An Intelligent Hybrid Control to Enhance Applicability of Mobile Robots in Cluttered Environments

Zahid, Taimoor; Kausar, Zareena; Shah, Muhammad Faizan; Saeed, Muhammad; Pan, Jianfei

doi:10.1109/access.2021.3068988

Cited by 6 publications

(3 citation statements)

References 31 publications

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“…Liu X. et al [13] used the grid method to establish a robot navigation map, but ignored the size of the robot. Reference [14] considers the size of the robots and includes it in the size of obstacles for environmental modeling based on grid maps to avoid collisions between robots and obstacles. Alireza M. et al [15] constructed a grid-based map for path planning algorithms, which can determine collision-free trajectories from the initial position to the target position.…”

Section: Environmental Map Constructionmentioning

confidence: 99%

Map Construction and Path Planning Method for Mobile Robots Based on Collision Probability Model

Li,

Tang,

Zhang

et al. 2023

Symmetry

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A map construction method based on a collision probability model and an improved A* algorithm is proposed to address the issues of insufficient security in mobile robot map construction and path planning in complex environments. The method is based on modeling the asymmetry of paths, which complicates problem solving. Firstly, this article constructs a collision probability function model, and based on this model it is fused with the obstacle grid map, which is based on the grid method, to draw a collision probability grid map (CPGM) containing collision probability information. Secondly, incorporating the collision probability values from the CPGM into the actual cost function of the traditional A* algorithm improves the security of path planning in complex environments. The experimental results show that the improved A* algorithm decreases the percentage of dangerous nodes in complex environments by 69.23%, shortens the path planning length by 19.52%, reduces the search time by 16.8%, and reduces the number of turns by 46.67%. Therefore, the method in this paper solves the problem of traditional grid maps lacking security information and can plan a path with higher security and which is smoother, improving the security and robustness of mobile robot autonomous navigation in complex environments.

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Section: Environmental Map Constructionmentioning

confidence: 99%

Map Construction and Path Planning Method for Mobile Robots Based on Collision Probability Model

Li,

Tang,

Zhang

et al. 2023

Symmetry

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“…At present, the trajectory tracking control methods of threewheel differential drive robots are commonly involved with PID control [2][3][4], back-stepping [5][6][7], and intelligent control [8][9][10][11] as well as adaptive control [12][13][14][15][16] and sliding mode variable structure control [17][18][19][20][21], etc. As a simple and reliable working mode, PID control is widely applied in tracking the trajectory of mobile robots.…”

Section: Introductionmentioning

confidence: 99%

Trajectory Tracking Strategy for Sliding Mode Control With Double Closed-Loop for Lawn Mowing Robot Based on ESO

et al. 2023

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The mowing robots work with a multivariable strong coupling underactuated system that is mostly troubled by difficulty controlling and unsatisfactory accuracy. Especially, the frequent external disturbances and parameter changes are likely to get missed and heavy cutting. In this paper, a new trajectory tracking control method based on extended state observer (ESO) is introduced with a particular focus on dual closed-loop sliding mode. Firstly, from the perspective of kinematics, a speed assistant controller was designed to generate the speed control quantity, and secondly, a sliding mode control algorithm based on the improved Fractional Power Rate Reaching Law (IFPRRL) was programmed to control the drive motor that tracked the speed control quantity. By means of comparison, our improved algorithm presented faster arrival time and better robustness along with similar jittering. At the same time, the robustness of the system was further enhanced with the help of an optimized ESO to tackle unmodeled disturbances and uncertain disturbances during the operation. Finally, the experimental analysis of the motor drive circuit and the trajectory tracking control system of the lawn mowing robot were both carried out respectively. The analysis shows that the performance of the proposed reaching law sliding mode control algorithm had some new pleasing changes, such as adjustment time and robustness. The circular trajectory and the detour mowing trajectory were respectively tracked in the double closed-loop sliding mode designed in this paper. The experimental goal was to ensure that the error vector Pe = (x Axis position error xe, y Axis position error ye, Angle error θe) all remaining at (0.01m, 0.01m, 0.01rad) were 5.34s and 5.36s, respectively, and both could be finally converged to 0. The results show that the newly developed controller based on ESO presented smaller arrival time and stronger robustness. The dual-closed-loop control of sliding-mode trajectory tracking method was capable to meet the real-time and precision requirements of the lawnmower robot for quick trajectory tracking.

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“…The mobile robot is a system that can work in various environments. This means that the robot must be able to navigate without delay and avoid any obstacles placed within the boundaries of its movement [8], [9], [10], [11]. Therefore, to design mobile robots that can be intelligently managed and operate autonomously when traveling from one site to another, at least two steps are required.…”

Section: Introductionmentioning

confidence: 99%

Path Planning and Trajectory Tracking Control for Two-Wheel Mobile Robot

Hassan,

Abed,

Al-Hussaibi

2023

Journal of Robotics and Control

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The mobile robot is a system that can work in various environments. This means that the robot must be able to navigate without delay and avoid any obstacles placed within the boundaries of its movement. Designing mobile robots that can be intelligently managed and operate autonomously when traveling from one place to another requires at least two steps. To start with, path planning is required to prevent motion collisions. Tracking the robot's trajectory is a crucial second task. The primary goal of this study is to find the quickest and safest path between the two positions. In this work, we investigated the path planning of a mobile robot with dynamic, and dynamic obstacles with moving goal environments using RRT, BiRRT, and HA* algorithms. These algorithms are easy, computationally inexpensive, and simple to use. They have been chosen for numerous real-time path-planning applications. The DDMR's kinematic model has been utilized in this paper to control path tracking, and a PID controller has been proposed to reduce tracking deviations between the robot's actual route and the reference trajectory. This work introduced the PSO, FPA, CSA, SSA, BWOA, and proposed HBPO optimization techniques for obtaining PID parameters (k_p,k_i,k_d) for improved mobile robot trajectory tracking. The simulation results have been examined using three trajectory shapes: step, circular, and infinite. The simulation findings reveal that HA* outperforms the other algorithms by generating collision-free pathways that are smoother and shorter than their RRT and BiRRT equivalents. On the other hand, the proposed HBPO outperforms the other methods. The HBPO method converges quicker than the other proposed algorithms.

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An Intelligent Hybrid Control to Enhance Applicability of Mobile Robots in Cluttered Environments

Cited by 6 publications

References 31 publications

Map Construction and Path Planning Method for Mobile Robots Based on Collision Probability Model

Map Construction and Path Planning Method for Mobile Robots Based on Collision Probability Model

Trajectory Tracking Strategy for Sliding Mode Control With Double Closed-Loop for Lawn Mowing Robot Based on ESO

Path Planning and Trajectory Tracking Control for Two-Wheel Mobile Robot

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