Motion Estimation of Moving Target Using Multiple Images in Intelligent Space

With the continuous progress and development of robotics, mobile robots have been widely used in many different fields. In the power grid, mobile robots are used to inspect electrical equipment, which greatly reduce the investment of manpower and material resources. However, in many cases, mobile robots need to work in a constantly changing and complex environment. Because they cannot obtain environmental information in time, it is often difficult to make path planning. In response to this problem, this paper proposes a path planning method for mobile robots based on improved reinforcement learning. This method establishes a grid environment model, defines the return value through the number of steps of the robot, and then proposes a changing action selection strategy for the balance between the robot’s exploration and utilization of the environment in reinforcement learning, so that the exploration factor dynamically change with the increase of the robot’s exploration degree of the environment, thus speeding up the convergence speed of the learning algorithm. Simulation results show that this method can realize autonomous navigation and path planning of mobile robots in complex environments. Compared with traditional algorithms, it greatly reduces the number of iterations.

Path planning is a fundamental task in autonomous mobile robot navigation and one of the most computationally intensive tasks. In this work, a parallel version of the bacterial potential field (BPF) method for path planning in mobile robots is presented. The BPF is a hybrid algorithm, which makes use of a bacterial evolutionary algorithm (BEA) with the artificial potential field (APF) method, to take advantage of intelligent and classical methods. The parallel bacterial potential field (parallel-BPF) algorithm is implemented on a graphics processing unit (GPU) to speed up the path planning computation in mobile robot navigation. Simulation results to validate the analysis and implementation are provided; the experiments were specially designed to show the effectiveness and the efficiency of the parallel-BPF algorithm.

Mobile robot is an important part of robotics. Along with the rapid progress of science and technology, human society has more and more strong demand for mobile robots. The environment which the robot faces has become increasingly complex, and robots' capability of obstacle avoidance is expected at a higher level of requirements. This paper describes map building and global path planning when the mobile robot moves in a 2D plane. Firstly, the thesis summarizes the methods of map building and path planning on mobile robot and analyzes the advantages and disadvantages of these methods. Secondly, according to the needs of high-accuracy map building and high-performance path planning in practice, we use the method called "Map Forgetting" to increase mapping accuracy and take D <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">*</sup> algorithm to ensure the planning efficiency. Finally, the program architecture from information perception to base controller based on the robot operating system (ROS) is established. We build simulation platform and entity platform based for testing. The experiments show that the mapping accuracy and programming efficiency satisfy design requirements.

As the demand for autonomous mobile robots has risen sharply, the collision-free path planning/navigation problem has become and still is a focus of attention for many researchers. This paper covers a rangeof path planning approaches for various types of mobile robots, such as ground mobile robots, unmanned aerial vehicles, and autonomous vehicles. The literature is classified into two categories: global path planning and reactive path planning. To help readers comprehend the flow within each category, we analyze and compare each category from the perspectives of environmental modeling, optimization criteria, and different methods for path planning. In comparison to earlier survey articles, we place a greater emphasis on Artificial Intelligence (AI) and self-learning navigation methods. In particular, different robot kinematic models are also discussed in thisarticle. Finally, we indicated some future research directions.

Robot navigation is challenging for mobile robots technology in environments with maps. Since finding an optimal path for the agent is complicated and time consuming, path planning in robot navigation is an axial issue. The objective of this paper is to find a reasonable relation between parameters used in the path planning algorithm in a platform which a robot will be able to move from the start point in a dynamic environment with map and plan an optimal path to specified goal without any collision with moving and static obstacles. For this purpose, an asymptotically optimal version of Rapidly-exploring Random Tree RRT algorithm, named RRT* is used. The algorithm is based on an incremental sampling which covers the whole space and acts fast. Moreover this algorithm is computationally efficient, therefore it can be used in multidimensional environments. The obtained results indicate that a feasible path for mobile holomonic robot may be found in a short time by using this algorithm. Also different standard distances measurements like (Chebyshev, Euclidean, and City Block) are examined, and coordinated with sampling node number in order to reach the suitable result based on environment circumstances.

&lt;p align="left"&gt;Path planning is an essential task for the navigation of Autonomous Mobile Robot. This is one of the basic problems in robotics. Path planning algorithms are classified as global or local, depending on the knowledge of surrounding environment. In local path planning, the environment is unknown to the robot, and sensors are used to detect the obstacles and to avoid collision. Bug algorithms are one of the frequently used path planning algorithms where a mobile robot moves to the target by detecting the nearest obstacle and avoiding it with limited information about the environment. This proposed Critical-PointBug algorithm, is a new Bug algorithm for path planning of mobile robots. This algorithm tries to minimize traversal of obstacle border by searching few important points on the boundary of obstacle area as a rotation point to goal and end with a complete path from source to goal.&lt;/p&gt;

PurposeThe purpose of this paper is to put forward a rolling assistant robot with two rolling modes, and the multi-mode rolling motion strategy with path planning algorithm, which is suitable to this multi-mode mobile robot, is proposed based on chessboard-shaped grid division (CGD).Design/methodology/approachBased on the kinematic analysis and motion properties of the mobile parallel robot, the motion strategy based on CGD path planning algorithm of a mobile robot with two rolling modes moving to a target position is divided into two parts, which are local self-motion planning and global path planning. In the first part, the mobile parallel robot can move by switching and combining the two rolling modes; and in the second part, the specific algorithm of the global path planning is proposed according to the CGD of the moving ground.FindingsThe assistant robot, which is a novel 4-RSR mobile parallel robot (where R denotes a revolute joint and S denotes a spherical joint) integrating operation and rolling locomotion (Watt linkage rolling mode and 6R linkage rolling mode), can work as a moving spotlight or worktable. A series of simulation and prototype experiment results are presented to verify the CGD path planning strategy of the robot, and the performance of the path planning experiments in simulations and practices shows the validation of the path planning analysis.Originality/valueThe work presented in this paper is a further exploration to apply parallel mechanisms with two rolling modes to the field of assistant rolling robots by proposing the CGD path planning strategy. It is also a new attempt to use the specific path planning algorithm in the field of mobile robots for operating tasks.

PLANNING OPTIMAL PATHS IN A PARTIALLY UNKNOWN ENVIRONMENT

The recent advances in robotics has resulted in a more convenient use of mobile robots in alications such as assisting the disabled, deliveries and domestic purposes. The main challenge faced by mobile robots is navigation in a dynamic environment, which is path planning for dynamic obstacle avoidance. This paper proposes a novelty method for solving the path planning problem for mobile robots posed by dynamic obstacles based on SLAM (Simultaneous Localisation and Maing) algorithm and Reinforcement Learning. The algorithms implemented relied on the Kinect sensor for maing and rotary encoder for localisation of the robot in the map. The SLAM algorithm implemented resulted in a mean error metric of 4.07%. The modified Q-learning algorithm implemented in this paper allowed the mobile robot to avoid dynamic obstacles by re-planning the path to find another optimal path different from the previously set global optimal path. From the investigation, it was shown that it is possible for a robot to navigate in a dynamic using the Reinforcement Learning technique.

This paper is introduced to be the obstacle recognition, path planning and oscillating control for an automobile crane system on-line. We studied the autonomous mobile overhead traveling crane so for. In particular, the research of obstacle recognition, path plan and oscillating control is carried out. For on-line obstacle recognition, the obstacle recognition system using the ultrasonic sensor(USS) is built. And on-line partial obstacle recognition of the conveyance environment is carried out using this obstacle recognition system. For an on-line path planning, the path plan system which it could process with the information of transfer object position and the obstacle position that is recognized by obstacle recognition system in a short time was built. The feedback transfer control system in consideration of oscillating control is built. Finally, the usefulness of the system that these were integrated is proved by an experiment. Keywords-component; Automation, Mobile Robot, Overhead Traveling Crane, On-line Path Planning, Oscillating control. I. INTORODUCTION OF THIS PAPER An overhead traveling crane system has an advantage of selecting 3rd-dimentionally and freely a transfer path achieving a freedom thereof. Therefore, automation and autonomy thereof is desired to increase the operational freedom of FMS(Flexible Manufacturing System). In the present, the overhead traveling crane is mainly operated by an expert operator to ensure safety during transferring. The development of an autonomous mobile overhead traveling crane is necessary for problem of the lack of skillful operators or the problem of work efficiency. For the reason, the results of research about an autonomous mobile overhead traveling crane system are presented by us[1],[2],[4],[5]. In particular, our research for path planning was conducted from various points of view[1],[2],[4],[5]. In a past research, we has made an experimental apparatus of on overhead traveling crane shown in Fig.1, and an autonomous mobile traveling crane system with function of the obstacle recognition, path planning on-line and oscillation control has been constructed. In this visual and planning system, when there was the change of an obstacle conveying, it was necessary for the path plan to be handled in a short time and had a problem with on-line processing in a conventional study. And the path planning system is constructed[2], [4],[5]. We propose the path-planning system that can be quickly carried out with an easy algorithm on-line. The proposed online path planning system is built using two-dimensions pathplanning algorithm[3] to perform a three-dimensional path planning[4],[5]. In this proposed system, path planning can be derived by information of target position and any obstacle position that is recognized by an ultrasonic sensor (USS). This on-line path plan method performs a path plan by the partial information of a transportation environment recognized by USS of the obstacle recognition system during transfer. The path plan of the overhead traveling crane is constructed to the goal position by repeating a suggested process (algorithm). Furthermore, the inspection of the proposed path planning method was evaluated from a view point of a qualitative and a quantitative[5]. In the present condition, the transfer control system in consideration of oscillating control was a feed-forward control system which using the notch filter. Therefore this control system had the problem that oscillating control was not completed when there was remains vibration at the time of change of a path. In this paper, 2 degrees of freedom control system which added the feedback control system to the present control system are designed. And the purpose of this research is to verify the usefulness of the on-line obstacle avoidance of the proposed autonomous mobile overhead traveling crane system. 2nd International Symposium on Computer, Communication, Control and Automation (3CA 2013) © 2013. The authors Published by Atlantis Press 468 Fig. 2 Obstacle recognition system

Currently, robots are used to carry out dangerous tasks such as bomb detonations, spray painting, disaster management and so on. To ensure sufficient robot navigation, various path-planning methods are available in literature. These methods enable robots to create collision-free path in their working environments. Such environments can be either static (laboratory) or dynamic (realistic). The available path planning methods differ with respect to latency, applicability, computational load, power consumption rates and reliability. The ever-increasing applications of the mobile robots require computable and efficient methods. However, most path planning methods are theoretical, not always reliable and require huge computational resources, hence are not suitable for all real time applications. This paper is aimed at studying and comparing some of the available and important path planning methods for autonomous mobile robots in terms of characteristics of path planning methods, such as graph-based methods, sample-based methods and potential field methods. The paper compares characteristics of the path planning methods on the uniform performance index, by designing a comparison criterion. The performance of these methods will be studied based on the designed framework, including reliability, computational requirements, and power consumption rates. Recommendations towards a feasible solution with respect to the robot-working environment and the available resources will be provided.

Mobile Additive Manufacturing (MAM) systems are transforming large-scale fabrication across various industries, particularly in building and construction. This review explores recent advancements and ongoing challenges in deploying mobile robots within dynamic additive manufacturing (AM) environments. A primary focus is placed on mobile robots' path planning and real-time navigation methods, identified as critical knowledge gaps that impact the accuracy of printing trajectories. AI-driven techniques, such as deep learning and reinforcement learning, are presented as promising solutions to these challenges, offering improvements in trajectory optimisation, obstacle avoidance, and multi-robot cooperation. However, significant obstacles remain, particularly in scaling up MAM operations while maintaining both precision and efficiency. This review provides analysis of the current state of mobile robotic AM, outlines potential pathways for future research, and underscores the alignment of these technologies with Industry 4.0 objectives, emphasising the ongoing need for innovation to unlock the full potential of mobile robotics in large-scale manufacturing.

This paper describes a method for autonomous navigation of mobile robots considering environmental rules based on human tracking. In most cases of traditional mobile robot navigation, static environmental maps are used for global path planning. However, dynamic environmental characteristics such as human frequency maps acquired from tracking many humans should be also utilized for robot navigation coexisting with humans. For example, robots should not pass through areas under construction where any humans don't walk even if the areas are passable from static environmental maps based on just building structure. This paper introduces a robot system that observes human walking for generating human frequency maps without any sensors placed in environments. Also, a path planning method based on environmental rules by human frequency maps is described.

본 논문에서는 이동로봇에 장착된 초음파 센서 및 CCD 카메라를 통해 입력되는 환경정보에서 장애물을 인식 및 추출하여 주행전방의 환경을 구분하는 방법을 제시하였다. 복도 내에서 주행하는 로봇에 탑재된 초음파에 입력된 거리 데이터 정보에 의해 장애물을 검출하고 이동로봇이 미지의 동적인 환경을 초음파센서로 인지하여 지능적으로 목표점을 찾아가는 운행 알고리즘을 제안하고 검증하기 위한 실험결과를 제시하였다. 이동로봇에 다양한 센서 기술들을 이용하여 실내에서 활용하기 적합한 지능적 역할을 수행할 수 있는 다목적용 자율 이동 로봇에 환경인식을 위한 pan/tile 카메라(EVI-D30)를 장착하여 주행실험을 할 수 있도록 하였다. 제작한 로봇의 주행성능을 보이기 위해서 VFF 알고리즘을 적용하여 임의의 환경에서도 자율주행의 실험과 결과를 통해 제시한 방법에 대한 유효성을 검증하였다. This paper deals with the method of using the amended virtual force field technique to avoidance the front environment(wall, obstacles etc.) in navigating by using the environmental informations recognized by a ultrasonic-ring and pan/tilt CCD camera equipped on a mobile robot. we will give an explanation for the robot system architecture designed and implemented in this study and a short review of existing techniques, since there exist several recent thorough books and review paper on this paper. It is proposed the rusult from the experimental run based on a virtual force field(VFF) method to support the validity of the aforementioned architecture of mobile service robot for local navigation and obstacle avoidance for autonomous mobile robots. We will conclude by discussing some possible future extensions of the project. The results show that the proposed algorithm is apt to identify obstacles in an indoor environments to guide the robot to the goal location safely.

Background: Path planning technology has a wide range of applications in many fields. Applications in the field of high technology include: autonomous and touchless action of robots; obstacle avoidance and defense flight of drones; cruise missiles to avoid radar search, anti- bouncing attacks, and to complete the task of sudden defense and demolition. Applications in daily life include: GPS navigation; road planning based on GIS system; urban road network planning and navigation. Applications in the field of decision-making management include: vehicle problem (VRP) in logistics management and similar resource allocation problems in resource management. Routing problems in the field of communication technology. Any planning problem that can be topologized as a point and line network can basically be solved by the method of path planning. Different intelligent algorithms have different characteristics, and their application scope and fields are also different, so it is of great significance for the development of path planning technology to study the path planning intelligent algorithms from the characteristics of the algorithms themselves and their applications. Objective: Analyze the advantages and disadvantages of various types of planning algorithms, look forward to the future development trend of mobile robot path planning, and provide certain ideas for the research of mobile robot path planning. Methods: Search journals, patents, conferences, and papers related to mobile robot path planning, and summarize and analyze the advantages and disadvantages of various planning algorithms. Results: Based on the research results of many scholars, this study summarizes different mobile robot path planning methods. It is divided into four categories: traditional planning, intelligent search, artificial intelligence and local obstacle avoidance. This paper introduces and analyzes the latest research results of these types, including their design ideas, advantages and disadvantages, and improvement measures. The research methods adopted are analyzed in order to maximize the advantages of each algorithm and expand the application field of robot path planning to provide ideas and references. Conclusion: This paper provides guidance for the design and optimization of robot path planning. Finally, this paper summarizes the future development trend of robot path planning, and looks forward to the future development trend and key areas of robot path planning.