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To improve the path planning efficiency of a robotic arm in three-dimensional space and improve the obstacle avoidance ability, this paper proposes an improved artificial potential field and rapid expansion random tree (APF-RRT) hybrid algorithm for the mechanical arm path planning method. The improved APF algorithm (I-APF) introduces a heuristic m...
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... the coordinates of the center of the sphere are (x, y, z), the radii of the spheres are R1 and R2, and the radius of the cylinder is r; then, the distances between the coordinates of the centers of the two spheres and the central axis of the cylinder are calculated, denoted d1 and d2. As shown in Figure 4, when d > r + R, the robotic arm does not collide with the obstacle; otherwise, the arm collides with the obstacle. This method can greatly improve the computational efficiency. ...
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In addressing the challenge of obstacle-avoidance motion planning for space redundant manipulators operating in environments with obstacles, an improved method based on a rapidly exploring random tree (RRT) algorithm is proposed. The method first adopts a target probabilistic sampling strategy to enhance the target orientation. Secondly, an artific...
Citations
... Yuan et al. hybridised the RRT algorithm with APF algorithm, using it to quickly obtain the optimal shortcut in the relatively open range of the environment, and using RRT for obstacle avoidance in the relatively small obstacle environment [18]. The hybrid algorithm not only makes up for the defect of the RRT algorithm that consumes excess arithmetic leading to the inefficiency of the algorithm in the complex situation of a large environment, but also avoids the deficiency of the traditional APF that cannot approach the target point when the obstacle is close to it by jumping out of the thinking range. ...
Path planning algorithms are widely used in the fields of robotics and robotic arms, unmanned devices, automatic navigation, etc., and are an important technical basis for promoting the development of automation as well as the popularisation of artificial intelligence technology. This paper will briefly introduce various path planning algorithms implemented by mathematical models or inspired by biological features or genetics from the aspects of geometric search algorithms, intelligent search algorithms, artificial intelligence algorithms, and hybrid algorithms, including the characteristics, advantages and disadvantages, and important improvements of typical path planning algorithms as well as hybrid algorithms which are made by imitating and improving each other of several algorithms. Meanwhile, the development trend of path planning algorithms is also summarised, and the outlook of its development is made to provide reference for related fields.
... However, there are many turning points in paths when working in areas of obstacles. Yuan et al. [16] propose an APF-RRT algorithm for the motion planning of robotic arms. It introduces a heuristic method based on the number of adjacent obstacles to escape from local minima. ...
In this study, a novel planning and tracking approach is proposed for a mobile robotic arm to grab objects in an obstacle environment. First, we developed an improved APF-RRT* algorithm for the motion planning of a mobile robotic arm. This algorithm optimizes the selection of random tree nodes and smoothing the path. The invalid branch and the planning time are decreased by the artificial potential field, which is determined by the specific characteristics of obstacles. Second, a Fuzzy-DDPG-PID controller is established for the mobile robotic arm to track the planned path. The parameters of the PID controller are set using the new DDPG algorithm, which integrated FNN. The response speed and control accuracy of the controller are enhanced. The error and time of tracking of the mobile robotic arm are decreased. The experiment results verify that the proposed approach has good planning and tracking results, high speed and accuracy, and strong robustness. To avoid the occasionality of the experiments and fully illustrate the effectiveness and generality of the proposed approach, the experiments are repeated multiple times. The experiment results demonstrate the effectiveness of the proposed approach. It outperforms existing planning and tracking approaches.
... Qingni Yuan et al. introduced a hybrid algorithm, APF-RRT (Artificial Potential Field -Rapid Expansion Random Tree), as an improved robot arm path planning method. This algorithm combines the strengths of an enhanced artificial potential field and the rapid expansion random tree technique [17]. Abdi et al. employed a hybrid path planning approach for a 2D workspace, integrating both Q-learning and neural networks [18]. ...
Intelligent robot arms are advanced robotic systems used in Industry 4.0 to perform complex tasks. Unlike conventional robot arms, which perform predefined tasks, intelligent robot arms have autonomy and can operate in changing environments, interact with other machines, and collaborate with humans. In this regard, adaptive path planning is crucial for intelligent robot arms, involving real-time environment monitoring and path generation to continuously update the robot’s trajectory based on changes in the surroundings. This paper presents an adaptive path planning method for intelligent robot arms to be used in dynamic environments. The proposed method is based on a hybrid active-passive approach and has been tested in a dynamic workspace simulation environment. The results indicate the ability of the proposed method to respond dynamically in a complex scenario where the target is fluctuating, and an obstacle is intentionally placed in the robot’s path. Additionally, real-time analysis results show that the method can be categorized as real-time path planning with less than 100 ms reaction time for grid sizes with less than 96×96×96 cells. This insight presents opportunities for the establishment of smart factories, smart homes, and smart cities, where the presence of intelligent robot arms in dynamic environments becomes essential.
... The Rapidly-exploring Random Trees (RRT) algorithm, based on random sampling, has been extensively researched in the literature and is widely used in various applications such as mobile robot path planning [25], mechanical arm trajectory planning [26], etc. Compared to traditional methods such as A* and genetic algorithms, the main advantages of RRT are its speed and ability to handle non-holonomic constraints [27]. ...
The RRT algorithm based on random sampling is widely used in planning problems with non-holonomic constraints. In indoor environments, narrow passages exist in the map, and the RRT method suffers from slow convergence speed and poor path quality. Additionally, the typical feature of large indoor room spaces with small exits creates traps where RRT can easily become stuck, making it difficult to find a path quickly. To solve
the above problems, this paper proposes an improved RRT algorithm. This method first uses the simplified Bridge Test and point cloud clustering algorithm to locate the narrow passages on the map and place the root nodes in the narrow passage. Then grow multiple random trees from the root nodes, starting and endpoints. As the number of samples increases, the random trees gradually expand and connect to each other. The trees are connected to each other to build a complete path. Finally, the initial path is optimized and the final planning result is obtained. The simulation of the improved RRT, RRT*, RRT, and Bi-RRT is carried out. The simulation results show that the improved RRT algorithm proposed in this paper outperforms in terms of path length, number of exploring nodes, and processing time. We also tested 4 algorithms on a real robot, and the results prove the practical value of the improved algorithm.
... The artificial potential field method is introduced to improve the step size step efficiency by integrating the potential field method. The gravitational field step size expression [33] is shown in Equation (15): ...
... Additionally, the step size of the repulsive field [33] is expressed as: ...
The search efficiency of a rapidly exploring random tree (RRT) can be improved by introducing a high-probability goal bias strategy. In the case of multiple complex obstacles, the high-probability goal bias strategy with a fixed step size will fall into a local optimum, which reduces search efficiency. Herein, a bidirectional potential field probabilistic step size rapidly exploring random tree (BPFPS-RRT) was proposed for the path planning of a dual manipulator by introducing a search strategy of a step size with a target angle and random value. The artificial potential field method was introduced, combining the search features with the bidirectional goal bias and the concept of greedy path optimization. According to simulations, taking the main manipulator as an example, compared with goal bias RRT, variable step size RRT, and goal bias bidirectional RRT, the proposed algorithm reduces the search time by 23.53%, 15.45%, and 43.78% and decreases the path length by 19.35%, 18.83%, and 21.38%, respectively. Moreover, taking the slave manipulator as another example, the proposed algorithm reduces the search time by 6.71%, 1.49%, and 46.88% and decreases the path length by 19.88%, 19.39%, and 20.83%, respectively. The proposed algorithm can be adopted to effectively achieve path planning for the dual manipulator.
... Second, we must smooth the joints of the primitive polyline to satisfy the constraints (9) and provide the average desired velocity in the different sections. To design the control law (11), it is sufficient to obtain signals χ 1 (t), (8) and (9). Thus, we obtain the reference trajectory in signal form instead of analytical form. ...
... To design the tracking system (11), we need continuous signals that form an achievable trajectory (8) and their first and second derivatives that satisfy the given constraints (9). To smooth the primitive trajectory signals, we use a tracking dynamic differentiator with sigmoid correcting actions. ...
... The scaling parameters of sigma functions satisfying (26) and (27) ensure that the output variables x 1 (t) of system (28) track the primitive trajectory (16) with some accuracy (25) and at the same time constrain the state variables considering (9). The dynamic model (28) naturally smooths the signals χ 1 (t) at its input, so that the output generates an achievable trajectory x 1 (t) for the UAV's center of mass. ...
For an unmanned aerial vehicle (UAV) of an aircraft type, the problems of planning achievable trajectories as well as robust control under wind disturbances are considered. A computationally simple method for compiling a primary non-smooth 4D trajectory is proposed. Its segments connect the given waypoints and determine the desired average velocity in various sections. Instead of time-consuming methods of analytical smoothing of broken path joints using polynomials, a tracking differentiator with S-shaped smooth and limited sigmoid corrective actions is developed. This virtual dynamic model provides natural smoothing of the primary trajectory considering the design constraints on the velocity, acceleration, and thrust of the UAV. The tracking differentiator variables create an achievable trajectory and are used to synthesize the UAV tracking system. To compensate for the action of wind disturbances on the UAV, a disturbance observer is developed. It is a replica of the equations of the control plant model, which are directly affected by external uncontrolled disturbances. These algorithms also use sigmoid corrections. Unlike standard disturbances observers, this approach does not require the development of a dynamic model of external disturbances and does not assume their smoothness. The effectiveness of the developed algorithms was confirmed by numerical simulation.
... Due to the presence of various objects in the working environment, path planning for robots is one of the most important topics in robotics research and is widely discussed [1][2][3][4]. If the robot does not have a good path planning method to choose a collision-free path, various collision situations may occur. ...
An object pick-and-place system with a camera, a six-degree-of-freedom (DOF) robot manipulator, and a two-finger gripper is implemented based on the robot operating system (ROS) in this paper. A collision-free path planning method is one of the most fundamental problems that has to be solved before the robot manipulator can autonomously pick-and-place objects in complex environments. In the implementation of the real-time pick-and-place system, the success rate and computing time of path planning by a six-DOF robot manipulator are two essential key factors. Therefore, an improved rapidly-exploring random tree (RRT) algorithm, named changing strategy RRT (CS-RRT), is proposed. Based on the method of gradually changing the sampling area based on RRT (CSA-RRT), two mechanisms are used in the proposed CS-RRT to improve the success rate and computing time. The proposed CS-RRT algorithm adopts a sampling-radius limitation mechanism, which enables the random tree to approach the goal area more efficiently each time the environment is explored. It can avoid spending a lot of time looking for valid points when it is close to the goal point, thus reducing the computing time of the improved RRT algorithm. In addition, the CS-RRT algorithm adopts a node counting mechanism, which enables the algorithm to switch to an appropriate sampling method in complex environments. It can avoid the search path being trapped in some constrained areas due to excessive exploration in the direction of the goal point, thus improving the adaptability of the proposed algorithm to various environments and increasing the success rate. Finally, an environment with four object pick-and-place tasks is established, and four simulation results are given to illustrate that the proposed CS-RRT-based collision-free path planning method has the best performance compared with the other two RRT algorithms. A practical experiment is also provided to verify that the robot manipulator can indeed complete the specified four object pick-and-place tasks successfully and effectively.
... In the process of tree sampling growth, pruning redundant paths can improve the planning efficiency [3]. In the movement of the robot and the manipulator, the RRT algorithm is combined with the artificial potential field method and the extended algorithm, which can remove redundant branches, select appropriate nodes and complete the path planning [4,5]. ...
In a complex environment, although the artificial potential field (APF) method of improving the repulsion function solves the defect of local minimum, the planned path has an oscillation phenomenon which cannot meet the vehicle motion. In order to improve the efficiency of path planning and solve the oscillation phenomenon existing in the improved artificial potential field method planning path. This paper proposes to combine the improved artificial potential field method with the rapidly exploring random tree (RRT) algorithm to plan the path. First, the improved artificial potential field method is combined with the RRT algorithm, and the obstacle avoidance method of the RRT algorithm is used to solve the path oscillation; The vehicle kinematics model is then established, and under the premise of ensuring the safety of the vehicle, a model predictive control (MPC) trajectory tracking controller with constraints is designed to verify whether the path planned by the combination of the two algorithms is optimal and conforms to the vehicle motion. Finally, the feasibility of the method is verified in simulation. The simulation results show that the method can effectively solve the problem of path oscillation and can plan the optimal path according to different environments and vehicle motion.
