Path Planning Of Manipulator Research Articles

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Currently, the robotization of various spheres of human life is moving at a high pace. Robots of various types and purposes are used everywhere, from storage robots moving along a given route or markers to high-tech robotic complexes that solve tasks with minimal operator participation. Robotics technology continues to evolve, and its potential for automation and solving various tasks is constantly expanding. One of the key issues of increasing the autonomy of mobile robots is the development of new and improvement of the existing approaches to controlling the movement of robots, in particular to path planning. In this paper, the task of path planning is solved using artificial neural networks and deep machine learning with reinforcement, in which the robot learns to choose actions in the environment in such a way as to maximize some numerical reward or achieve a certain goal. This approach allows you to plan the trajectory of movement by modeling the environment, the behavior of the robot, as well as the interaction between them. The reinforcement learning method provides an effective way for robots and autonomous systems to learn to adapt to diverse conditions and perform path planning tasks. In this paper, the possibility of solving the problem of planning movement to a given point using the method of approximate strategy optimization and the "Action – Criticism" method is investigated. The results obtained show the possibility of solving the task when learning on a relatively small number of episodes. The proposed approach can be used to control ground-based robotic systems for various purposes.

Robot Path Planning Algorithm based on Improved A* and DWA

Aiming at the problems of low search efficiency of A* algorithm in traditional path planning, many redundant points, and inability to avoid unknown obstacles in real-time in complex environments, this paper proposes a path planning algorithm based on A* combined with Dynamic Window Approach (DWA) algorithm. First, the evaluation function of the traditional A* algorithm and the expansion strategy of sub-nodes are improved to improve the safety and search efficiency of the global path. Then the redundant nodes in the global path are processed to reduce the number of turning points and improve the smoothness of the global path. to improve the instability and energy consumption of the robot during travel; finally, based on the global path planning, the DWA algorithm is introduced to perform path planning in the local unknown environment. The local path planning is completed by retaining the key path turning points as intermediate path guidance. Real-time obstacle avoidance. Through simulation experiments, the effectiveness and feasibility of the fusion algorithm are verified.

End-effector path tracking of a 14 DOF Rover Manipulator System in CG-Space framework

AbstractIn order to resolve redundancy and path planning of a high DOF mobile manipulator using conventional approaches like Jacobian and a pseudoinverse method, researchers face the limitation of computational load and delay in response. If such kind of mobile manipulator is traversing the rough terrain, then conventional methods become too costly to implement due to the handling of redundant joints, obstacles, and wheel-terrain interaction. A few optimization-based redundancy resolution approaches try incorporating wheel-terrain interaction but fail in real-time response. This paper describes a 14 DOF Rover Manipulator System’s end-effector path-tracking approach using CG-Space framework to incorporate wheel-terrain interaction. CG-Space means the center of gravity (CG) locus of the Rover. The Rover’s CG is calculated while traversing over 3D terrain using a multivariable optimization method and a 3D point cloud image of the actual terrain and stored as CG-Space over the given terrain. First of all, we decide which part of the system moves to track the path, that is, arm or Rover, depending upon the manipulator’s work volume and manipulability measure restrictions. The next task is to obtain the Rover pose according to the end-effector path using a simple arm’s inverse kinematic solution between the CG-Space and end-effector task space without resolving redundancy. Meanwhile, obstacles and non-traversable regions are avoided in CG-Space. On diverse 3D terrains, simulation and experimental results support the suggested CG-Space framework for end-effector tracking.

Maximizing UAV Coverage in Maritime Wireless Networks: A Multiagent Reinforcement Learning Approach

In the field of ocean data monitoring, collaborative control and path planning of unmanned aerial vehicles (UAVs) are essential for improving data collection efficiency and quality. In this study, we focus on how to utilize multiple UAVs to efficiently cover the target area in ocean data monitoring tasks. First, we propose a multiagent deep reinforcement learning (DRL)-based path-planning method for multiple UAVs to perform efficient coverage tasks in a target area in the field of ocean data monitoring. Additionally, the traditional Multi-Agent Twin Delayed Deep Deterministic policy gradient (MATD3) algorithm only considers the current state of the agents, leading to poor performance in path planning. To address this issue, we introduce an improved MATD3 algorithm with the integration of a stacked long short-term memory (S-LSTM) network to incorporate the historical interaction information and environmental changes among agents. Finally, the experimental results demonstrate that the proposed MATD3-Stacked_LSTM algorithm can effectively improve the efficiency and practicality of UAV path planning by achieving a high coverage rate of the target area and reducing the redundant coverage rate among UAVs compared with two other advanced DRL algorithms.

A robot path planning method using improved Harris Hawks optimization algorithm

The traditional Harris Hawks optimization algorithm is prone to the local shortest path, slow search speed and poor path accuracy in indoor mobile robot path planning. For the above problems, a multi-strategy improvement of the Harris Hawks optimization algorithm (MIHHO) is proposed. In this study, a Chebyshev chaotic mapping strategy is used to increase the diversity of the Harris Hawk population, improve the global search performance of the Harris Hawk algorithm, and prevent being trapped in the locally optimal path. A fusion exploration mechanism is proposed to fuse the discovery mechanism of the sparrow algorithm with the exploration mechanism of the HHO. Then the influence factor E is improved to improve the algorithm’s search accuracy and search efficiency, and finally, in the design of a dynamic Lévy flight strategy, which accelerates the convergence speed of the algorithm and improves the robot planning speed. Simulation results show that the proposed MIHHO method exhibits better search performance in path planning, improved planning efficiency, and superior quality of planned paths.

Recent Advances in Robot Visual SLAM

Background: SLAM plays an important role in the navigation of robots, unmanned aerial vehicles, and unmanned vehicles. The positioning accuracy will affect the accuracy of obstacle avoidance. The quality of map construction directly affects the performance of subsequent path planning and other algorithms. It is the core algorithm of the intelligent mobile application. Therefore, robot vision slam has great research value and will be an important research direction in the future. Objective: By reviewing the latest development and patent of Computer Vision SLAM, this paper provides references to researchers in related fields. Methods: Computer Vision SLAM patents and literature were analyzed from the aspects of the algorithm, innovation, and application. Among them, there are more than 30 patents and nearly 30 pieces of literature in the past ten years. Results: This paper reviews the research progress of robot visual SLAM in the last 10 years, summarizes its typical features, especially describes the front part of the visual SLAM system in detail, describes the main advantages and disadvantages of each method, analyses the main problems in the development of robot visual SLAM, prospects its development trend, and finally discusses the related products and patents research status and future of robot visual SLAM technology. Conclusion: The Robot Vision SLAM can compare the texture information of the environment and identify the difference between the two environments, thus improving accuracy. However, the current SLAM algorithm is easy to fail in fast motion and highly dynamic environments, most SLAM action plans are inefficient, and the image features of VSLAM are too distinguishable. Furthermore, more patents on the Robot Vision SLAM should also be invented.

Local Path Planning via Improved Fuzzy and Q(λ)-learning Algorithms for the Mobile Robot

<p>With the complexity of the robot operating environment increases, there becoming higher demands on the optimal path planning for robots. Most of the path planning is performed in known environments and static models. However, there are still challenges for robots to perform path planning in complex unknown or dynamic environments, which will suffer from deadlock problems and obstacle avoidance failures. Reinforcement learning (RL) can help fuzzy algorithm to optimize the strategy. However, the difficulty of designing the rewards in RL makes the algorithm require a large number of samples to learn the strategy, resulting in computational complexity. To solve these problems, a new local path planning based on the improved fuzzy and Q(λ)-learning algorithms is proposed, aiming to plan the shortest path and avoid obstacles. For solving the problems of breaking through and avoiding obstacles, a fuzzy controller is designed. The distance of nearest obstacle in front of the mobile robot and the distance between the obstacles in the two breakout directions are regarded as the two inputs for this controller. And the two fuzzy quantities of the mobile robot’s running angle and the safe step length are outputted. In the path planning, the Q(λ)-learning algorithm are used to optimize the weights of the running angle and the safe step, obtaining a more accurate robot position and speeding up path planning efficiency. Furthermore, to solve the overlap problems among the starting point, end point, and obstacles, a safer running environment is designed considering radiuses of these objects. Besides, the mobile robot breakout scheme and sustainable obstacle avoidance scheme are designed to solve the deadlock problem and “large obstacle” avoidance problem, respectively. Simulation results in the sparse and complex operating environment show that our proposed algorithm can plan a relatively optimal and safe path, improving the success rate of path planning.</p> <p> </p>

Improved path planning method for unmanned aerial vehicles based on artificial potential field

With the advancement of unmanned aerial vehicle (UAV) technology and its widespread use in many facets of manufacturing and daily life, the need for UAV mission automation is becoming more and more practical. In order to improve the automatic obstacle avoidance and path planning performance of UAVs, this essay proposes an optimized route planning algorithm based on the artificial potential field (APF) method, which have solved the typical issue of the APF. This method chooses to conduct a pre planning trajectory of the UAV based on a rapidly expanding random tree (RRT). The pre-planned path will be split into continuous particles, and then generating intermediate waypoints. A nearby waypoint offers a gravitational force to aid the UAV in escaping the local minimum when it enters it. At the same time, taking the distance from the UAV to the obstacle and the radius of influence of the obstacle itself into consideration, dynamically adjust the gravitational and repulsive coefficients, set up a non-gravitational zone around the obstacle, which is beneficial for the UAV to elude the obstacle. And set up a repulsive limited action zone to reduce unnecessary turns in the trajectory to achieve the effect of path optimization. Considering the difficulty of single UAV missions in most cases, this paper discusses cooperative flight path planning for multiple UAVs into consideration.

A Cooperative Hunting Method for Multi-USV Based on the A* Algorithm in an Environment with Obstacles.

A single unmanned surface combatant (USV) has poor mission execution capability, so the cooperation of multiple unmanned surface ships is widely used. Cooperative hunting is an important aspect of multi USV collaborative research. Therefore, this paper proposed a cooperative hunting method for multi-USV based on the A* algorithm in an environment with obstacles. First, based on the traditional A* algorithm, a path smoothing method based on USV minimum turning radius is proposed. At the same time, the post order traversal recursive algorithm in the binary tree method is used to replace the enumeration algorithm to obtain the optimal path, which improves the efficiency of the A* algorithm. Second, a biomimetic multi USV swarm collaborative hunting method is proposed. Multiple USV clusters simulate the hunting strategy of lions to pre-form on the target's path, so multiple USV clusters do not require manual formation. During the hunting process, the formation of multiple USV groups is adjusted to limit the movement and turning of the target, thereby reducing the range of activity of the target and improving the effectiveness of the algorithm. To verify the effectiveness of the algorithm, two sets of simulation experiments were conducted. The results show that the algorithm has good performance in path planning and target search.

Path Planning and Energy Optimization in Optimal Control of Autonomous Wheel Loaders Using Reinforcement Learning

This paper proposes a novel solution based on reinforcement learning for optimal control of an autonomous Wheel Loader (WL). The solution considers the movement of a WL in a Short Loading Cycle (SLC) as a switched system with controlled subsystems such that the sequence of active modes is fixed. Therefore, the optimal control system solves two different levels of optimization. In the upper level, optimal switching times are sought. In the lower level, the control inputs to navigate the wheel loader and performing path planning are sought. For solving the problem, Approximate Dynamic Programming (ADP), which is the application of reinforcement learning to find near-optimal control solution, is used. Simulation results are provided to show the effectiveness of the solution. At last, challenges of using the proposed method and future works are summarized in Conclusion.

An Analysis Review: Optimal Trajectory for 6-DOF-based Intelligent Controller in Biomedical Application

With technological advancements and the development of robots have begun to be utilized in numerous sectors, including industrial, agricultural, and medical. Optimizing the path planning of robot manipulators is a fundamental aspect of robot research with promising future prospects. The precise robot manipulator tracks can enhance the efficacy of a variety of robot duties, such as workshop operations, crop harvesting, and medical procedures, among others. Trajectory planning for robot manipulators is one of the fundamental robot technologies, and manipulator trajectory accuracy can be enhanced by the design of their controllers. However, the majority of controllers devised up to this point were incapable of effectively resolving the nonlinearity and uncertainty issues of high-degree freedom manipulators in order to overcome these issues and enhance the track performance of high-degree freedom manipulators. Developing practical path-planning algorithms to efficiently complete robot functions in autonomous robotics is critical. In addition, designing a collision-free path in conjunction with the physical limitations of the robot is a very challenging challenge due to the complex environment surrounding the dynamics and kinetics of robots with different degrees of freedom (DoF) and/or multiple arms. The advantages and disadvantages of current robot motion planning methods, incompleteness, scalability, safety, stability, smoothness, accuracy, optimization, and efficiency are examined in this paper.

Obstacle Avoidance Path Planning of 7-DOF Redundant Manipulator Based on Improved Ant Colony Optimization

Background: Obstacle avoidance path planning is an important parameter of robot manipulators. Path planning can directly affect the working efficiency of the manipulator. Objective: This study aims to summarize the optimization design method from a large number of literature and propose a new optimization design method to make the planned obstacle avoidance path of the manipulator shorter and smoother. Methods: The forward and inverse kinematics of the redundant manipulator is solved. Secondly, the obstacle and the robot manipulator envelope are simplified for collision detection. Then, the ant colony algorithm is improved by adding an obstacle environment to the construction of the heuristic function and dynamically adjusting the heuristic function factor to make the shortest path distance planned by the algorithm. Finally, the worst ant colony is added to the pheromone update to avoid the algorithm falling into the local optimal solution. Results: Through experiments and comparative studies, the optimized design process shows that the path planned by the improved ant colony algorithm has obvious advantages of shorter path distance and smoother path distance, which verifies the rationality of the improved algorithm. Conclusion: This method optimizes the convergence speed of the ant colony algorithm and avoids the ant colony algorithm from falling into the local optimal solution, which is of great significance for improving the obstacle avoidance path planning problem of a redundant manipulator with a degree of freedom.

A Path Planning Method with a Bidirectional Potential Field Probabilistic Step Size RRT for a Dual Manipulator

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.

Moderating Effect of Strategic Planning on the Relationship between Career Path Planning and Job Performance

This study aimed to assess the moderating effect of strategic planning on the relationship between career path planning and job performance among employees in small and medium-sized enterprises (SMEs) in the Kingdom of Bahrain. A convenience sample consisting of 249 participants was selected. The study used SPSS and Smart-PLS to analyze the data. The findings of the study revealed that strategic planning and career path planning affected SME employees’ performance. In contrast, it was found that strategic planning substantially moderated the relationship between career path planning and job performance. Moreover, the results showed that SP and CPP had the greatest impact on JP. Based on the obtained findings, it is shown that the proposed hypotheses are accepted. The study revealed the impact of the moderator on the relationship between CPP and JP. The results of this study may serve as a guide for the owners and managers of enterprises and decision makers who aim to develop strategic planning and enhance employee performance.

Research on Path Planning of Manipulator for Terahertz Time-domain Imaging System of Power Insulators

To ensure detection accuracy, the focus of the terahertz lens is required to be located on the surface of the workpiece in the process of terahertz imaging detection, and the outgoing optical axis of the lens is also required to be perpendicular to the surface of the workpiece. Therefore, when imaging curved samples, a 6-degree freedom manipulator is generally used to drive the terahertz lens, and the lens realizes non-destructive testing along the workpiece surface. The mechanical arm is required to drive the terahertz lens to move along the rotation curve in a two-dimensional plane when the mechanical arm is used to detect the power insulator of the rotating structure. This paper focuses on the path planning of the manipulator, that is, the curve is divided into several connected straight lines and arcs, and the manipulator moves continuously in each characteristic line segment. The D-H dynamic model of the manipulator was established to analyze the joint velocity and acceleration to ensure the stability of the moving process.

Path Planning and Obstacle Avoidance Control for Autonomous Multi-Axis Distributed Vehicle Based on Dynamic Constraints

Obstacle avoidance problem for the autonomous vehicle has received more and more attention these years, especially for the autonomous heavy vehicle, which has the high gravity center, large carrying capacity and promising commercial prospect. However, the vehicle dynamic properties have been rarely considered for path planning, which is a crucial factor for driving safety. An integrated collision avoidance framework that consists of a new path planner and a tracking controller considering multiple dynamic constraints is proposed for an autonomous multi-axis distributed vehicle. In the upper level, a new optimal path generator based on the B-splines is designed for safety and stability purposes, and the collision avoidance path is optimized considering multiple dynamic constraints, including the tire nonlinear dynamic, lateral stability limits and the anti-slip constraints. Meanwhile, a safety assessment evaluator according to the time to collision calculation is designed for risk level recognition and path planning weight determination. In the lower level, a path tracking controller based on the nonlinear model predictive control (NMPC) is designed to improve the control accuracy, and a high-fidelity nonlinear dynamic predictive vehicle model with 16 degrees of freedom is established to depict the vehicle dynamic properties comprehensively. To solve the nonlinear optimization problem and get the optimal control steering angle output, an adaptive weight adjustment strategy and a varying predictive duration method are formulated. The optimization constraints related to steering actuator ability, plane stability limits, rollover prevention thresholds and tire adhesion requirements are constructed and converted as the bounds of the vehicle dynamic states. To verify the proposed framework, the simulation and HIL test platform are built and different driving scenarios are validated. The test results show the satisfactory performance of path planning and path tracking considering the dynamic constraints for safety and stability

System design of an intelligent sweeper robot

With the advancement of technology and science, various intelligence household appliances are becoming increasingly popular. This paper proposes a household sweeping robot with the ability to climb stairs. The robot adopts ATmega328p as controller, two gear motors and a DC motor to drive the robot to walk and vacuum. Using rational algorithms in WEBOTS, the robot is deemed able to effectively avoid obstacles and perform path planning, demonstrating its ability to follow a path to bypass obstacles and climb stairs. Such design proposes a new intelligent path panning solution for floor sweeping robots which enables high coverage and low repetition of cleaning tasks by creating workflow systems and algorithms. As opposed to traditional sweeping robots, the robot can climb stairs and perform cleaning tasks more efficiently and is less expensive. Additionally, the design enables the robot to work in more complex environment such as multi-floor situations, as well as to perform self-cleaning tasks.

Impacts of decision support systems on cognition and performance for intelligence-gathering path planning

ABSTRACT Decision Support Systems (DSS) are tools designed to help operators make effective choices in workplace environments where discernment and critical thinking are required for effective performance. Path planning in military operations and general logistics both require individuals to make complex and time-sensitive decisions. However, these decisions can be complex and involve the synthesis of numerous tradeoffs for various paths with dynamically changing conditions. Intelligence collection can vary in difficulty, specifically in terms of the disparity between locations of interest and timing restrictions for when and how information can be collected. Furthermore, plans may need to be changed adaptively mid-operation, as new collection requirements appear, increasing task difficulty. We tested participants in a path planning decision-making exercise with scenarios of varying difficulty in a series of two experiments. In the first experiment, each map displayed two paths simultaneously, relating to two possible routes for the two available trucks. Participants selected the optimal path plan, representing the best solution across multiple routes. In the second experiment, each map displayed a single path, and participants selected the best two paths sequentially. In the first experiment, utilizing the DSS was predictive of adoption of more heuristic decision strategies, and that strategic approach yielded more optimal route selection. In the second experiment, there was a direct effect of the DSS on increased decision performance and a decrease in perceived task workload.

Dynamic Path Planning for Multiple UAVs with Incomplete Information

To address the dynamic path planning for multiple UAVs using incomplete information, this paper studies real-time conflict detection and intelligent resolution methods. When the UAVs execute the task under the condition of incomplete information, the mission strategy of different UAVs may conflict with each other due to the difference in target, departure place, time and other factors. Based on the multi-agent deep deterministic policy gradient algorithm (MADDPG), we designed new global reward and partial local reward functions for the UAVs’ path planning and named the improved algorithm as a complex memory driver-MADDPG (CMD-MADDPG). Thus, the trained UAVs can effectively and efficiently perform path planning tasks in conditions of incomplete information (each UAV does not know its reward function and so on). Finally, the simulation verifies that the proposed method can realize fast and accurate dynamic path planning for multiple UAVs.

MDHO: Mayfly Deer Hunting Optimization Algorithm for Optimal Obstacle Avoidance Based Path Planning Using Mobile Robots

Mobile robot becomes more significant in human life and industry, whereas navigation of robot in the dynamic environment results a challenging problem and it need to be solved in an efficient way. Path planning gained more attention in recent decades and puts its practical usage in different industries. Path planning for the mobile robot is to determine feasible path to reach target location in workspace. A more challenging problem with mobile robot is solving path planning issue by avoiding obstacles in an optimize way. Various methods are designed to perform path planning mechanism, but it faced complexity in finding the solution to reach the target. Hence, an efficient Mayfly Deer Hunting Optimization (MDHO) algorithm is designed in this research to move the mobile robots to reach target location in the environment using multi-objective function. However, multi-objective function is designed by considering the factors, like path length, path smoothness, and the obstacle avoidance. The path that satisfies the objective constraints is selected as optimal path to reach the target of mobile robot. The proposed model attains minimum path length, maximal path smoothness, and maximum fitness as 1159.0 m, 0.913, and 3.5418 by considering fixed obstacles and multiple targets.