the complementary angle .
- Turn-in point limit is satisfied when track-out point limit fails: As shown in Figure 5b, we may find an intermediate pose to first achieve the G03/G02 cornering motion from to and proceed to a normal cornering motion from to increasing the complementary angle .
- Both the turn-in point limit and the track-out point limit fail: As shown in Figure 5c, we may find the set of intermediate poses , , and that first achieve the G03/G02 cornering motion from to connecting by a G01 linear motion from to and proceed to G02/G03 cornering motion from to followed by a normal cornering motion from to .
- The complementary angle is very small: As shown in Figure 5d, if the adjacent vertices and are nearly collinear, then the normal cornering motion may still fail. In this case, we can determine the inflection point to achieve a lane-change motion which consists of one G03/G02 motion followed by the other G02/G03.
4. Motion Interpolation
4.1. Linear Segment Interpolation
4.2. Arc Segment Interpolation
4.3. Transition Curve
5. IMR Discrete Control Scheme
5.1. Vehicle Command Generation
5.2. LiDAR-Based Image Segmentation
- Determining the initial rotation using the minimum bounding box method.
- Forming the LiDAR meshes for the walls vertical to the floor.
- Determining the most significant corner (MSC) from the vertical walls based on the histogram analysis.
- According to the base LiDAR pose frame, referred to as the floor LiDAR origin, performing the inverse transformation to map all LiDAR data back to the room map.
- According to the rotation and translation of the LiDAR pose scheme relative to the floor LiDAR origin, performing the active localization.
- According to the least quadratic estimation (LQE) method associated with the IMR steering and traction control as stated in Equation (26), performing the localization estimation.
- According to the LiDAR data XY projection image, performing the IMR translation update.
- Performing the room segmentation and floor management.
6. Implementation and Experiment
6.1. Sidomotion Software
6.2. LiDAR Localization Software
6.3. Experiment
7. Discussion
- (1)
- The vehicle speed planning including the maximum linear speed and the corner turning speed; the LiDAR localization can fail due to the speed command higher than 200 cm/s which is 8 kmph. In the 10 Hz sampling, the displacement of the IMR is 20 cm per frame, which may deteriorate the orientation judgment of the IMR localization. Therefore, it is recommended that the speed of 100 cm/s yields the best robustness.
- (2)
- The height of the LiDAR; it is preferred to set it slightly higher than the average human height around 180 cm because of the upper eight rings and the lower eight rings of the LiDAR that are used to perform the IMR localization and to detect obstacles. To obtain a higher resolution of the IMR localization, we can also select nine upper rings for the localization and seven lower rings for obstacle detection. This situation can happen in the case of a long corridor where the horizontal wall is at a distance of 20 m away. On the 20 m away wall, the two-degree ring spacing is mapped into 70 cm height.
- (3)
- The obstacle angle and distance; there is an oval cone looking front used to detect the obstacle, and the width of the cone is determined by the obstacle angle which is set to be . A larger obstacle angle can cause the IMR to be blocked by the side walls. The obstacle detection distance is recommended to be 80 cm.
- (4)
- The Sidomotion command generation; the command always brings the IMR forward on the path, and thus we have to choose the number of interpolation points ahead on the path planned to bring the IMR forward. A command produced from a small increment from the current pose can slow down the IMR navigation; however, the IMR can lose control when the increment is large. Empirically, we will set the current IMR control command into the next time tick achievable, making it speed-dependent. For instance, the command position is 40 cm ahead of the current pose when the speed is 100 cm/s.
- I.
- Floor manager, which converts the floor plan into a Voronoi map for the Sido (Service In Door Operation) motion service use.
- II.
- Service manager, which creates the Sido motion planning due to the assigned task.
- III.
- Camera manager, which is not included in this paper.
- IV.
- LiDAR manager, which sets up the LiDAR parameters and performs the e-SLAM localization.
- V.
- Motor manager, which sets up control parameters for both steering and traction motors and performs real-time communication with the EtherCAT motor driver.
- VI.
- IMR HMI, which provides the human–machine interface to the user.
8. Conclusions
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
Appendix A
Powertrain Systems |
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Chassis and Steering Systems |
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Electronic Systems |
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Physical Specifications |
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Roy, R.; Tu, Y.-P.; Sheu, L.-J.; Chieng, W.-H.; Tang, L.-C.; Ismail, H. Path Planning and Motion Control of Indoor Mobile Robot under Exploration-Based SLAM (e-SLAM). Sensors 2023, 23, 3606. https://doi.org/10.3390/s23073606
Roy R, Tu Y-P, Sheu L-J, Chieng W-H, Tang L-C, Ismail H. Path Planning and Motion Control of Indoor Mobile Robot under Exploration-Based SLAM (e-SLAM). Sensors. 2023; 23(7):3606. https://doi.org/10.3390/s23073606
Chicago/Turabian StyleRoy, Rohit, You-Peng Tu, Long-Jye Sheu, Wei-Hua Chieng, Li-Chuan Tang, and Hasan Ismail. 2023. "Path Planning and Motion Control of Indoor Mobile Robot under Exploration-Based SLAM (e-SLAM)" Sensors 23, no. 7: 3606. https://doi.org/10.3390/s23073606
APA StyleRoy, R., Tu, Y. -P., Sheu, L. -J., Chieng, W. -H., Tang, L. -C., & Ismail, H. (2023). Path Planning and Motion Control of Indoor Mobile Robot under Exploration-Based SLAM (e-SLAM). Sensors, 23(7), 3606. https://doi.org/10.3390/s23073606