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In this paper, a planning method based on the spatiotemporal variable-step-size A* algorithm is proposed to address the problem of safe trajectory planning for incremental, wheeled, mobile robots in complex motion scenarios with multiple robots. After constructing the known conditions, the spatiotemporal variable-step-size A* algorithm is first used to perform a collision-avoiding initial spatiotemporal trajectory search, and a variable time step is utilized to ensure that the robot completes the search at the target speed. Subsequently, the trajectory is instantiated using B-spline curves in a numerical optimization considering constraints to generate the final smooth trajectory. The results of simulation tests in a field-shaped, complex, dynamic scenario show that the proposed trajectory planning method is more applicable, and the results indicate higher efficiency compared to the traditional method in the incremental robot trajectory planning problem. [ABSTRACT FROM AUTHOR]

This work presents a retrospective analysis of indoor CO2 measurements obtained with a mobile robot in an educational building after the COVID-19 lockdown (May 2021), at a time when public activities resumed with mandatory local pandemic restrictions. The robot-based CO2 measurement system was assessed as an alternative to the deployment of a net of sensors in a building in the pandemic period, in which there was a global stock outage of CO2 sensors. The analysis of the obtained measurements confirms that a mobile system can be used to obtain interpretable information on the CO2 levels inside the rooms of a building during a pandemic outbreak. [ABSTRACT FROM AUTHOR]

Autonomous mobile robots are essential to the industry, and human–robot interactions are becoming more common nowadays. These interactions require that the robots navigate scenarios with static and dynamic obstacles in a safely manner, avoiding collisions. This paper presents a physical implementation of a method for dynamic obstacle avoidance using a long short-term memory (LSTM) neural network that obtains information from the mobile robot's LiDAR for it to be capable of navigating through scenarios with static and dynamic obstacles while avoiding collisions and reaching its goal. The model is implemented using a TurtleBot3 mobile robot within an OptiTrack motion capture (MoCap) system for obtaining its position at any given time. The user operates the robot through these scenarios, recording its LiDAR readings, target point, position inside the MoCap system, and its linear and angular velocities, all of which serve as the input for the LSTM network. The model is trained on data from multiple user-operated trajectories across five different scenarios, outputting the linear and angular velocities for the mobile robot. Physical experiments prove that the model is successful in allowing the mobile robot to reach the target point in each scenario while avoiding the dynamic obstacle, with a validation accuracy of 98.02%. [ABSTRACT FROM AUTHOR]

Navigation of a mobile robot in an unknown environment ensuring the safety of the robot and its surroundings is of utmost importance. Traditional methods, such as pathplanning algorithms, simultaneous localization and mapping, computer vision, and fuzzy techniques, have been employed to address this challenge. However, to achieve better generalization and self-improvement capabilities, reinforcement learning has gained significant attention. The concern of privacy issues in sharing data is also rising in various domains. In this study, a deep reinforcement learning strategy is applied to the mobile robot to move from its initial position to a destination. Specifically, the Deep Q-Learning algorithm has been used for this purpose. This strategy is trained using a federated learning approach to overcome privacy issues and to set a foundation for further analysis of distributed learning. The application scenario considered in this work involves the navigation of a mobile robot to a charging point within a greenhouse environment. The results obtained indicate that both the traditional deep reinforcement learning and federated deep reinforcement learning frameworks are providing 100% success rate. However federated deep reinforcement learning could be a better alternate since it overcomes the privacy issue along with other advantages discussed in this paper. [ABSTRACT FROM AUTHOR]

Aiming at the problem that the dynamic window approach (DWA) has low planning efficiency and it is difficult to avoid fast-moving obstacles, an improved adaptive DWA based on risk degree function is proposed in this paper. The risk function is designed and introduced into the evaluation function of the traditional DWA to evaluate the risk of collision between dynamic obstacles and the robot, so that the robot can effectively avoid faster obstacles. Then, according to the fuzzy control principle, the adaptive weight coefficient is designed to improve the evaluation function, so that the mobile robot can move to the target point more efficiently. The simulation results show that compared with the traditional DWA, the adaptive DWA based on risk degree function reduces the time of completing the planning task by about 8%, and the path length after the completion of the planning by about 8%, it indicates that the improved algorithm has higher efficiency. After 50 repeated experiments, using the adaptive DWA based on risk degree function successfully avoids all obstacles to complete the planning task, which shows that this algorithm has higher security. [ABSTRACT FROM AUTHOR]

When the traditional RBPF-SLAM algorithm is applied to indoor positioning and mapping of mobile robots, it is prone to inaccurate positioning and mapping due to the long computation time of the front-end matching algorithm and the presence of particle missing in the back-end optimization algorithm. We popose to optimize the matching efficiency of lidar using the PL-ICP algorithm in the front-end of the SLAM system. In the backend of the SLAM system, the RBPF-SLAM algorithm is optimized using an improved beetle swarm algorithm. The experimental results show that the PL-ICP algorithm can significantly reduce computation time in front-end matching. In terms of backend optimization, the improved RBPF-SLAM algorithm can significantly reduce the number of particles and resampling, effectively improving the accuracy of indoor positioning and mapping for mobile robots. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

In order to address the issues with the jump point search algorithm in the process of pathfinding, such as numerous path inflection points, numerous intermediate search hop points, numerous extended nodes, and lengthy pathfinding time in the process of finding jump points, this paper proposed the improved bidirectional dynamic JPS algorithm. The improved algorithm dynamically defined the target points in the forward and reverse expansion directions, dynamically defined the heuristic function, and used the dynamic constraint ellipse to restrict the expansion region of the algorithm to distinguish the expansion priorities of the inner and outer regions of the ellipse. The new cost minimum point served as the focus of the new ellipse, and the orientation and constraint region were dynamically changed as the extension from the starting point and goal point to each other. The simulation results show that the optimized bidirectional dynamic JPS algorithm has a certain performance in general maps, especially in indoor environment maps with fewer obstacles and the target point is close to the starting point. [ABSTRACT FROM AUTHOR]

An improved Ant Colony Optimization (ACO) algorithm, named IACO, is proposed to address the inherent limitation of slow convergence, susceptibility to local optima and excessive number of inflection in traditional ACO when solving path planning problems. To this end, firstly, the search direction number is expanded from 4 or 8 into 32; Secondly, the distance heuristic information is replaced by an area heuristic function, which deviated from the traditional approach that only considers pheromone information between two points; Then, the influence of path angle and number of turns is taken into account in the local pheromone update. Additionally, a reward and punishment mechanism is employed in the global pheromone update to adjust the pheromone concentrations of different paths; Furthermore, an adaptive update strategy for pheromone volatility factor adaptive is proposed to expand the search range of the algorithm. Finally, simulation experiments are conducted under various scenarios to verify the superiority and effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

This paper proposes a novel tour guide robot, "ASAHI ReBorn", which can lead a guest by hand one-on-one while maintaining a proper distance from the guest. The robot uses a stretchable arm interface to hold the guest's hand and adjusts its speed according to the guest's pace. The robot also follows a given guide path accurately using the Robot Side method, a robot navigation method that follows a pre-defined path quickly and accurately. In addition, a control method is introduced that limits the angular velocity of the robot to avoid the robot's quick turn while guiding the guest. We evaluated the performance and usability of the proposed robot through experiments and user studies. The tour-guiding experiment revealed that the proposed method that keeps distance between the robot and the guest using the stretchable arm enables the guests to look around the exhibits compared with the condition where the robot moved at a constant velocity. [ABSTRACT FROM AUTHOR]

The integration of machine learning and robotics brings promising potential to tackle the application challenges of mobile robot navigation in industries. The real-world environment is highly dynamic and unpredictable, with increasing necessities for efficiency and safety. This demands a multi-faceted approach that combines advanced sensing, robust obstacle detection, and avoidance mechanisms for an effective robot navigation experience. While hybrid methods with default robot operating system (ROS) navigation stack have demonstrated significant results, their performance in real time and highly dynamic environments remains a challenge. These environments are characterized by continuously changing conditions, which can impact the precision of obstacle detection systems and efficient avoidance control decision-making processes. In response to these challenges, this paper presents a novel solution that combines a rapidly exploring random tree (RRT)-integrated ROS navigation stack and a pre-trained YOLOv7 object detection model to enhance the capability of the developed work on the NAV-YOLO system. The proposed approach leveraged the high accuracy of YOLOv7 obstacle detection and the efficient path-planning capabilities of RRT and dynamic windows approach (DWA) to improve the navigation performance of mobile robots in real-world complex and dynamically changing settings. Extensive simulation and real-world robot platform experiments were conducted to evaluate the efficiency of the proposed solution. The result demonstrated a high-level obstacle avoidance capability, ensuring the safety and efficiency of mobile robot navigation operations in aviation environments. [ABSTRACT FROM AUTHOR]

The loading and unloading operations of smart logistic application robots depend largely on their perception system. However, there is a paucity of study on the evaluation of Lidar maps and their SLAM algorithms in complex environment navigation system. In the proposed work, the Lidar information is finetuned using binary occupancy grid approach and implemented Improved Self-Adaptive Learning Particle Swarm Optimization (ISALPSO) algorithm for path prediction. The approach makes use of 2D Lidar mapping to determine the most efficient route for a mobile robot in logistical applications. The Hector SLAM method is used in the Robot Operating System (ROS) platform to implement mobile robot real-time location and map building, which is subsequently transformed into a binary occupancy grid. To show the path navigation findings of the proposed methodologies, a navigational model has been created in the MATLAB 2D virtual environment using 2D Lidar mapping point data. The ISALPSO algorithm adapts its parameters inertia weight, acceleration coefficients, learning coefficients, mutation factor, and swarm size, based on the performance of the generated path. In comparison to the other five PSO variants, the ISALPSO algorithm has a considerably shorter path, a quick convergence rate, and requires less time to compute the distance between the locations of transporting and unloading environments, based on the simulation results that was generated and its validation using a 2D Lidar environment. The efficiency and effectiveness of path planning for mobile robots in logistic applications are validated using Quanser hardware interfaced with 2D Lidar and operated in environment 3 using proposed algorithm for production of optimal path. [ABSTRACT FROM AUTHOR]

We propose a methodology that uses the relative positional information of multiple antennas to estimate the Wrong Fix, which refers to an erroneous determination of the carrier-phase ambiguity utilized in GNSS satellites. The proposed approach is based on the fundamental notion that the mutual positional relationship of multiple antennas mounted on a mobile robot remains constant, and it uses machine-learning techniques based on the relative position information among the antennas to identify instances of Wrong Fixes. The relative distance between the antennas is derived from the real-time kinematic (RTK) position information of each antenna. The confidence level of the RTK positioning results was calculated using logistic regression, considering the measurement error with respect to the true value. To determine the Wrong Fixes, a labeled dataset was constructed, indicating that data were categorized as wrong fixes when the error from the true value exceeded 0.1 m. This dataset served as the training database for the logistic regression model. Experimental results demonstrate that the proposed methodology effectively reduced the root mean squared error between the measured location, classified as fixed by a trained discriminator, and the true value. [ABSTRACT FROM AUTHOR]

In this study, a simultaneous localization and mapping (SLAM) system for a two-wheeled mobile robot was developed in an indoor environment using RGB images, depth images, and wheel odometry. The proposed SLAM system applies planar motion constraints performed by a robot in a two-dimensional space to robot poses parameterized in a three-dimensional space. The formulation of these constraints is based on a conventional study. However, in this study, the information matrices that weigh the planar motion constraints are given dynamically based on the wheel odometry model and the number of feature matches. These constraints are implemented into the SLAM graph optimization framework. In addition, to effectively apply these constraints, the system estimates two of the rotation components between the robot and camera coordinates during SLAM initialization using a point cloud to construct a floor recovered from a depth image. The system implements feature-based Visual SLAM software. The experimental results show that the proposed system improves the localization accuracy and robustness in dynamic environments and changes the camera-mounted angle. In addition, we show that planar motion constraints enable the SLAM system to generate a consistent voxel map, even in an environment of several tens of meters. [ABSTRACT FROM AUTHOR]

In areas inaccessible to humans, such as the lunar surface and landslide sites, there is a need for multiple autonomous mobile robot systems that can replace human workers. Robots are required to remove water and sediment from landslide sites such as river channel blockages as soon as possible. Conventionally, several construction machines are deployed at civil engineering sites. However, owing to the large size and weight of conventional construction equipment, it is difficult to move multiple units of construction equipment to a site, which results in significant transportation costs and time. To solve such problems, this study proposes GREEMA: growing robot by eating environmental material, which is lightweight and compact during transportation and functions by eating environmental materials once it arrives at the site. GREEMA actively takes in environmental materials, such as water and sediment, uses them as its structure, and removes them by moving itself. In this study, two types of GREEMAs were developed and experimentally verified. First, we developed a fin-type swimming robot that passively takes in water into its body using a water-absorbing polymer and forms a body to express its swimming function. Second, we constructed an arm-type robot that eats soil to increase the rigidity of its body. We discuss the results of these two experiments from the viewpoint of explicit-implicit control and describe the design theory of GREEMA. [ABSTRACT FROM AUTHOR]

Collaborative robots combined with mobile platforms can move freely and have a wide work area. Technological developments in AI and vision have also led to increased research on recognition of objects and surroundings using mobile manipulators. However, even though such trends have resulted in growing demand for research on mobile manipulators, their high prices make them difficult to purchase. We solved this problem using 3D printing. A mobile manipulator was manufactured by replacing more expensive reducers with 3D printed planetary gear and cycloid gear reducers. As well, a mecanum in-wheel motor equipped with a 3D printed cycloid reducer was developed to increase the usability of the interior space of the mobile platform. The study employed a task-space control algorithm, which controls the mobile platform and the manipulator simultaneously to ensure effective control of the mobile manipulator. Task-space motion control performance was verified through Gazebo simulation and experiment. Additionally, the repeatability of the task-space position control was measured through experiments and its performance was compared with a commercial manipulator. Finally, we have developed a mobile manipulator that can perform similar levels of work as a commercial robot at one tenth the price of a general commercial mobile manipulator. [ABSTRACT FROM AUTHOR]

Gestures are a common way people communicate. Gesture-based teleoperation control systems tend to be simple to operate and suitable for most people’s daily use. This paper employed a LeapMotion sensor to develop a mobile robot control system based on gesture recognition, which mainly established connections through a client/server structure. The principles of gesture recognition in the system were studied and the relevant self-investigated algorithms—GestureMoRo, for the association between gestures and mobile robots were designed. Moreover, in order to avoid the unstably fluctuated movement of the mobile robot caused by palm shaking, the Gaussian filter algorithm was used to smooth and denoise the collected gesture data, which effectively improved the robustness and stability of the mobile robot’s locomotion. Finally, the teleoperation control strategy of the gesture to the WATER2 mobile robot was realized, and the effectiveness and practicability of the designed system were verified through multiple experiments. [ABSTRACT FROM AUTHOR]

Mobile manipulator robots that combine mobile platforms and robotic arms have been attracting considerable attention in recent years. In this paper, the motion control problem of the mobile manipulator system is considered with the following proposed control strategy. Firstly, a decoupled dynamic model is created to increase operation safety and to reduce complexity such that the independent controllers can be designed for the mobile system and the manipulator system. Subsequently, an effective reference trajectory generator is proposed to guide the mobile manipulator systems to the proper position for the manipulation being able to grip the target. Thereafter, the fuzzy controllers are designed for the mobile system to eliminate the tracking error and for the manipulator system to accomplish the gripping mission. The stability of the mobile manipulator control system can be guaranteed by the Lyapunov theory. Finally, the numerical simulations are given to demonstrate the effectiveness of the proposed approach. From the simulation results, it can be seen that this paper as well as other compared methods have good control response in case of small controller gain, in which the convergence time are about 4–5 s. However, by increasing the controller gain to improve the control response, the other methods will make the system unstable or the controller output will produce a large amount of chattering. The proposed controller in this paper can not only decrease the convergence time, from 5 to 3 s, but also provide a smooth control response. [ABSTRACT FROM AUTHOR]

Ground mobile robots can replace human beings to perform special tasks in threatened areas. Path planning technology provides mobile robots with the ability to reach the target position autonomously. When there are threats in the environment, the ground mobile robot needs to be able to reach the target position quickly and safely. Because threats are often difficult to calculate in such environments, and planned paths are difficult to use for path tracing. Therefore, path planning should comprehensively consider the distance, continuity and possible threats when moving. Aiming at the problem that the threat in the environment cannot be accurately calibrated usually, this paper proposes a method to mark the threat degree on the global elevation map by using the fuzzy logic system. In order to verify the feasibility of the algorithm, the improved algorithm with the classical algorithm in different environments and the current similar algorithm are compared with the current simulation experiment. The simulation results show that the algorithm has achieved good results, which proves the superiority of the algorithm. The path planning results of the algorithm in the threatened 3D environment not only have less threat, but also have better adaptability to the natural environment, and the planning path quality is better than that of the same type of algorithm. [ABSTRACT FROM AUTHOR]

This study explores the kinematic model of the popular RHex hexapod robots which have garnered considerable interest for their locomotion capabilities. We study the influence of tripod trajectory parameters on the RHex robot's movement, aiming to craft a precise kinematic model that enhances walking mechanisms. This model serves as a cornerstone for refining robot control strategies, enabling tailored performance enhancements or specific motion patterns. Validation conducted on a bespoke test bed confirms the model's efficacy in predicting spatial movements, albeit with minor deviations due to motor load variations and control system dynamics. In particular, the derived kinematic framework offers valuable insights for advancing control logic, particularly navigating in flat terrains, thereby broadening the RHex robot's application spectrum. [ABSTRACT FROM AUTHOR]

Path planning for mobile robots in complex circumstances is still a challenging issue. This work introduces an improved deep reinforcement learning strategy for robot navigation that combines dueling architecture, Prioritized Experience Replay, and shaped Rewards. In a grid world and two Gazebo simulation environments with static and dynamic obstacles, the Dueling Deep Q-Network with Modified Rewards and Prioritized Experience Replay (PMR-Dueling DQN) algorithm is compared against Q-learning, DQN, and DDQN in terms of path optimality, collision avoidance, and learning speed. To encourage the best routes, the shaped Reward function takes into account target direction, obstacle avoidance, and distance. Prioritized replay concentrates training on important events while a dueling architecture separates value and advantage learning. The results show that the PMR-Dueling DQN has greatly increased convergence speed, stability, and overall performance across conditions. In both grid world and Gazebo environments the PMR-Dueling DQN achieved higher cumulative rewards. The combination of deep reinforcement learning with reward design, network architecture, and experience replay enables the PMR-Dueling DQN to surpass traditional approaches for robot path planning in complex environments. [ABSTRACT FROM AUTHOR]

To solve the problem of optimal path planning for forest fire prevention mobile robots in forest terrain conditions, an improved ant colony algorithm based on extended neighborhood was proposed. Firstly, the plan of directional extended neighborhood was introduced, and the search neighborhood was extended from 8 to 10, which was in an attempt to expand search efficiency and scope. Then, considering multiple factors that affect the mobile robot, path length and energy consumption were used to improve heuristic functions. Next, the initial pheromone was improved by incorporating location information. Finally, the advantages of the Max-Min Ant System (MMAS) and the elite ant model were combined to improve the pheromone updating rule. Results indicated that the proposed improved algorithm compared with traditional ant colony algorithm and the ant colony algorithm based on multiple inspired factor, which can shorten the path length by 7. 66% and 6. 53%, reduce energy consumption by 62. 2% and 49. 3%, decrease the comprehensive index by 32. 6% and 23. 1%. The research demonstrated that the proposed improved ant colony algorithm had stronger global search capabilities and considerable practical value [ABSTRACT FROM AUTHOR]

In order to ensure safe and comfortable human–robot navigation in close proximity, it is imperative for robots to possess the capability to understand human behavioral intention. With this objective in mind, this paper introduces a Human-Aware Navigation (HAN) algorithm. The HAN system combines insights from studies on human detection, social behavioral model, and behavior prediction, all while incorporating social distance considerations. This information is integrated into a layer dedicated to human behavior intention cognition, achieved through the fusion of data from laser radar and Kinect sensors, employing Gaussian functions to account for individual private space and movement trend. To cater to the mapping requirements of the HAN system, we have reduced the computational complexity associated with traditional multilayer cost map by implementing a "first-come, first-served" expansion method. Subsequently, we have enhanced the trajectory optimization equation by incorporating an improved dynamic triangle window method that integrates human behavior intention cognition, leading to the determination of an appropriate trajectory for the robot. Finally, experimental evaluations have been conducted to assess and validate the efficacy of the human behavior intention cognition and the HAN system. The results clearly demonstrate that the HAN system outperforms the traditional Dynamic Window Approach algorithm in ensuring the safety and comfort of humans in human–robot coexistence environments. [ABSTRACT FROM AUTHOR]

The intricate water-land intermingled nature of wild environments necessitates robots to exhibit multimodal cross-domain mobility capabilities. This paper introduces a novel wheel-spoke-paddle hybrid amphibious robot (WSP-bot) that can operate on flat and rough terrains, water surfaces, and water-land transitional zones. The proposed robot relies on a propulsion mechanism called transformable wheel-spoke-paddle (WSP), which combines the stability of wheeled robots with the obstacle-climbing capability of legged robots, while also providing additional aquatic mobility. The utilization of a crank-slider-based transformation mechanism enables seamless switching between multiple motion modes. An analysis of mode transition and ground motion in spoke mode was conducted, along with an investigation of its obstacle-crossing capability. Simulations were performed for mode transition, ground locomotion, and obstacle-crossing, as well as propulsion of a single WSP module on water. Based on the above work, a prototype robot was manufactured. Prototype tests, including mode transition and mobility tests on land and water surfaces under multimodal states, confirmed the effectiveness of the proposed WSP-bot. [ABSTRACT FROM AUTHOR]

The main objective of this paper is to handle the trajectory tracking control for a class of three-wheel mobile robot by using a novel adaptive robust control. The mobile robot consists of nonholonomic constraints and uncertainties. The uncertainties include initial condition offset, mass, and moment of inertia of the system, which are time-varying and bounded (unknown). The control force in analytical form is obtained by UK theory, and the adaptive robust control is formulated to handle the uncertainties. For estimating the unknown bounds of uncertainties, the leakage-type adaptive law is designed, which can automatically adjust its own value according to the trajectory tracking errors. Then we verify the uniform boundedness and uniform ultimate boundedness of the proposed control by Lyapunov method. Finally, numerical simulations are conducted to show the trajectory tracking effectiveness of the designed control method. [ABSTRACT FROM AUTHOR]

The auditory system is considered one of the crucial pathways for robots to perceive environmental information. The perception and decision-making capabilities of mobile robots are greatly enhanced by accurate and effective sound source localization, making it highly significant for applications in hazardous environment rescue and inspection. With the widespread adoption of deep learning, the effectiveness of sound source localization has been notably improved through the introduction of convolutional neural networks (CNNs). Sound source localization for mobile robots was comprehensively compared and analyzed from four perspectives: network architecture and improvements, types of sound features, data simulation and augmentation, as well as the fusion of multimodal information. Reflections and prospects on the application of the technology are also presented. [ABSTRACT FROM AUTHOR]

The challenge of precise dynamic positioning for mobile robots is addressed through the development of a multi-inertial navigation system (M-INSs). The inherent cumulative sensor errors prevalent in traditional single inertial navigation systems (INSs) under dynamic conditions are mitigated by a novel algorithm, integrating multiple INS units in a predefined planar configuration, utilizing fixed distances between the units as invariant constraints. An extended Kalman filter (EKF) is employed to significantly enhance the positioning accuracy. Dynamic experimental validation of the proposed 3INS EKF algorithm reveals a marked improvement over individual INS units, with the positioning errors reduced and stability increased, resulting in an average accuracy enhancement rate exceeding 60%. This advancement is particularly critical for mobile robot applications that demand high precision, such as autonomous driving and disaster search and rescue. The findings from this study not only demonstrate the potential of M-INSs to improve dynamic positioning accuracy but also to provide a new research direction for future advancements in robotic navigation systems. [ABSTRACT FROM AUTHOR]

To solve the problem of cumulative errors when robots build maps in complex orchard environments due to their large scene size, similar features, and unstable motion, this study proposes a loopback registration algorithm based on the fusion of Faster Generalized Iterative Closest Point (Faster_GICP) and Normal Distributions Transform (NDT). First, the algorithm creates a K-Dimensional tree (KD-Tree) structure to eliminate the dynamic obstacle point clouds. Then, the method uses a two-step point filter to reduce the number of feature points of the current frame used for matching and the number of data used for optimization. It also calculates the matching degree of normal distribution probability by meshing the point cloud, and optimizes the precision registration using the Hessian matrix method. In the complex orchard environment with multiple loopback events, the root mean square error and standard deviation of the trajectory of the LeGO-LOAM-FN algorithm are 0.45 m and 0.26 m which are 67% and 73% higher than those of the loopback registration algorithm in the Lightweight and Ground-Optimized LiDAR Odometry and Mapping on Variable Terrain (LeGO-LOAM), respectively. The study proves that this method effectively reduces the influence of the cumulative error, and provides technical support for intelligent operation in the orchard environment. [ABSTRACT FROM AUTHOR]

In this paper, we propose a reinforcement learning-based end-to-end learning method for the autonomous driving of a mobile robot in a dynamic environment with obstacles. Applying two additional techniques for reinforcement learning simultaneously helps the mobile robot in finding an optimal policy to reach the destination without collisions. First, the multifunctional reward-shaping technique guides the agent toward the goal by utilizing information about the destination and obstacles. Next, employing the hindsight experience replay technique to address the experience imbalance caused by the sparse reward problem assists the agent in finding the optimal policy. We validated the proposed technique in both simulation and real-world environments. To assess the effectiveness of the proposed method, we compared experiments for five different cases. [ABSTRACT FROM AUTHOR]

With the superior development of technology, mobile robots have become an essential part of humans' daily life. Consequently, interacting and dealing with them pushes us to develop and propose different suitable Human-Robot Interaction (HRI) systems that can detect the interacted user's actions and achieve the desired output in real-time. In this paper, we propose a closed-loop smart mechanism for two main agents: the hand gloves' controller and the mobile robot. To be more specific, the developed model employs flex sensors to measure the curve of the finger. The sensor signals are then processed by aiding the Hedge Algebras Algorithm to control the movement direction and customize the speed of the mobile robot via wireless communication. Numerical simulation and experiments demonstrate that the mobile robot could operate reliably, respond rapidly to control signals, and vary its speed continually based on the different finger gestures. Besides, the control results are also compared with those obtained from the traditional fuzzy controller to prove the superiority and efficiency of the proposed method. [ABSTRACT FROM AUTHOR]

Initiative service is a key research direction for the new generation of service robots. It is important to automatically track humans for initiative service in human-robot interaction. To solve the problems of low precision and poor anti-interference capability of only using single-modal (audio or visual) information, a speaker positioning and tracking method based on an audio-visual bimodal combination is proposed. First, the azimuth of the speaker is obtained based on the time difference of arrival using a microphone array, and face detection based on AdaBoost is carried out using the camera. A distance and azimuth calculation model is established to obtain the position of the speaker. Second, a speaker positioning strategy based on an audio-visual bimodal combination is designed to handle different situations. Third, the path is planned by which the azimuth and distance between the robot and the speaker are maintained in a limited range. Different azimuths and distances for speaker tracking are set to perform various simulations. Finally, the mobile robot is driven to follow the path using the STM32 real-time control system. Information from the microphone array and the camera is collected and processed by Raspberry Pi. The tracking accuracy was tested under a single-face situation by setting 20 different target points, and 10 tests were carried out under each point. Under multi-face situations, the audio-visual bimodal information is combined to identify the speaker, and then the Kalman filter is used in face tracking. The experimental results demonstrate that the running trajectory of the mobile robot is close to the ideal trajectory, which ensures effective speaker tracking. [ABSTRACT FROM AUTHOR]

In modern applications, mobile robots are important dynamic systems. Motion planning is a crucial responsibility for these robots as it enables them to move from one place to another safely and efficiently. Extensive research has been conducted on motion planning for static scenarios. However, research is limited when the scenarios are dynamic because it is difficult and expensive to continuously replan the robot's moves to ensure its safety. This paper presents the circular arc path algorithm for sensorless differential drive mobile robot movement, given that the initial point and endpoint are defined. The differential drive mobile robot, which is adopted for various vehicles and robots, is very popular due to its simple operation and robustness. A mobile robot with two driving wheels that uses a differential drive splits its overall velocity between its left and right wheels. Accordingly, the radius of the circle that forms after movement between two points is the main factor that determines the left and right velocity of the robot's wheels. Through this algorithm, all points will be interpolated with G1 continuity, which only requires an initial direction to move. Robot simulation is performed using CoppeliaSim, and the algorithm is developed using Remote API functions in Matlab. [ABSTRACT FROM AUTHOR]

To solve the problem of optimal path planning for forest fire prevention mobile robots in forest terrain conditions, an improved ant colony algorithm based on extended neighborhood was proposed. Firstly, the plan of directional extended neighborhood was introduced, and the search neighborhood was extended from 8 to 10, which was in an attempt to expand search efficiency and scope. Then, considering multiple factors that affect the mobile robot, path length and energy consumption were used to improve heuristic functions. Next, the initial pheromone was improved by incorporating location information. Finally, the advantages of the Max-Min Ant System (MMAS) and the elite ant model were combined to improve the pheromone updating rule. Results indicated that the proposed improved algorithm compared with traditional ant colony algorithm and the ant colony algorithm based on multiple inspired factor, which can shorten the path length by 7. 66% and 6. 53%, reduce energy consumption by 62. 2% and 49. 3%, decrease the comprehensive index by 32. 6% and 23. 1%. The research demonstrated that the proposed improved ant colony algorithm had stronger global search capabilities and considerable practical value. [ABSTRACT FROM AUTHOR]

When two-dimensional Laser Radar (LiDAR) is used for obstacle detection, the position change of mobile robot's own attitude usually leads to the change of LiDAR reference position, which will produce a large error in the positioning calculation of obstacle. In this study, an obstacle target location method based on LiDAR scanning angle correction is proposed. The K-means clustering algorithm is used to divide the LiDAR point cloud data, and then the angle correction processing is performed on the clustered data, so that the processed data information is more consistent with the real value. Finally, each cluster data is enveloped to improve the accuracy of LiDAR scanning data. The test results show that the the proposed method can improve the positioning accuracy and meet the requirement of accurate obstacle positioning. [ABSTRACT FROM AUTHOR]

The navigation of mobile robots is a key element of autonomous systems, which allows robots to move effectively and securely in changing environments with greater autonomy and precision. This study aims to provide researchers with a comprehensive guide for selecting the best path-planning methods for their particular projects. We evaluate some popular algorithms that are regularly used in mobile robot navigation, in order to demonstrate their specifications and determine where they are most effective. For example, one algorithm is used to model the problem as a standard graph, and another algorithm is found to be the most suitable for highly dynamic and highly dimensional environments, due to its robust path-planning capabilities and efficient route construction. We also filter high-performance algorithms in terms of computational complexity, accuracy, and robustness. In conclusion, this study provides valuable information on its individual strengths and weaknesses, helping robotics and engineers make informed decisions when selecting the most appropriate algorithm for their specific applications. [ABSTRACT FROM AUTHOR]

This paper uses a cascade-connected fuzzy-PI controller to control the position and speed of a differential drive and four-wheel drive of an autonomous mobile robot for optimal path planning. The angular speed information obtained from the encoder of each motor and the instantaneous position and angle information of the robot were calculated. The angle and position error between the reference points and these values is applied to the fuzzy logic controller as an input signal. The robot angular and linear speed data obtained from the fuzzy logic output were converted into reference speed values with kinematic equations to be applied to the motors. The speed controls of the motors were carried out with a PI controller based on these reference values. The study was performed both as a simulation in the MATLAB program and experimentally in the laboratory environment for one and more reference coordinates. In the experimental study, reference values were sent to the robot via Bluetooth with the Android application designed. At the same time, the instant data of the robot was also collected on the Android device through the same application. These data collected in Excel format were transferred to the computer via e-mail and the graphics were drawn in the MATLAB program. When the results were examined, it was seen that both speed and position control were successfully implemented with the fuzzy-PI controller for optimum path planning of the robot. [ABSTRACT FROM AUTHOR]

This paper proposes an improved Soft Actor–Critic Long Short-Term Memory (SAC-LSTM) algorithm for fast path planning of mobile robots in dynamic environments. To achieve continuous motion and better decision making by incorporating historical and current states, a long short-term memory network (LSTM) with memory was integrated into the SAC algorithm. To mitigate the memory depreciation issue caused by resetting the LSTM's hidden states to zero during training, a burn-in training method was adopted to boost the performance. Moreover, a prioritized experience replay mechanism was implemented to enhance sampling efficiency and speed up convergence. Based on the SAC-LSTM framework, a motion model for the Turtlebot3 mobile robot was established by designing the state space, action space, reward function, and overall planning process. Three simulation experiments were conducted in obstacle-free, static obstacle, and dynamic obstacle environments using the ROS platform and Gazebo9 software. The results were compared with the SAC algorithm. In all scenarios, the SAC-LSTM algorithm demonstrated a faster convergence rate and a higher path planning success rate, registering a significant 10.5 percentage point improvement in the success rate of reaching the target point in the dynamic obstacle environment. Additionally, the time taken for path planning was shorter, and the planned paths were more concise. [ABSTRACT FROM AUTHOR]

In fruit cultivation, viticulture requires the longest working hours in extended arm postures, much of which is carried out in standing postures to accumulate fatigue on arms, shoulders, and legs: a tough working environment. In this study, we propose a power assist system to assist its users in their extended arm work while they move in vineyards. The proposed system largely consists of a mobile robot, a power assist device for work, and a control system. The mobile robot is structured with a tracked vehicle for rough terrain arranged on its left and right sides so that the users can sit between the two vehicles and be assisted by the power assist device for work installed on it. The power assist device for work with a single linear actuator utilizing a linkage mechanism has the function to retain users' hand attitude angles while assisting the flexion and extension movements of their shoulder, elbow, and carpometacarpal joints. Then, we verify by simulations the effects that the arrangement and lengths of links will have on the carpometacarpal joints' trajectories as well as on the hand attitude angles. Finally, in order to check the effectiveness of the proposed power assist device for work, we conducted the evaluation experiments for assumed grape-harvesting work and gibberellin treatments. As a result, we proved its work assisting effects from the muscle activity states as well as its applicability to other kinds of work by altering its linkage structure and hand support part. [ABSTRACT FROM AUTHOR]

This paper proposes a robust posegraph optimization (PGO) method for posegraphs with keypoints. In the conventional PGO formulation, a loop constraint is defined between a pair of nodes, whereas in the proposed method, it is defined between a pair of keypoints. In this manner, robust PGO based on switch variables can be realized in a more fine-grained manner. Loop constraint is defined based on the unique geometric property of proximity point, and implemented as a new edge type of the g2o solver. The proposed method is compared with other robust PGO methods using real world data recorded in Nakanoshima Robot Challenge 2021. [ABSTRACT FROM AUTHOR]

This paper describes a study of the simple system integration of a mobile robot in the Nakanoshima Robot Challenge 2022. To improve the operability of the robot at the start of its journey, we studied the solution to the problem of initial localization during the experimental run and the setting of virtual obstacles on the map to be used by the mobile robot. This method reduces the amount of time and manual operation required to estimate the initial position and orientation of a mobile robot in mobile robot experiments. In this study, a mobile robot is implemented using open-source products such as robot operating system (ROS) and i-Cart mini. Experimental runs in the Extra Challenge of the Nakanoshima Robot Challenge 2022 demonstrate the feasibility of the method. [ABSTRACT FROM AUTHOR]

The stability of the Tracking Wheel Mobile Robot with Teleoperation System and Path Following Method is discussed in this study. The path is to be tracked by the host computer which is the master robot. The response from the robot is captured on camera. As the slave robot approaches the target position, the camera captures the response robot’s position and as well as moving trajectory. The host computer receives all of the images, enabling mobile robot deviation recoveries. The slave robot can use teleoperation to follow the sensor based on the decisions made by the master robot. The Lyapunov function in the Fuzzy Neural Network (FNN) control structure assures the system’s stability and satisfactory performance. It supports a mobile robot’s ability to adhere to a reference trajectory without deviating from it. Finally, the outcome of the simulation demonstrates that our controller is capable of tracking different environmental conditions and maintaining stability. [ABSTRACT FROM AUTHOR]

This paper proposed a skill transmission technique for the mobile robot via learning by demonstration. When the material is transported to the designated location, the robot can show the human-like capabilities: autonomous tracking target. In this case, a skill transmission framework is designed, which the Kinect sensor is utilized to distinguish human activity recognition to create a planned path. Moreover, the dynamic movement primitive method is implemented to represent the teaching data, and the Gaussian mixture regression is utilized to encode the learning trajectory. Furthermore, in order to realize the accurate position control of trajectory tracking, a model predictive tracking control is investigated, where the recurrent neural network is used to eliminate the uncertain interaction. Finally, some experimental tasks using the mobile robot (BIT-6NAZA) are carried out to demonstrate the effectiveness of the developed techniques in real-world scenarios. [ABSTRACT FROM AUTHOR]

Purpose: Driven by sustainable production, mobile robots are introduced as a new clean-energy material handling tool for mixed-model assembly lines (MMALs), which reduces energy consumption and lineside inventory of workstations (LSI). Nevertheless, the previous part feeding scheduling method was designed for conventional material handling tools without considering the flexible spatial layout of the robotic mobile fulfillment system (RMFS). To fill this gap, this paper focuses on a greening mobile robot part feeding scheduling problem with Just-In-Time (JIT) considerations, where the layout and number of pods can be adjusted. Design/methodology/approach: A novel hybrid-load pod (HL-pod) and mobile robot are proposed to carry out part feeding tasks between material supermarkets and assembly lines. A bi-objective mixed-integer programming model is formulated to minimize both total energy consumption and LSI, aligning with environmental and sustainable JIT goals. Due to the NP-hard nature of the proposed problem, a chaotic differential evolution algorithm for multi-objective optimization based on iterated local search (CDEMIL) algorithm is presented. The effectiveness of the proposed algorithm is verified by dealing with the HL-pod-based greening part feeding scheduling problem in different problem scales and compared to two benchmark algorithms. Managerial insights analyses are conducted to implement the HL-pod strategy. Findings: The CDEMIL algorithm's ability to produce Pareto fronts for different problem scales confirms its effectiveness and feasibility. Computational results show that the proposed algorithm outperforms the other two compared algorithms regarding solution quality and convergence speed. Additionally, the results indicate that the HL-pod performs better than adopting a single type of pod. Originality/value: This study proposes an innovative solution to the scheduling problem for efficient JIT part feeding using RMFS and HL-pods in automobile MMALs. It considers both the layout and number of pods, ensuring a sustainable and environmental-friendly approach to production. [ABSTRACT FROM AUTHOR]

In this paper, the rectilinear motion of a body controlled by moving internal mass in a medium with quadratic resistance is considered. The conditions under which the translational motion of a body occurs with periodically varying velocity are considered. The average velocity of this movement is determined. Conditions that provide the highest average velocity were established. [ABSTRACT FROM AUTHOR]

Path-planning research has been the key to mobile-robot-navigation technology. However, traditional path-planning algorithms have some shortcomings. To solve these problems, this paper proposes a fusion algorithm that combines the kinematical constrained A* algorithm with the Dynamic Window Approach (DWA) algorithm. The kinematical constrained A* algorithm can plan the global path, and then the DWA algorithm can plan the local path under the global path's guidance. Firstly, combined with robot kinematics, we improve the node-expansion method and heuristic-function model of the A* algorithm, which improves the search efficiency, reduces the number of path bends and lowers the computational cost so that the path generated by the A* algorithm better meets the needs of robot motion. Secondly, we optimize the trajectory-evaluation function of the DWA algorithm so that the local paths planned by the DWA algorithm are smoother and more coherent, which is easier for robot-motion execution. Finally, we extract the key nodes from the global path planned by the A* algorithm to guide the DWA algorithm for local path planning and dynamic-obstacle avoidance and to make the local path closer to the global path. Through simulation and practical experiments, the effectiveness of the fusion algorithm proposed in this paper is verified. [ABSTRACT FROM AUTHOR]

It is important for older and disabled people who live alone to be able to cope with the daily challenges of living at home. In order to support independent living, the Smart Home Care (SHC) concept offers the possibility of providing comfortable control of operational and technical functions using a mobile robot for operating and assisting activities to support independent living for elderly and disabled people. This article presents a unique proposal for the implementation of interoperability between a mobile robot and KNX technology in a home environment within SHC automation to determine the presence of people and occupancy of occupied spaces in SHC using measured operational and technical variables (to determine the quality of the indoor environment), such as temperature, relative humidity, light intensity, and CO2 concentration, and to locate occupancy in SHC spaces using magnetic contacts monitoring the opening/closing of windows and doors by indirectly monitoring occupancy without the use of cameras. In this article, a novel method using nonlinear autoregressive Neural Networks (NN) with exogenous inputs and nonlinear autoregressive is used to predict the CO2 concentration waveform to transmit the information from KNX technology to mobile robots for monitoring and determining the occupancy of people in SHC with better than 98% accuracy. [ABSTRACT FROM AUTHOR]

Planning the path of a mobile robot that must transport and deliver small packages inside a multi-story building is a problem that requires a combination of spatial and operational information, such as the location of origin and destination points and how to interact with elevators. This paper presents a solution to this problem, which has been formulated under the following assumptions: (1) the map of the building's floors is available; (2) the position of all origin and destination points is known; (3) the mobile robot has sensors to self-localize on the floors; (4) the building is equipped with remotely controlled elevators; and (5) all doors expected in a delivery route will be open. We start by defining a static navigation tree describing the weighted paths in a multi-story building. We then proceed to describe how this navigation tree can be used to plan the route of a mobile robot and estimate the total length of any delivery route using Dijkstra's algorithm. Finally, we show simulated routing results that demonstrate the effectiveness of this proposal when applied to an autonomous delivery robot operating in a multi-story building. [ABSTRACT FROM AUTHOR]

The path planning problem of nuclear environment robots refers to finding a collision-free path under the constraints of path length and an accumulated radiation dose. To solve this problem, the Improved Dueling Deep Double Q Network algorithm (ID3QN) based on asymmetric neural network structure was proposed. To address the issues of overestimation and low sample utilization in the traditional Deep Q Network (DQN) algorithm, we optimized the neural network structure and used the double network to estimate action values. We also improved the action selection mechanism, adopted a priority experience replay mechanism, and redesigned the reward function. To evaluate the efficiency of the proposed algorithm, we designed simple and complex radioactive grid environments for comparison. We compared the ID3QN algorithm with traditional algorithms and some deep reinforcement learning algorithms. The simulation results indicate that in the simple radioactive grid environment, the ID3QN algorithm outperforms traditional algorithms such as A*, GA, and ACO in terms of path length and accumulated radiation dosage. Compared to other deep reinforcement learning algorithms, including DQN and some improved DQN algorithms, the ID3QN algorithm reduced the path length by 15.6%, decreased the accumulated radiation dose by 23.5%, and converged approximately 2300 episodes faster. In the complex radioactive grid environment, the ID3QN algorithm also outperformed the A*, GA, ACO, and other deep reinforcement learning algorithms in terms of path length and an accumulated radiation dose. Furthermore, the ID3QN algorithm can plan an obstacle-free optimal path with a low radiation dose even in complex environments. These results demonstrate that the ID3QN algorithm is an effective approach for solving robot path planning problems in nuclear environments, thereby enhancing the safety and reliability of robots in such environments. [ABSTRACT FROM AUTHOR]

For several decades, cooperative control of autonomous mobile robots has been an active research area with potential applications in dynamic areas such as agriculture, industrial plants, and the military. Although numerous research studies on pattern formation using multiple mobile agents have been conducted, no algorithm for symmetric pattern formation that is independent of the number of agents and prioritizes user control has been introduced. This article demonstrates an attempt to manifest an end-to-end symmetrical pattern in a swarm of mobile robots. Here, a two-staged paradigm named SymSwarm is developed, which incorporates the novelty of forming symmetrical formations around a point within a coveted area via decentralized communication among the swarm. SymSwarm is built on a novel concept of a bio-inspired state model called compete and point state model (CPSM), which enables smarter and more robust computation and self-regulation. While most bio-inspired models focus on the characteristics of bees, ants, cats, chimpanzees, and a variety of other animals, this paradigm focuses on the competitive sentiment and leadership ability of human beings to shape the CPSM mentioned above. The algorithm for acquiring symmetrical points is developed using the circle polygon analogy in Cartesian geometry. The following sections propose a decentralized communication mechanism for adjacent consecutive robots as well as a heuristic nearest point finder algorithm. The proposed paradigm is evaluated using robotics simulator (V-REP), and the results indicate that it is consistently successful and has a low rate of pattern formation error in the presence or absence of obstacles in the environment. [ABSTRACT FROM AUTHOR]

Path planning is one of the fundamental issues in the research field of robot. In the last decades, Rapidly‐exploring Random Tree star (RRT*) was one of the chosen path planner for robot with its probabilistically complete. In this paper, an improved bidirectional RRT* path planner for robot is proposed (Bi‐RRT*), which has planned path with safety and smoothly guarantee. The main contributions of this paper include, firstly, combining the backtracking idea of the Quick‐RRT algorithm with the greedy search strategy of the RRT‐Connect algorithm, which can reduce the path cost and improve the operation efficiency of the algorithm. Second, in order to further reduce the running time of the algorithm, a new collision detection algorithm based on cross‐product is proposed to replace the conventional collision detection strategy. To ensure the quality of the path, two post‐processing strategies, path optimization strategy and path smoothing strategy, are proposed. Path optimization strategy is based on triangle inequality, and two optimization methods are proposed to effectively reduce the path cost. The path smoothing strategy based on the Bezier curve improves the continuity of a smooth arc and is better applied to path smoothing. Simulation results for both virtual and real environments show the advantages of combining Quick‐RRT and RRT‐Connect and verify the effectiveness of the proposed collision detection algorithm. Compared with similar algorithms, the proposed Bi‐RRT algorithm has higher operational efficiency, smaller path cost, smoother planned paths, and improved path quality. © 2023 Institute of Electrical Engineer of Japan and Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]