Placing Artificial Visual Landmarks in a Mobile Robot Workspace

The construction of a smart autonomous mobile robot system that can operate in an anonymous environment can be difficult. Building an autonomous mobile robot requires collecting data from sensors and cameras in order to recognize the surrounding environment, get knowledge of existing landmarks, and figure out its position and orientation. Many research studies have been carried out in recent years providing algorithms for achieving autonomous motion of mobile robot systems and object detection. The most common platform utilized for robot motion design is ROS (Robot Operating System) and for object detection YOLO (You Only Look Once) is a common choice. Additionally, the autonomous system needs motion planning in a systematic way to avoid obstacles by using LIDAR or other sensing methods generating the feedback signals required for control of motion around occupied spaces. One of the most effective and versatile feedback systems for mobile robot control can be constructed using visual feedback, which relies on a high-resolution camera that processes the image in real-time and the result is fed back to the control system for decision making. Visual feedback can be employed alone without sensors for feedback control of autonomous mobile robots, and it can be used for multiple tasks like path planning, obstacle avoidance, object detection, object recognition in addition to motion control. In this research, results of design and construction of an autonomous mobile rescue robot for finding victims through video data are presented. It is aimed to find victims via applying video as a data collection and feedback generation tool in the autonomous mobile robot control system. The visual feedback and YOLO v3 are used not only for finding victims but also for motion control. Our autonomous mobile robot is designed to move smoothly to the victim's direction and avoids obstacles by using ROS Global Planner. The robot is tested in multiple scenarios in different locations, various obstacle and victim numbers, and satisfactory results with good performances are obtained.

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.

During the past two decades, researchers in mobile robotics have dealt with different path planning methods. In most cases, the methods goal is to find free collision paths; which will meet the initial and final configurations to complete a mission. Some researchers have proposed methods where the robot’s configuration is perfectly known at each instant during the planning and navigation stages(Moreno & Dapena, 2003) . This is not always possible. Dealing with uncertainty, in the planning stage, is essential when the position errors values approach values close to the allowed thresholds for the mission. Plans based on geometrical models, assuming null uncertainty, are clearly insufficient when the mobile robot has to coexist with humans or other kind of difficult situations. Thus, the use of planners, which not explicitly deal with uncertainty, is limited to simple situations, where the errors are less than the allowed thresholds to accomplish the missions (Bouilly & Simeon, 1996). In general, the basic requirements for the autonomous navigation of a mobile robot are environmental recognition, path planning, driving control and location estimation/correction capabilities (Nakamura, 1991, Haralick & Shapiro). The location estimation and correction capabilities are practically indispensable for the autonomous mobile robot to execute the given tasks efficiently. There are many factors involved in obtaining accurate location information while the mobile robot is moving (Sim & Dudek). To get reliable and precise location data, sensor fusion techniques (Ayache & Faugeras, 1989, Zhou & Sakane, 2001) have also been developed. When a CCD camera is utilized under good illumination conditions, certain patterns or shapes of objects are also effective for determining the location (Han et.al, 1999, Segvic & Ribaric, 2001). Similarly when a mobile robot is moving in a building, the walls, edges, and doors can be utilized for position estimation (Betke, 1994 David, 1989). Most researches (Choset, 2001, Sanisa, 2001, Philippe & Colle, 2001) focus on the indoor navigation of a mobile robot in a well-structured environment. In other words, beacons, doors, and corridor edges are utilized to estimate the current location of the mobile robot. However, in cases such as when a mobile robot is navigating under a deep sea or in a forest (Kim, et.al, 2001), there are no landmarks that can be utilized to determine the location. This paper considers the situations where a mobile robot and a walking human coexist in a structured intelligent environment, such as assembly line in a factory. In these cases, one cannot utilize any landmarks or special features known a priori (Lallet & Lacroix, 1998,

An Autonomous mobile robot needs to be equipped with the intelligence of autonomous navigational ability in its operating environment. The ability is mainly decided by the way the robot perceives the environment, the way it plans the navigational path to be traversed, and how it executes its motion actuation. While designing the mobile robots, the accurate perception of the environment is taken care of by intelligent sensing modules. Further, the strategy of navigational path planning is implemented by robust algorithms that ensure the shortest path traversal, hence lesser time consumption and efficient obstacle avoidance capability. The choice of a path planning algorithm depends on the needs of the application employing the mobile robot. The level of autonomy of the mobile robot depends on the way the designer exploits the advantages of the path planning algorithm. The proposed work aims at comparing the intricacies of navigational path planning algorithms, namely, Dijkstra's algorithm, Genetic algorithm and A* algorithm, that can be employed for mobile robots. The demonstrational results of navigational path planning implemented using the A* algorithm is presented

Path planning algorithms are the most significant area in the robotics field. Path planning (PP) can be defined as the process of determining the most appropriate navigation path before a mobile robot moves. Path planning optimization refers to finding the optimal or near-optimal path. Multi-objective optimization (MOO) is concerned with finding the best solution values that satisfy multiple objectives, such as shortness, smoothness, and safety. MOO present the challenge of making decisions while balancing these contradictory issues through compromise (tradeoff). As a result, there is no single solution appropriate for all purposes in MOO, but rather a range of solutions. Several objectives are considered as part of this study, including path security, length, and smoothness, when planning paths for autonomous mobile robots in a dynamic environment with a moving target. Particle swarm optimization (PSO) algorithms are combined with bat algorithms (BA) to make a balance between exploration and exploitation. PSO algorithms used to optimize two important parameters of the bat algorithm. The proposed solution is tested through several simulations based on varying scenarios. The results demonstrate that mobile robots can travel clearly and safely along short paths and smoothly, proving this method's efficiency.

PLANNING OPTIMAL PATHS IN A PARTIALLY UNKNOWN ENVIRONMENT

The mobile robot has been one of the hottest fields in recent years, it has been widely applied in the most critical and efficient occasions in the industry, meanwhile, path planning becomes a frontier and interesting research topic for the mobile robot. In this paper, firstly mobile robot is introduced, some universal path planning algorithms of the mobile robot are reviewed, then a fuzzy logic and filter smoothing based on the data from the laser scan sensor is proposed, the path planning and autonomous obstacle avoidance based on this algorithm is emphasized because it can automatically find the best path according to the size and position of gaps between the obstacles in the dynamic environment, finally our designed mobile robot and corresponding Android APP are introduced, the path planning and obstacle avoidance algorithms are tested on this mobile robot, the testing results show this algorithm has the advantage of responding quickly, global optimized and hardware resource saved compared with other algorithms, it is suitable for the mobile robot that is designed on the embedded system and it can meet our designed requirement.

Accurate and reliable localization is a base requirement for a mobile robot. Landmark localization is a very common technique to do this. The arrangement of landmarks is important for reliable and accurate estimation of localization data. In this paper the optimized arrangement of artificial landmarks is discussed and a solution for building an accurate and reliable localization system based on combining artificial and natural landmarks is presented. Landmark placement is done by definition and optimization of a confidence level describing the expected localization estimation error. Areas in the environment which are ill conditioned by natural landmarks are furnished with additional artificial landmarks. The proposed system has been tested in a simulation and with a mobile robot in a real office environment.

Mobile robots are mostly used in many industrial and military applications. Researches in the path planning is one of the most important aspects in mobile robot researches. Path planning for a mobile robot need to find a collision free path through the robot's environment with obstacles from the specified start location to a desired(target) goal location while satisfying certain optimization conditions. Existing path planning methods like graphical methods such as visibility graph, the potential field and the cell decomposition are designed for static environments, in which there are stationary obstacles. In practical systems such as Marine Science Research, Robots in Industry, and military combat applications, robots usually face dynamic environments where both moving and stationary obstacles exist. This project proposes a heuristic based method to search the feasible initial path efficiently. The heuristic based method is then combined into the simulated annealing algorithm based approach for dynamic robot path planning. Therefore the quality of the solution is characterised by the length of the planned path and it is improved with the combined heuristic method in the simulated annealing based approach for both runtime and offline path planning.

Problem. The issue of path planning for a mobile robot is one of the most important ones of mobile robotics. Proper path planning ensures the safety of the robot and its environment, the efficiency of the tasks carried out by a robot, saves time and energy consumption for these tasks, etc. Therefore, research is constantly conducted on the implementation of new and improving existing optimization methods for the path planning for a mobile robot. The utilization of classical optimization methods is limited by their significant drawbacks, such as computational complexity and long time for searching the optimal path. To eliminate these issues, heuristic and then metaheuristic methods have been developed. Among metaheuristic methods, bio-inspired optimization methods, which are based on evolutionary processes in nature, as well as the behaviour of living organisms, are becoming increasingly popular. Goal. This paper aims to analyse the most popular bio-inspired algorithms used for mobile robot path planning. Methodology. The paper briefly reviews the bio-inspired optimization methods that are applicable to the path planning of a mobile robot. Particular emphasis is given to swarm intelligence algorithms, in which the relatively simple behaviour of individual agents interacting with each other and with the environment allows a swarm of these agents to achieve a given goal. Results. A classification of bio-inspired optimization methods used for mobile robot path planning is proposed. Pseudocodes for swarm optimization algorithms that are most frequently applied in mobile robotics are presented. Originality. This paper is one of the first in Ukraine to offer a comprehensive overview of bio-inspired methods of optimization used for mobile robot path planning. Practical value. The implementation of the considered algorithms in mobile robot control systems will improve the efficiency of robots in performing their assigned tasks. The given pseudocodes will simplify the development of software to implement the mentioned algorithms.

This paper considers the real-time navigation and obstacle avoidance of mobile robots in complex environments. A novel local environment perception and navigation framework for mobile robot navigation based on the elevation information is proposed. A local elevation map is introduced to perceive and map obstacles in the local environment. Calculating obstacles’ heights and shapes by local elevation maps, the terrain traversability of the surrounding environment of a robot is determined, and a two-dimensional local occupancy grid map for robot path planning and navigation in local environment is generated. The effectiveness of our approach is validated experimentally through real-time navigation and obstacle avoidance in the artificial environment. The experimental results show that using the proposed framework, a mobile robot can safely reach its target position while avoiding the non-crossable area. For indoor mobile robots with limited computing power, in complex environments, it could provides more safe navigation and path-planning.

Purpose This review paper aims to provide a comprehensive review of Dubins robot path planning, which has become increasingly important in mobile robotics, autonomous vehicles and industrial applications. The authors identify key developments, analyze existing approaches and highlight the fundamental constraints, algorithms and future opportunities involving curvature-constrained, forward-only path planning. Design/methodology/approach The review consists of three parts: basic Dubins theory, seven decades of development and future opportunities. In the first part, the authors summarize problem formulations and representative computational strategies/algorithms. In the second part, the seventy years of development are divided into four stages, with each stage summarizing developments in important topics such as point-to-point planning, obstacle avoidance and variable speed planning. In addition, the authors analyze current challenges and evaluate recent extensions in cooperative, real-time and learning-based planning frameworks. Findings Dubins path planning has evolved from simple analytical models to complex multirobot and dynamic scenarios. While exact solutions exist for basic tasks, the scalability, obstacle constraints and real-time responsiveness remain active challenges. Promising future directions include integrating machine learning for adaptive planning, and extending to Reeds–Shepp and nonholonomic systems in three-dimensional environments. Originality/value This review offers a structured and in-depth overview of path planning for Dubins robots, connecting core theoretical concepts with the latest developments and real-world implementations. Unlike prior surveys that roughly treat general nonholonomic planning or sampling-based methods, this paper for the first time emphasizes the curvature-constrained planning, especially under the Dubins constraints. By identifying underexplored areas such as dynamic environments, real-time applications, this review offers actionable insights and future research directions valuable to both researchers and practitioners in mobile and industrial robots.

A detailed survey of the available literature on path planning of Autonomous Ground Vehicle (AGV) is conducted, including the overview of single-robot control architectures, different path-planning approaches, analyses of current sensor systems and different velocity estimation techniques. In order to achieve the full autonomous operation of a mobile robot, path or motion planning, i.e., the planning of a collision-free path from a start to goal position through a collection of obstacles, is the fundamental task in the field of autonomous control systems. As AGVs are used in a wide variety of applications to perform autonomous tasks, organising their intelligence plays a key role in successfully programming a robot for a particular application and applying the right control architecture makes the autonomous control problem easier to solve. For autonomous vehicles, sensors play an important role in acquiring different attributes of the working environment and, by extracting meaningful information from these data, the autonomous system can acquire knowledge about its environment. Moreover, different velocity estimation techniques are reviewed in the context of dealing with the dynamic obstacles. A brief review of the available literature on path-planning approaches and techniques is provided.

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

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.