Application of Fuzzy Logic and PID Controller for Mobile Robot Navigation

Babunski, Darko; Berisha, Jakup; Zaev, Emil; Bajrami, Xhevahir

doi:10.1109/meco49872.2020.9134317

Cited by 10 publications

(8 citation statements)

References 8 publications

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“…Control theory has been commonly applied to implement ground robot navigation over terrains in robotics. Many early methods used fuzzy logic to implement robot control for ground navigation over unstructured terrains (Babunski et al (2020); Adib and Masoumi (2017); Fries (2018)). As these methods didn't consider robot dynamics, methods were proposed to generate navigational controls that also model a robot's dynamics (Meng and Kak (1993); Kelly (1994); Dash et al (2015); Verginis and Dimarogonas (2021); Tsunoda et al (2020)).…”

Section: Control Methods For Ground Robot Navigationmentioning

confidence: 99%

Self-Reflective Terrain-Aware Robot Adaptation for Consistent Off-Road Ground Navigation

Siva¹,

Wigness²,

Rogers³

et al. 2021

Preprint

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Ground robots require the crucial capability of traversing unstructured and unprepared terrains and avoiding obstacles to complete tasks in real-world robotics applications such as disaster response. When a robot operates in off-road field environments such as forests, the robot's actual behaviors often do not match its expected or planned behaviors, due to changes in the characteristics of terrains and the robot itself. Therefore, the capability of robot adaptation for consistent behavior generation is essential for maneuverability on unstructured off-road terrains. In order to address the challenge, we propose a novel method of self-reflective terrain-aware adaptation for ground robots to generate consistent controls to navigate over unstructured off-road terrains, which enables robots to more accurately execute the expected behaviors through robot self-reflection while adapting to varying unstructured terrains. To evaluate our method's performance, we conduct extensive experiments using real ground robots with various functionality changes over diverse unstructured off-road terrains. The comprehensive experimental results have shown that our self-reflective terrain-aware adaptation method enables ground robots to generate consistent navigational behaviors and outperforms the compared previous and baseline techniques.

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Section: Control Methods For Ground Robot Navigationmentioning

confidence: 99%

Self-Reflective Terrain-Aware Robot Adaptation for Consistent Off-Road Ground Navigation

Siva¹,

Wigness²,

Rogers³

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…Traditional PID control algorithms are characterized by their straightforward structures and high levels of popularity; non-etheless, an accurate mathematical model is required in order to provide superior linear system control. PID control is restricted for use with control objects that have high hysteresis and inertia as well as complex signals for tracking, as is common knowledge ( Babunski et al, 2020 ). On the other hand, the control effect that it has on non-linear systems is not ideal.…”

Section: Fuzzy System For Tuning the Pid Gainsmentioning

confidence: 99%

“…Frontiers in Robotics and AI frontiersin.org use with control objects that have high hysteresis and inertia as well as complex signals for tracking, as is common knowledge (Babunski et al, 2020). On the other hand, the control effect that it has on nonlinear systems is not ideal.…”

Section: Fuzzy Controller Systemmentioning

confidence: 99%

A hybrid controller method with genetic algorithm optimization to measure position and angular for mobile robot motion control

et al. 2023

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Due to the complexity of autonomous mobile robot’s requirement and drastic technological changes, the safe and efficient path tracking development is becoming complex and requires intensive knowledge and information, thus the demand for advanced algorithm has rapidly increased. Analyzing unstructured gain data has been a growing interest among researchers, resulting in valuable information in many fields such as path planning and motion control. Among those, motion control is a vital part of a fast, secure operation. Yet, current approaches face problems in managing unstructured gain data and producing accurate local planning due to the lack of formulation in the knowledge on the gain optimization. Therefore, this research aims to design a new gain optimization approach to assist researcher in identifying the value of the gain’s product with a qualitative comparative study of the up-to-date controllers. Gains optimization in this context is to classify the near perfect value of the gain’s product and processes. For this, a domain controller will be developed based on the attributes of the Fuzzy-PID parameters. The development of the Fuzzy Logic Controller requires information on the PID controller parameters that will be fuzzified and defuzzied based on the resulting 49 fuzzy rules. Furthermore, this fuzzy inference will be optimized for its usability by a genetic algorithm (GA). It is expected that the domain controller will give a positive impact to the path planning position and angular PID controller algorithm that meet the autonomous demand.

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“…From the perspective of mobile robots, the real point is to move from a designed starting point to another target point and the need to smoothly avoid obstacles in a pathoptimal manner [4]. Therefore, the navigation of mobile robots is crucial for mobile robots [5]. Sarif and Buniyamin pointed out that path planning is the main problem of mobile robots and an important component of navigation [6].…”

Section: Introduction 1research Backgroundmentioning

confidence: 99%

Improved JPS Path Optimization for Mobile Robots Based on Angle-Propagation Theta* Algorithm

Luo

Qin

et al. 2022

Algorithms

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The Jump Point Search (JPS) algorithm ignores the possibility of any-angle walking, so the paths found by the JPS algorithm under the discrete grid map still have a gap with the real paths. To address the above problems, this paper improves the path optimization strategy of the JPS algorithm by combining the viewable angle of the Angle-Propagation Theta* (AP Theta*) algorithm, and it proposes the AP-JPS algorithm based on an any-angle pathfinding strategy. First, based on the JPS algorithm, this paper proposes a vision triangle judgment method to optimize the generated path by selecting the successor search point. Secondly, the idea of the node viewable angle in the AP Theta* algorithm is introduced to modify the line of sight (LOS) reachability detection between two nodes. Finally, the paths are optimized using a seventh-order polynomial based on minimum snap, so that the AP-JPS algorithm generates paths that better match the actual robot motion. The feasibility and effectiveness of this method are proved by simulation experiments and comparison with other algorithms. The results show that the path planning algorithm in this paper obtains paths with good smoothness in environments with different obstacle densities and different map sizes. In the algorithm comparison experiments, it can be seen that the AP-JPS algorithm reduces the path by 1.61–4.68% and the total turning angle of the path by 58.71–84.67% compared with the JPS algorithm. The AP-JPS algorithm reduces the computing time by 98.59–99.22% compared with the AP-Theta* algorithm.

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Application of Fuzzy Logic and PID Controller for Mobile Robot Navigation

Cited by 10 publications

References 8 publications

Self-Reflective Terrain-Aware Robot Adaptation for Consistent Off-Road Ground Navigation

Self-Reflective Terrain-Aware Robot Adaptation for Consistent Off-Road Ground Navigation

A hybrid controller method with genetic algorithm optimization to measure position and angular for mobile robot motion control

Improved JPS Path Optimization for Mobile Robots Based on Angle-Propagation Theta* Algorithm

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