An overview: on path planning optimization criteria and mobile robot navigation

Atiyah, Anis Naema; Adzhar, Noraziah; Jaini, Nor Izzati

doi:10.1088/1742-6596/1988/1/012036

Cited by 10 publications

(7 citation statements)

References 29 publications

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“…In this study, we focused on the autonomous driving technology of a patient-transfer robot (PTR) applied to medical facilities, one of the various application fields of autonomous driving technology. For the implementation of autonomous driving technology, it is necessary to have elements of environment recognition through sensor information, workspace mapping, localization, path planning, and motion control [6]. Among these, this study deals with the path planning of the robot to move from the current position to the goal and the control of the robot to travel the planned path under the assumption that the building of the work environment (also called the map) and localization have been completed by SLAM technology.…”

Section: Introductionmentioning

confidence: 99%

Development of Autonomous Driving and Motion Control System for a Patient Transfer Robot

Kim

2023

Actuators

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In this study, an autonomous driving system of a patient-transfer robot is developed. The developed autonomous driving system has a path-planning module and a motion-control module. Since the developed autonomous driving system is applied to medical robots, such as patient-transfer robots, the main purpose of this study is to generate an optimal path for the robot’s movement and to ensure the patient on board moves comfortably in the PTR. In particular, for the patient’s comfortable movement, a lower controller is needed to minimize the sway angle of the patient. In this paper, we propose a hybrid path-planning algorithm that combines the A-STAR algorithm as a global path-planning method and the AHP (Analytic Hierarchy Process)-based path-planning algorithm as a local path-planning method. In addition, model-based controllers are designed to move patient-transport robots along planned paths. In particular, the LQR controller with the Kalman filter is designed to be robust to the uncertainty and disturbance of the model including the patient. The optimal path generation and patient shaking angle reduction performance of the proposed autonomous driving system have been demonstrated via a simulation on a map that mimics a hospital environment.

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Section: Introductionmentioning

confidence: 99%

Development of Autonomous Driving and Motion Control System for a Patient Transfer Robot

Kim

2023

Actuators

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“…Robots using the path planning method can decide and know the direction of movement to avoid collisions so they can reach their destination. In addition, path planning is needed for the robot to carry out environmental recognition for all information about obstacles and goals that can be known a priori [3], [4].…”

Section: Introductionmentioning

confidence: 99%

Artificial Potential Field Path Planning Algorithm in Differential Drive Mobile Robot Platform for Dynamic Environment

Samodro

Puriyanto

Caesarendra

2023

IJRCS

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Mobile robots need path-planning abilities to achieve a collision-free trajectory. Obstacles between the robot and the goal position must be passed without crashing into them. The Artificial Potential Field (APF) algorithm is a method for robot path planning that is usually used to control the robot for avoiding obstacles in front of the robot. The APF algorithm consists of an attractive potential field and a repulsive potential field. The attractive potential fields work based on the predetermined goals that are generated to attract the robot to achieve the goal position. Apart from it, the obstacle generates a repulsive potential field to push the robot away from the obstacle. The robot's localization in producing the robot's position is generated by the differential drive kinematic equations of the mobile robot based on encoder and gyroscope data. In addition, the mapping of the robot's work environment is embedded in the robot's memory. According to the experiment's results, the mobile robot's differential drive can pass through existing obstacles. In this research, four test environments represent different obstacles in each environment. The track length is 1.5 meters. The robot's tolerance to the goal is 0.1 m, so when the robot is in the 1.41 m position, the robot's speed is 0 rpm. The safe distance between the robot and the obstacle is 0.2 m, so the robot will find a route to get away from the obstacle when the robot reaches that safe distance. The speed of the resulting robot decreases as the distance between the robot and the destination gets closer according to the differential drive kinematics equation of the mobile robot.

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“…Path planning is one of critical techniques for space robots [4], [5]. Path planning has always been a research hotspot in many fields.…”

Section: Introductionmentioning

confidence: 99%

Joint Task Allocation and Path Planning for Space Robot

Sun

Liu

2023

IEEE Access

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Space robots have a broad application prospect in the aerospace industry. It is difficult for space robots to keep running for a long time due to limited fuel. In addition, it is impossible to replenish the fuel for space robots at any time due to the unique working environment. Therefore, a proper path is crucial for the effective operation of the space robot. In this paper, we investigate the allocation of exploration tasks and the path planning of space robots jointly. The goal is to minimize the completion latency of exploration tasks. We propose two algorithms named subbranch insertion task allocation (SI-TA) and parallel search task allocation (PS-TA) to solve the problem. We also customize an algorithm named random path planning task allocation (RTA) as the baseline. At last, we implement extensive experiments to demonstrate that proposed algorithms can obtain lower completion latency than RTA. Compared with RTA, the proposed algorithms SI-TA and PS-TA can reduce completion latency by at least 20% and 40%, respectively. Moreover, both algorithms work more stably than RTA.

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An overview: on path planning optimization criteria and mobile robot navigation

Cited by 10 publications

References 29 publications

Development of Autonomous Driving and Motion Control System for a Patient Transfer Robot

Development of Autonomous Driving and Motion Control System for a Patient Transfer Robot

Artificial Potential Field Path Planning Algorithm in Differential Drive Mobile Robot Platform for Dynamic Environment

Joint Task Allocation and Path Planning for Space Robot

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