Mobile Robot Motion Planning and Multi Objective Optimization Using Improved Approach

ChaborAlwawi, Bashra K. O.; Roth, Hubert; Kazem, Bahaa I.; Abdullah, Mustafa W.

doi:10.18178/ijmerr.4.4.325-330

Cited by 5 publications

(2 citation statements)

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“…In extremely complicated and vast environments, local path planning may not be able to locate the path to the target. This mostly occurs as a result of the sensors' inability to supply enough data to send the robot to the desired location [7]. The base position of the robot, its rotation, and the rotation or translation of its entire links are called the robot's configuration.…”

Section: Introductionmentioning

confidence: 99%

Classical and Heuristic Approaches for Mobile Robot Path Planning: A Survey

Abdulsaheb

Kadhim

2023

Robotics

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The most important research area in robotics is navigation algorithms. Robot path planning (RPP) is the process of choosing the best route for a mobile robot to take before it moves. Finding an ideal or nearly ideal path is referred to as “path planning optimization.” Finding the best solution values that satisfy a single or a number of objectives, such as the shortest, smoothest, and safest path, is the goal. The objective of this study is to present an overview of navigation strategies for mobile robots that utilize three classical approaches, namely: the roadmap approach (RM), cell decomposition (CD), and artificial potential fields (APF), in addition to eleven heuristic approaches, including the genetic algorithm (GA), ant colony optimization (ACO), artificial bee colony (ABC), gray wolf optimization (GWO), shuffled frog-leaping algorithm (SFLA), whale optimization algorithm (WOA), bacterial foraging optimization (BFO), firefly (FF) algorithm, cuckoo search (CS), and bat algorithm (BA), which may be used in various environmental situations. Multiple issues, including dynamic goals, static and dynamic environments, multiple robots, real-time simulation, kinematic analysis, and hybrid algorithms, are addressed in a different set of articles presented in this study. A discussion, as well as thorough tables and charts, will be presented at the end of this work to help readers understand what types of strategies for path planning are developed for use in a wide range of ecological contexts. Therefore, this work’s main contribution is that it provides a broad view of robot path planning, which will make it easier for scientists to study the topic in the near future.

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Section: Introductionmentioning

confidence: 99%

Classical and Heuristic Approaches for Mobile Robot Path Planning: A Survey

Abdulsaheb

Kadhim

2023

Robotics

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“…However, the disadvantage of this type of algorithm is the planning of the obstacle avoidance path without consideration of the actual vehicle dynamics model. In vehicle attitude control, a high-speed vehicle in a complex dynamic environment has a limited degree of freedom compared to a low-speed robot with a high degree of freedom for rotation [ 10 ]. For this reason, it is difficult for an autonomous vehicle to avoid the dynamic obstacles quickly and ensure the stability of the vehicle dynamics simultaneously without considering the specific vehicle dynamic characteristics.…”

Section: Introductionmentioning

confidence: 99%

A Study on Dynamic Motion Planning for Autonomous Vehicles Based on Nonlinear Vehicle Model

Tang

2022

Sensors

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Autonomous driving technology, especially motion planning and the trajectory tracking method, is the foundation of an intelligent interconnected vehicle, which needs to be improved urgently. Currently, research on path planning methods has improved, but few of the current studies consider the vehicle’s nonlinear characteristics in the reference model, due to the heavy computational effort. At present, most of the algorithms are designed by a linear vehicle model in order to achieve the real-time performance at the cost of lost accuracy. To achieve a better performance, the dynamics and kinematics characteristics of the vehicle must be simulated, and real-time computing ensured at the same time. In this article, a Takagi–Sugeno fuzzy-model-based closed-loop rapidly exploring random tree algorithm with on-line re-planning process is applied to build the motion planner, which effectively improves the vehicle performance of dynamic obstacle avoidance, and plans the local obstacle avoidance path in line with the dynamic characteristics of the vehicle. A nonlinear vehicle model is integrated into the motion planner design directly. For fast local path planning mission, the Takagi–Sugeno fuzzy modelling method is applied to the modeling process in the planner design, so that the vehicle state can be directly utilized into the path planner to create a feasible path in real-time. The performance of the planner was evaluated by numerical simulation. The results demonstrate that the proposed motion planner can effectively generate a reference trajectory that guarantees driving efficiency with a lower re-planning rate.

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