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A path planning approach
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Mobile robots are growing more significant from time to time and have been applied to many fields such as agriculture, space, and even human life. It could improve mobile robot navigation efficiency, ensure path planning safety and smoothness, minimize time execution, etc. The main focus of mobile robots is to have the most optimal functions. An in...
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... Autonomous control typically comprises three principal elements: mapping, selfposition estimation, and path planning [8][9][10][11]. Path planning can be subdivided into global and local planning. Global path planning delineates the route from a starting point to a destination on a map, with algorithms such as A* [12], D* [13], and RRT* [14] being conventionally utilized for this function. ...
The dynamic window approach (DWA) serves as a pivotal collision avoidance strategy for mobile robots, meticulously guiding a robot to its target while ensuring a safe distance from any perceivable obstacles in the vicinity. While the DWA has seen various enhancements and applications, its foundational computational process has predominantly remained constant, consequently resulting in a heightened level of time complexity. Inspired by the velocity invariance assumption inherent in the DWA and the utilization of polar coordinate transformations in the model, we introduce a high-speed version of the DWA.
... 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]. ...
... The repulsive potential field equation ( ( , )) can be seen in (4). ...
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.
... 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. ...
... Recently, geofencing methods that generate paths and avoid obstacles via a virtual fence of the obstacles were used in [9][10][11]. On the other hand, local path planning is an online planning method, and based on limited information obtained from robot sensors, it interacts with the surrounding environment and plans a moving path [6,12]. In a static workspace where the workspace does not change, it is possible to work with only global path planning. ...
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.
... Path planning is one of critical techniques for space robots [4], [5]. Path planning has always been a research hotspot in many fields. ...
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.
... Advances in the space field have greatly promoted the research of space robots, such as path planning of space robots and system design [3,4]. As early as 1994, some scholars have already systematically studied the path planning problem [5]. ...
Space robots have a wide application prospect in the aerospace industry. Due to the limited fuel for the space robots, which cannot support the space robots to run for a long time. In addition, the special working environment makes it impossible to replenish the fuel for the robot at any time. Therefore, a proper path is crucial for the operation of space robots. In order to ensure the operation of space robots, we optimize the allocation of exploration tasks and the selection of space robot paths, jointly. We propose two combination algorithms named Parallel Search and Task Allocation (PS-TA) and Subbranch Insertion and Task Allocation (SI-TA) to optimize the path and the task allocation , intend to obtain the minimum completion latency. We also construct Random Path Planning and Task Allocation (RTA) as the baseline. At last, we provide extensive experiments to demonstrate that proposed algorithms can obtain lower completion latency compared with RTA. Furthermore, SI-TA is more energy-efficient than PS-TA.
... 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]. ...
... Next, the constraint calculation of the viewable angle of x is needed to obtain its viewable angle as [0,45]. Set the (d,4) node to x in the order of the nodes in the Route(< s, 1, 2, . . . , e >) list. ...
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.
