A novel method to implement an interactive mobile robot

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.

The robust control of a mobile robot in a rough terrain environment is a challenging endeavour, since any reliance on favourable surface or environmental conditions will inevitably lead to task failure. This paper presents the preliminary development of a unified navigation system used to control a nonholonomic mobile robot in an a priori unknown outdoor domain. Accurate, high-resolution environmental data was gathered from a scanning laser rangefinder (ladar), which constitutes the robot's exteroceptive perception system. Using this data, a 3D tessellated environmental model was created that generically captures terrain traversability. By adapting the rapidly-exploring random tree approach to the tessellated model, an efficient kinodynamic path planning algorithm was devised that enables point-to-point trajectory traversal. This path planning strategy was found to be a computationally efficient method of producing robust and versatile path plans.

Recently MR (Mixed Reality) techniques are used in many fields, and one of them is work support. Work support using MR techniques can display work instructions directly in the work space and help user to work effectively especially in assembling tasks. MR work support for assembling tasks often uses HMD (Head Mounted Display) to construct MR environment. However, there are some problems for the use of HMD, such as a burden to the head, a narrow view and a motion picture sickness. One of the techniques to solve such problems is pepper's ghost which is an optical illusion using a glass. In this paper, we propose the naked eye MR work supporting system for assembling tasks using pepper's ghost. This system enables a beginner to assemble some blocks into one object by the naked eye with a few burdens.

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 is essential for mobile robots to operate efficiently in uncertain environments. As the number of robots increases, centralised approaches struggle to provide feasible solutions within real-time constraints. To address this, an adaptive hybrid strategy-based decentralised path planning algorithm is proposed to solve the real-time path planning problem through decentralised computing. First, various path planning strategies are introduced, including a neural computing planning strategy, a dynamic search planning strategy, and a cluster coordination planning strategy. Then, an intelligent strategy selection mechanism with an adaptive strategy adjustment factor is designed, allowing each robot to dynamically select optimal planning strategies based on their current state and ensuring the planned paths exhibit greater flexibility and adaptability. Finally, the results of the ablation experiment indicate that all three strategies effectively enhance the navigation capabilities of robots in a decentralised mode. The algorithm comparison experiment demonstrates that the proposed algorithm achieves a higher task completion rate and a lower detour percentage in various environments. The decision response experiment shows that our approach has an average decision-making time of approximately 60 ms, which meets the real-time requirements for decentralised path planning of mobile robots in most scenarios.

Aiming at the problem of task path planning for mobile robot in complex multi-obstacle environment, this paper presents a path planning method that can solve time constraints. Considering the kinematics constraints of the robot in the case of constant speed movement, multiple route points are selected in the complex obstacle environment. By setting the vector direction of the robot at the route points, the smooth transition between different sections can be achieved, and the speed and kinematics constraints of the robot can be guaranteed at the same time. Based on the bi-arc, the path planning scheme of the robot is given, and the analytical expressions of path parameters and the range of path length under different conditions are derived. The numerical simulation results show the effectiveness and practicability of the path planning scheme in complex environment.

Mobile robots start to appear in our everyday life, e.g., in shopping malls, airports, nursing homes or warehouses. Often, these robots are operated by non-technical staff with no prior experience/education in robotics. Additionally, as with all new technology, there is certain reservedness when it comes to accepting robots in our personal space. In this work, we propose making use of state-of-the-art Mixed Reality (MR) technology to facilitate acceptance and interaction with mobile robots. By integrating a Microsoft HoloLens into the robot»s operating space, the MR device can be used to a) visualize the robot»s behavior-state and sensor data, b) visually notify the user about planned/future behavior and possible problems/obstacles of the robot, and c) to actively use the device as an additional external sensor source. Moreover, by using the HoloLens, users can operate and interact with the robot without being close to it, as the robot is able to sense with the users» eyes.

This paper addresses the localization of a gas emission source within a real-world human environment with a mobile robot. Our approach is based on an efficient and coherent system that fuses different sensor modalities (i.e., vision and chemical sensing) to exploit, for the first time, the semantic relationships among the detected gases and the objects visually recognized in the environment. This novel approach allows the robot to focus the search on a finite set of potential gas source candidates (dynamically updated as the robot operates), while accounting for the non-negligible uncertainties in the object recognition and gas classification tasks involved in the process. This approach is particularly interesting for structured indoor environments containing multiple obstacles and objects, enabling the inference of the relations between objects and between objects and gases. A probabilistic Bayesian framework is proposed to handle all these uncertainties and semantic relations, providing an ordered list of candidates to be the source. This candidate list is updated dynamically upon new sensor measurements to account for objects not previously considered in the search process. The exploitation of such probabilities together with information such as the locations of the objects, or the time needed to validate whether a given candidate is truly releasing gases, is delegated to a path planning algorithm based on Markov decision processes to minimize the search time. The system was tested in an office-like scenario, both with simulated and real experiments, to enable the comparison of different path planning strategies and to validate its efficiency under real-world conditions.

A* algorithm is widely adopted for path planning in the applications of mobile robots. However, the existence of extended nodes and redundant points after applying A* algorithm may cause a long planned path. To address this issue, an improved A* algorithm is proposed. Localizability of the robot's current node on a grid map is first estimated. Then, the parent node and the grandparent node of current node are referred to build the relation between the actual cost and the heuristic cost. The heuristic cost is weighted to analyze the effect of the actual cost and the heuristic cost on the planned path. Finally, a path planning strategy with adaptive step size is proposed to optimize the path. Simulations show that the proposed algorithm outperforms the A* algorithm and the ant colony algorithm in terms of localizability of the robot and the path cost.KeywordsMobile robotsPath planningLocalizability estimationA* algorithmAdaptive step size

To enhance the navigation and control efficiency of mobile robots in the field of health monitoring, a novel path planning and control strategy for mobile robots with uncertain dynamics based on improved artificial potential fields is proposed in this paper. Specifically, we propose an attractive potential field rotation method to overcome the limitation that traditional artificial potential fields tend to fall into local minima. Then, we define a new class of attractive potential fields to address the goals non-reachable with obstacles nearby (GNRON) and collisions caused by excessive attractive force at long distances from the target point. Furthermore, a control law is proposed for the mobile robot with uncertain dynamics, and the stability of the closed-loop system is rigorously proven using the Lyapunov method. Finally, the feasibility and effectiveness of the proposed method are verified by simulations and experiments.

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.

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.

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.

In this study, we designed an autonomous mobile robot based on the rules of the Federation of International Robotsoccer Association (FIRA) RoboSot category, integrating the techniques of computer vision, real-time image processing, dynamic target tracking, wireless communication, self-localization, motion control, path planning, and control strategy to achieve the contest goal. The self-localization scheme of the mobile robot is based on the algorithms featured in the images from its omni-directional vision system. In previous works, we used the image colors of the field goals as reference points, combining either dual-circle or trilateration positioning of the reference points to achieve selflocalization of the autonomous mobile robot. However, because the image of the game field is easily affected by ambient light, positioning systems exclusively based on color model algorithms cause errors. To reduce environmental effects and achieve the self-localization of the robot, the proposed algorithm is applied in assessing the corners of field lines by using an omni-directional vision system. Particularly in the mid-size league of the RobotCup soccer competition, selflocalization algorithms based on extracting white lines from the soccer field have become increasingly popular. Moreover, white lines are less influenced by light than are the color model of the goals. Therefore, we propose an algorithm that transforms the omni-directional image into an unwrapped transformed image, enhancing the extraction features. The process is described as follows: First, radical scan-lines were used to process omni-directional images, reducing the computational load and improving system efficiency. The lines were radically arranged around the center of the omni-directional camera image, resulting in a shorter computational time compared with the traditional Cartesian coordinate system. However, the omni-directional image is a distorted image, which makes it difficult to recognize the position of the robot. Therefore, image transformation was required to implement self-localization. Second, we used an approach to transform the omni-directional images into panoramic images. Hence, the distortion of the white line can be fixed through the transformation. The interest points that form the corners of the landmark were then located using the features from accelerated segment test (FAST) algorithm. In this algorithm, a circle of sixteen pixels surrounding the corner candidate is considered and is a high-speed feature detector in real-time frame rate applications. Finally, the dual-circle, trilateration, and cross-ratio projection algorithms were implemented in choosing the corners obtained from the FAST algorithm and localizing the position of the robot. The results demonstrate that the proposed algorithm is accurate, exhibiting a 2-cm position error in the soccer field measuring 600 cm2 x 400 cm2.

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.