Navigation of a Differential Wheeled Robot Based on a Type-2 Fuzzy Inference Tree
Abstract
:1. Introduction
- A type-2 fuzzy inference tree is proposed as real-time motion planning in the mobile robot navigation problem.
- Type-2 fuzzy theory is the theoretical support of the current system, which makes it different from common fuzzy controllers used in mobile robotics, where type-1 fuzzy theory is used.
- The tree scheme is suggested as a solution to reduce the computational complexity, by the delegation of tasks, specifically, obstacle avoidance, path recovering, and goal reaching, as well as behavior coordination.
- The proposal is fully functional on physical robotic platforms, in contrast with many state-of-the-art approaches that are computational simulations.
- The performance of the proposed system was evaluated on a real environment with a smooth floor and rough surfaces.
2. Materials and Methods
2.1. System Composition
2.2. Laser Range Scanner Measures
2.3. Odometry
2.4. Proposed Type-2 Fuzzy Inference Tree
2.5. Mathematical Support of the Proposal
2.5.1. Fuzzifier
2.5.2. Rule Base
2.5.3. Fuzzy Inference Engine
2.5.4. Type-Reduction
2.5.5. Defuzzification
3. Experimental Results
- Clearance: this metric is related to the distance from the trajectory points to the closest obstacle, and it is defined as
- Path smoothness: the smoothness of a path refers to the amplitude of the angles that are described while the robot follows the trajectory
- Path length: is defined as the sum of the distances from one way point to the next one in the planner state space.
- Travel time: is the time span that the robot travels the entire path from the start to the goal.
- Success rate: is equal to the percentage of times an algorithm is able to find a valid solution.
3.1. Experiment 1: Navigation on an Obstacles-Free Environment
3.2. Experiment 2: Navigation on a Static Obstacles Environment
4. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Method | Clearance (m) | Path Smoothness (rad) | Path Length (m) | Travel Time (s) | Success Rate |
---|---|---|---|---|---|
Hybrid AStar | 0.413 ± 0.064 | 0.602 ± 0.132 | 22.687 ± 0.840 | 449 ± 41 | |
RRT | 0.355 ± 0.082 | 2.336 ± 0.615 | 24.941 ± 1.413 | 418 ± 33 | |
BiRRT | 0.409 ± 0.091 | 0.826 ± 0.265 | 30.019 ± 2.534 | 396 ± 26 | |
PRM | 0.399 ± 0.073 | 1.984 ± 0.478 | 26.790 ± 1.657 | 280 ± 19 | |
Proposed | 0.503 ± 0.058 | 0.236 ± 0.097 | 18.289 ± 0.283 | 403 ± 15 |
Method | Clearance (m) | Path Smoothness (rad) | Path Length (m) | Travel Time (s) | Success Rate |
---|---|---|---|---|---|
Hybrid AStar | 0.390 ± 0.075 | 0.911 ± 0.242 | 29.182 ± 1.222 | 538 ± 46 | |
RRT | 0.297 ± 0.088 | 2.616 ± 0.607 | 31.724 ± 2.616 | 495 ± 37 | |
BiRRT | 0.201 ± 0.096 | 2.983 ± 0.624 | 32.614 ± 2.977 | 428 ± 28 | |
PRM | 0.368 ± 0.072 | 3.421 ± 0.712 | 34.007 ± 3.150 | 345 ± 23 | |
Proposed | 0.472 ± 0.047 | 0.569 ± 0.186 | 27.652 ± 0.535 | 570 ± 18 |
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Mújica-Vargas, D.; Vela-Rincón, V.; Luna-Álvarez, A.; Rendón-Castro, A.; Matuz-Cruz, M.; Rubio, J. Navigation of a Differential Wheeled Robot Based on a Type-2 Fuzzy Inference Tree. Machines 2022, 10, 660. https://doi.org/10.3390/machines10080660
Mújica-Vargas D, Vela-Rincón V, Luna-Álvarez A, Rendón-Castro A, Matuz-Cruz M, Rubio J. Navigation of a Differential Wheeled Robot Based on a Type-2 Fuzzy Inference Tree. Machines. 2022; 10(8):660. https://doi.org/10.3390/machines10080660
Chicago/Turabian StyleMújica-Vargas, Dante, Viridiana Vela-Rincón, Antonio Luna-Álvarez, Arturo Rendón-Castro, Manuel Matuz-Cruz, and José Rubio. 2022. "Navigation of a Differential Wheeled Robot Based on a Type-2 Fuzzy Inference Tree" Machines 10, no. 8: 660. https://doi.org/10.3390/machines10080660