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To solve the problem of mobile robots needing to adjust their pose for accurate operation after reaching the target point in the indoor environment, a localization method based on scene modeling and recognition has been designed. Firstly, the offline scene model is created by both handcrafted feature and semantic feature. Then, the scene recognition and location calculation are performed online based on the offline scene model. To improve the accuracy of recognition and location calculation, this paper proposes a method that integrates both semantic features matching and handcrafted features matching. Based on the results of scene recognition, the accurate location is obtained through metric calculation with 3D information. The experimental results show that the accuracy of scene recognition is over 90%, and the average localization error is less than 1 meter. Experimental results demonstrate that the localization has a better performance after using the proposed improved method. [ABSTRACT FROM AUTHOR]

Fruit industry is one of the important economic pillars of China’s agriculture and rural areas. The current orchard production management level,especially the mechanization and information level,is relatively backward. Orchard mobile robot based on multi-source information fusion can realize stable and high-precision autonomous navigation in complex environment,provide intelligent and efficient autonomous navigation means for orchard mobile platform,and strongly support the construction of smart orchard. By analyzing the research progress of orchard mobile robot autonomous navigation system based on multi-source information fusion,this paper proposes to combine the actual complex and diverse working conditions of orchard,focus on key technologies such as positioning and mapping,path planning and decision control strategy,and based on the existing mobile platform,study the multi-source sensor information fusion strategy to achieve autonomous navigation in complex environment. The performance of the autonomous navigation system is verified by field tests. [ABSTRACT FROM AUTHOR]

As the complexity of modern library management tasks increases, it is difficult for traditional mobile robots to meet the task of moving and classifying books. In order to design a mobile robot that can autonomously classify and transport books, the study realizes the tasks of book classification and transportation in libraries by fusing the differential speed algorithm and the robot motion model algorithm. First, the robot operating system is utilized to scan the books, classify the books, and obtain the category information of the books. Then, the differential speed algorithm is used to control the motion of the robot to ensure that the robot can accurately transport the books to the designated location. At the same time, combined with the robot motion model algorithm, the motion trajectory of the robot is planned to ensure that the robot can avoid obstacles and stably complete the book transportation task. Finally, the deep reinforcement learning algorithm is used to train the decision-making model of the robot to improve the intelligence level of the robot. The results of simulation experiments show that the research method has the highest accuracy, with an average accuracy of 99.98%, and the robot is able to accurately categorize the books and quickly avoid obstacles with strong stability. The results of the application experiments show that the research method has the shortest moving distance, with an average moving distance of 132 m and an average completion time of 34 seconds, which are lower than the remaining three types of robots. The research robot showed high accuracy in the task of returning books in four time periods within 10 days in the library, with an average accuracy of 99.58%. The experimental results validate the superiority of the research methodology and show that the robots are capable of accurately recognizing and classifying books and can autonomously perform transportation tasks in libraries. The research results help to improve the automation level and management efficiency of libraries and have important application value. [ABSTRACT FROM AUTHOR]

In the traditional Deep Deterministic Policy Gradient (DDPG) algorithm, path planning for mobile robots in mapless environments still encounters challenges regarding learning efficiency and navigation performance, particularly adaptability and robustness to static and dynamic obstacles. To address these issues, in this study, an improved algorithm frame was proposed that designs the state and action spaces, and introduces a multi-step update strategy and a dual-noise mechanism to improve the reward function. These improvements significantly enhance the algorithm's learning efficiency and navigation performance, rendering it more adaptable and robust in complex mapless environments. Compared to the traditional DDPG algorithm, the improved algorithm shows a 20% increase in the stability of the navigation success rate with static obstacles along with a 25% reduction in pathfinding steps for smoother paths. In environments with dynamic obstacles, there is a remarkable 45% improvement in success rate. Real-world mobile robot tests further validated the feasibility and effectiveness of the algorithm in true mapless environments. [ABSTRACT FROM AUTHOR]

This paper proposes a general kinematic-based design method for optimizing the side-mounted leg mechanism of BJTUBOT, a novel multi-mission quadrupedal Earth rover. The focus issue lies in designing structural improvements that not only enhance its kinematic performance but also prevent singularity, all while meeting the demands for miniaturization and lightweight without deviating from the original leg design concept. To solve this issue, a novel 3-UPRU&PPRR mechanism is envisaged based on the original configuration. Around the unique structural features of this mechanism, its inverse kinematic solution and Jacobian matrix are calculated, and a coupled motion relation between a key limb and its moving platform (MP) is presented. In order to achieve singularity avoidance, some typical singularity configurations based on line geometry analysis are given. In accordance with this result, an initial configuration for multi-objective dimensional optimization is presented. To further enhance its kinematic performance, we introduce the use of the GCI (global conditional index) performance at extreme positions as one of the optimization criteria based on the NSGA-II (Non-dominated Sorting Genetic Algorithm) algorithm, and directly measuring the crowding distance using the position vector of the U (universal) joints on the moving platform. This optimized mechanism prototype is demonstrated in a single-leg Adams simulation, which exhibits good velocity mapping effects and displacement accuracy. Finally, a new BJTUBOT prototype was constructed based on the optimized leg, and its flexibility was tested with various classical forms of motions. The workflow in this paper significantly improves the leg performance under the current design needs. [ABSTRACT FROM AUTHOR]

Parcel transportation is a task that is expected to be highly automated with the development of application-specific mobile robots. This paper presents the design and implementation of a parcel transportation and delivery mechanism aimed at converting a general-purpose indoor omnidirectional robot into an indoor delivery robot. The design of this new mechanism has considered the best placement in the robot and the limitation of not exceeding the original robot diameter. The mechanism consists of a basket with a lid that allows for the manual loading and automatic unloading of parcels. Despite the space limitations imposed by the general-purpose robot design, the designed mechanism can transport up to 90% of the packages received in an educational building. The mechanism was empirically validated by conducting 125 static manual loading experiments, 150 static unloading experiments, and 50 complete parcel delivery experiments. Results show that the delivery robot can efficiently deliver 78% of the total packages received in the building: envelopes, very small parcels, and small parcels. In the case of medium parcels, the delivery was unsuccessful in 30% of cases, in which the parcel did not properly slide out of the basket. [ABSTRACT FROM AUTHOR]

Individuals with reduced mobility, including the growing elderly demographic and those with spinal cord injuries, often face significant challenges in daily activities, leading to a dependence on assistance. To enhance their independence, we propose a robotic system that facilitates greater autonomy. Our approach involves a functional assistive robotic implementation for picking, placing, and delivering containers using the TIAGo mobile manipulator robot. We developed software and routines for detecting containers marked with an ArUco code and manipulating them using the MoveIt library. Subsequently, the robot navigates to specific points of interest within a room to deliver the container to the user or another designated location. This assistance task is commanded through a user interface based on a web application that can be accessed from the personal phones of patients. The functionality of the system was validated through testing. Additionally, a series of user trials were conducted, yielding positive feedback on the performance and the demonstration. Insights gained from user feedback will be incorporated into future improvements to the system. [ABSTRACT FROM AUTHOR]

The purpose of autonomous mobile robot navigation is to construct the optimal defended path. In order to ameliorate the accuracy of real time cleaning activity of the mobile robot path planning a rapidly exploring random tree (RRT) algorithm was widely used in larger space environment. This research present the real time cleaning obstacle avoidance in the movement of path using expert system based decision model on machine learning algorithm. Movable robots require a data source, a way to analyze that data, and a way to behave in response to an environment that is changing. The ability to detect and adjust to an unknown situation requires a robust cognitive system. A mobile robot is designed and analysed, which will be autonomously navigated using the RRT navigation algorithm and this will be virtually simulated in a virtual robot experimentation platform. The mobile robot that is designed is tested for its stability. The fuzzy logic analysis is used to predict the mobile robot acceleration and which terrain is most suitable for the robot. Finally using the K-nearest neighbour technique with the labelled accelerometer mobile robot data for velocity prediction. Simulation results decorate the performance of the proposed RRT control system. The duration of travel required for the robot to achieve its objective is calculated, and the findings indicate that operating the robot at 60% of its maximum velocity results is the ideal balance between cleaning effectiveness and time taken. [ABSTRACT FROM AUTHOR]

Copyright of Przegląd Elektrotechniczny is the property of Przeglad Elektrotechniczny and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Mobile robot navigation has been a very popular topic of practice among researchers since a while. With the goal of enhancing the autonomy in mobile robot navigation, numerous algorithms (traditional AI-based, swarm intelligence-based, self-learning-based) have been built and implemented independently, and also in blended manners. Nevertheless, the problem of efficient autonomous robot navigation persists in multiple degrees due to the limitation of these algorithms. The lack of knowledge on the implemented techniques and their shortcomings act as a hindrance to further development on this topic. This is why an extensive study on the previously implemented algorithms, their applicability, their weaknesses as well as their potential needs to be conducted in order to assess how to improve mobile robot navigation performance. In this review paper, a comprehensive review of mobile robot navigation algorithms has been conducted. The findings suggest that, even though the self-learning algorithms require huge amounts of training data and have the possibility of learning erroneous behavior, they possess huge potential to overcome challenges rarely addressed by the other traditional algorithms. The findings also insinuate that in the domain of machine learning-based algorithms, integration of knowledge representation with a neuro-symbolic approach has the capacity to improve the accuracy and performance of self-robot navigation training by a significant margin. [ABSTRACT FROM AUTHOR]

Robotics is intertwined with metrology, including aircraft component inspection, automotive processes, and part geometry optimization. Optimized trajectory planning is essential for reliable robotic arm operation and maintaining quality in inspections and geometric enhancements, as well as autonomous mobile robot navigation. Technically, a path planning is associated as an optimization problem that relies on various parameters such as length minimization problem, smooth trajectory planning, low time/space complexity, and computational load. While considering all these stated parameters, choosing an optimal path to reach the destination is the primary function of path planning techniques. This research paper is focused on the implementation of adaptive bidirectional A* (ABA*) algorithm along with new strategy of flexible controlling points technique (FCP) to reduce the trajectory error by generating smoother trajectory. With the increased number of sharp turns, the wheel skidding error is generated that reduce the reliability of the path planning techniques by increasing the pose estimation error. By conducting multiple trials, the proposed technique has been implemented, resulting in a 100% reduction in the number of collisions. Furthermore, the application of the new FCP technique eliminates all sharp turns, leading to a 38% decrease in time lag uncertainty compared to conventional approaches. The proposed technique improves autonomous navigation by selecting smoother trajectories. [ABSTRACT FROM AUTHOR]

Augmented reality (AR) technology is revolutionizing traditional assembly processes, offering intuitive and interactive guidance that significantly enhances operational efficiency and accuracy. This study investigates the impact of AR on the assembly of Turtlebots, a complex task representative of industrial applications. Through a comparative analysis involving traditional paper manuals, modified paper manuals, and AR-based manuals, the benefits of AR integration are quantitatively assessed. Participants utilizing AR-based manuals completed the Turtlebot assembly 21.72% faster than those using traditional paper manuals, with a notable reduction in assembly time from an average of 03:00:40 to 02:21:26. Furthermore, the incidence of assembly errors significantly decreased, with AR manual users making an average of 2.25 errors compared to 5 by paper manual users. These findings underscore the potential of AR to expedite complex assembly tasks and enhance the accuracy of these processes. The study highlights the novel application of AR in improving both the speed and quality of assembly in an industrial context, demonstrating AR’s role as a pivotal technology for the future of manufacturing. [ABSTRACT FROM AUTHOR]

Few mobile robot developers already test their software on simulated robots in virtual environments or sceneries. However, the majority still shy away from simulation-based test campaigns because it remains challenging to specify and execute suitable testing scenarios, that is, models of the environment and the robots' tasks. Through developer interviews, we identified that managing the enormous variability of testing scenarios is a major barrier to the application of simulation-based testing in robotics. Furthermore, traditional CAD or 3Dmodelling tools such as SolidWorks, 3ds Max, or Blender are not suitable for specifying sceneries that vary significantly and serve different testing objectives. For some testing campaigns, it is required that the scenery replicates the dynamic (e.g., opening doors) and static features of real-world environments, whereas for others, simplified scenery is sufficient. Similarly, the task and mission specifications used for simulation-based testing range from simple point-to-point navigation tasks to more elaborate tasks that require advanced deliberation and decision-making. We propose the concept of composable and executable scenarios and associated tooling to support developers in specifying, reusing, and executing scenarios for the simulation-based testing of robotic systems. Our approach differs from traditional approaches in that it offers a means of creating scenarios that allow the addition of new semantics (e.g., dynamic elements such as doors or varying task specifications) to existing models without altering them. Thus, we can systematically construct richer scenarios that remain manageable. We evaluated our approach in a small simulation-based testing campaign, with scenarios defined around the navigation stack of a mobile robot. The scenarios gradually increased in complexity, composing new features into the scenery of previous scenarios. Our evaluation demonstrated how our approach can facilitate the reuse of models and revealed the presence of errors in the configuration of the publicly available navigation stack of our SUT, which had gone unnoticed despite its frequent use. [ABSTRACT FROM AUTHOR]

SLAM (simultaneous localization and mapping) is essential for accurate positioning and reasonable path planning in outdoor mobile robots. LiDAR SLAM is currently the dominant method for creating outdoor environment maps. However, the mainstream LiDAR SLAM algorithms have a single point cloud feature extraction process at the front end, and most of the loop closure detection at the back end is based on RNN (radius nearest neighbor). This results in low mapping accuracy and poor real-time performance. To solve this problem, we integrated the functions of point cloud segmentation and Scan Context loop closure detection based on the advanced LiDAR-inertial SLAM algorithm (LIO-SAM). First, we employed range images to extract ground points from raw LiDAR data, followed by the BFS (breadth-first search) algorithm to cluster non-ground points and downsample outliers. Then, we calculated the curvature to extract planar points from ground points and corner points from clustered segmented non-ground points. Finally, we used the Scan Context method for loop closure detection to improve back-end mapping speed and reduce odometry drift. Experimental validation with the KITTI dataset verified the advantages of the proposed method, and combined with Walking, Park, and other datasets comprehensively verified that the proposed method had good accuracy and real-time performance. [ABSTRACT FROM AUTHOR]

In this paper, a novel intelligent controller for the trajectory tracking control of a nonholonomic mobile robot with time-varying parameter uncertainty and external disturbances in the case of tire hysteresis loss is proposed. Based on tire dynamics principles, a dynamic and kinematic model of a nonholonomic mobile robot is established, and the neural network approximation model of the system's nonlinear term caused by many coupling factors when the robot enters a roll is given. Then, in order to adaptively estimate the unknown upper bounds on the uncertainties and perturbations for each subsystem in real time, a novel adaptive law employed online as a gain parameter is designed to solve the problem of inter-system coupling and reduce the transient response time of the system with lower uncertainties. Additionally, based on improved gray wolf optimizer and fuzzy system techniques, an adaptive algorithm using the gray wolf optimizer study space as the output variable of the fuzzy system to expand the search area of the gray wolves is developed to optimize the controller parameters online. Finally, the efficacy of the proposed intelligent control scheme and the feasibility of the proposed algorithm are verified by the 2023a version of MATLAB/Simulink platform. [ABSTRACT FROM AUTHOR]

In the realm of autonomous robotics, navigating differential drive mobile robots through unknown environments poses significant challenges due to their complex nonholonomic constraints. This issue is particularly acute in applications requiring precise trajectory tracking and effective obstacle avoidance without prior knowledge of the surroundings. Traditional navigation systems often struggle with these demands, leading to inefficiencies and potential safety risks. To address this problem, our studies propose an algorithm that integrates machine learning and control concepts, especially through the synergistic software of a Q-learning set of rules and a (PID) controller. This technique leverages the adaptability of Q-learning pathfinding and the precision of PID control for actual-time trajectory adjustment, aiming to beautify the robotics' navigation skills. Our comprehensive technique includes growing a country-area version that integrates Q-values with the dynamics of differential power robots, employing Bellman's equation for iterative coverage refinement. This version enables the robotics' capacity to dynamically adapt its navigation techniques in reaction to instant environmental feedback, thereby optimizing efficiency and protection in actual time. The effects of our full-size simulations exhibit a marked improvement in trajectory-tracking accuracy and impediment-avoidance competencies. These findings underscore the capability of combining machine learning algorithms with traditional methods to increase autonomous navigation technology in robotic systems. Our effects, derived from full-size simulations, suggest that the integration of Q-learning with PID controller markedly improves trajectory tracking accuracy, reduces tour times to targets, and complements the robotics' ability to navigate round barriers. This incorporated method demonstrates a tremendous advantage over conventional navigation systems, providing a sturdy way to the challenges of autonomous robot navigation in unpredictable environments. [ABSTRACT FROM AUTHOR]

The problem of trajectory for mobile robots included motion mobile robot from beginning point to end-point without collision with the obstacles. This article proposed two optimization methods, represented by Pelican Optimization Algorithm (POA) and Particle Optimization Algorithm (PSO) to have optimal trajectory for the mobile wheeled robot without collision in presence of obstacles. A minimization of the Mean Square Error (MSE) of positions (x,y) and orientation (θ) as fitness function for the proposed techniques. The mathematical representation of a mobile wheeled robot has been developed and experimental results have been conducted to verify the numerical results, which have been simulated using MATLAB software environment. The results showed that both POA and PSO could successfully avoid static obstacles. In addition, the results showed that less trajectory error can be obtained with POA as compared to PSO. The experimental validation is done using a BOE-Bot wheeled mobile robot. This robot attached with WiFi camera used to detect the obstacle coordinates using suggested POA algorithm. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Chongqing University of Technology (Natural Science) is the property of Chongqing University of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Home Security Systems (HSSs) have received much attention and have been widely adopted for practical deployment. However, detection and warning accuracy still need to be improved, along with the range of the realm, making it challenging to satisfy the user demands. This study proposes a security monitoring system based on mobile robots and the Internet of Things (IoT), allowing users to monitor and control devices remotely. The mobile robot integrated sensor systems and surveillance cameras are utilized for unauthorized early intrusion detection and to give users instant warnings. The data collected by the robot were stored on the Firebase Cloud server, and a mobile application and a Telegram interface were integrated to manage and control the system. In addition, adaptive motion control was adopted to correct errors in the robot’s trajectory. The implementation results proved that this system operated effectively with a minimal response delay of 0.87–1.67 s and a high detection accuracy (96.25%) in two experimental cases, which makes it suitable for real-time applications. [ABSTRACT FROM AUTHOR]

Autonomous navigation in dynamic environments is a significant challenge in robotics. The primary goals are to ensure smooth and safe movement. This study introduces a control strategy based on an Adaptive Neuro-Fuzzy Inference System (ANFIS). It enhances autonomous robot navigation in dynamic environments with a focus on collision-free path planning. The strategy uses a path-planning technique to develop a trajectory that allows the robot to navigate smoothly while avoiding both static and dynamic obstacles. The developed control system incorporates four ANFIS controllers: two are tasked with guiding the robot toward its end point, and the other two are activated for obstacle avoidance. The experimental setup conducted in CoppeliaSim involves a mobile robot equipped with ultrasonic sensors navigating in an environment with static and dynamic obstacles. Simulation experiments are conducted to demonstrate the model's capability in ensuring collision-free navigation, employing a path-planning algorithm to ascertain the shortest route to the target destination. The simulation results highlight the superiority of the ANFIS-based approach over conventional methods, particularly in terms of computational efficiency and navigational smoothness. [ABSTRACT FROM AUTHOR]

In this study, under the limited volume of 18 cm × 18 cm × 21 cm, a small-sized mobile robot is designed and implemented. It consists of a CPU, a GPU, a 2D LiDAR (Light Detection And Ranging), and two fisheye cameras to let the robot have good computing processing and graphics processing capabilities. In addition, three functions of road detection, sign recognition, and obstacle avoidance are implemented on this small-sized robot. For road detection, we divide the captured image into four areas and use Intel NUC to perform road detection calculations. The proposed method can significantly reduce the system load and also has a high processing speed of 25 frames per second (fps). For sign recognition, we use the YOLOv4-tiny model and a data augmentation strategy to significantly improve the computing performance of this model. From the experimental results, it can be seen that the mean Average Precision (mAP) of the used model has increased by 52.14%. For obstacle avoidance, a 2D LiDAR-based method with a distance-based filtering mechanism is proposed. The distance-based filtering mechanism is proposed to filter important data points and assign appropriate weights, which can effectively reduce the computational complexity and improve the robot's response speed to avoid obstacles. Some results and actual experiments illustrate that the proposed methods for these three functions can be effectively completed in the implemented small-sized robot. [ABSTRACT FROM AUTHOR]

To address the issue of error-prone and unstable trajectory tracking and dynamic obstacle avoidance of mobile robots in locally observable nonlinear dynamic settings, a deep reinforcement learning (RL)-based visual perception, and decision-making system is proposed. The technique creates a closed loop between the system's environmental perception and decision-making capabilities by combining the perceptual capabilities of convolutional neural networks with the decision-making capabilities of RL in a generic form. It achieves direct output control from the visual perception input of the environment to the action through end-to-end learning. The simulation results show that this approach is capable of meeting the demands of multi-task intelligent perception and decision making. It also more effectively addresses issues with traditional algorithms, including their tendency to fall into local optimums, oscillate in groups of similar obstacles without recognizing the path, oscillate in tight spaces and inaccessible targets close to obstacles and significantly enhance real-time and adaptability of robot trajectory tracking and dynamic obstacle avoidance. [ABSTRACT FROM AUTHOR]

Object tracking is one of the major tasks for mobile robots in many real-world applications. Also, artificial intelligence and automatic control techniques play an important role in enhancing the performance of mobile robot navigation. In contrast to previous simulation studies, this paper presents a new intelligent mobile robot for accomplishing multi-tasks by tracking red-green-blue (RGB) colored objects in a real experimental field. Moreover, a practical smart controller is developed based on adaptive fuzzy logic and custom proportional-integral-derivative (PID) schemes to achieve accurate tracking results, considering robot command delay and tolerance errors. The design of developed controllers implies some motion rules to mimic the knowledge of experienced operators. Twelve scenarios of three colored object combinations have been successfully tested and evaluated by using the developed controlled image-based robot tracker. Classical PID control failed to handle some tracking scenarios in this study. The proposed adaptive fuzzy PID control achieved the best accurate results with the minimum average final error of 13.8 cm to reach the colored targets, while our designed custom PID control is efficient in saving both average time and traveling distance of 6.6 s and 14.3 cm, respectively. These promising results demonstrate the feasibility of applying our developed image-based robotic system in a colored object-tracking environment to reduce human workloads. [ABSTRACT FROM AUTHOR]

Recent technological advancements and a growing emphasis on circular and adaptable architectural designs within established built environments have catalyzed innovative approaches in the realm of human-building interfaces (HBIs). HBIs represent a transformative paradigm in architecture and construction, centered on integrating interactive technologies into pre-existing building surfaces. In this study, we investigate the design potential of novel interfaces tailored for existing building surfaces, employing readily available responsive materials and mobile robots. Leveraging accessible interactive technologies, we present a comprehensive review of three early-stage strategies of HBIs: PainterFace, PixelFace, and PixelGreen. These HBIs were aimed at enriching user interaction and experience within their surroundings, fostering spaces that are both adaptive and interactive. Experimental HBIs are constructed using a range of responsive materials and mobile robots, including conductive paints, spherical mobile robots, and unmanned aerial vehicles functioning as “agents” to facilitate communication, interactivity, and responsiveness on existing building surfaces. The multifaceted nature of HBIs showcased in this paper presents diverse approaches with substantial potential to transform established building surfaces into interactive and responsive environments. A comparative analysis of the three HBIs highlights potential implications and future avenues for HBIs. Moreover, the findings of these studies provide valuable insights into design considerations, challenges, and future trajectories for the development and implementation of HBIs on existing building surfaces, ushering in a new era of intelligent and responsive built environments. Future endeavors will focus on further research to address challenges and unlock the full potential of HBIs, ultimately leading to the advancement of enhanced, adaptable, circular, and sustainable architectural designs. [ABSTRACT FROM AUTHOR]

In order to enhance the speed and accuracy of cluster scheduling for mobile robots in the medication management system, a cluster scheduling model was established using mobile robots as carriers. The scheduling coding adopted a discrete-continuous dual-mapping encoding method. In order to enhance the computational performance of the scheduling algorithm, an improved harmony search (IHS) algorithm was proposed, which integrated the optimal estimation optimization method. The algorithm combined optimal iteration and directed search for optimal scheduling search, thereby improving the computational speed and accuracy of the algorithm. Adaptive bandwidth adjustment and global random crossover mutation were performed based on the global optimum and harmony, expanding the algorithm's search range and sample diversity, thereby enhancing the algorithm's ability to search and compute the global optimal solution. Furthermore, the improved algorithm was validated on continuous intervals using standard test functions, yielding favorable results. Through comparative experiments, the results show that under identical test conditions, the proposed improved algorithm exhibits superior capability in searching for optimal solutions compared to the control algorithm, resulting in varying degrees of improvement in the accuracy of scheduling results. It is evident that the IHS algorithm effectively optimizes the performance of the automatic medication system scheduling system. [ABSTRACT FROM AUTHOR]

This paper presents a low-cost system for simultaneous localization and mapping (SLAM) for unknown indoor environments. The system is based on a low-cost mobile-robot platform. The low-cost mobile robot is designed and fabricated in our control laboratory. The Rao-Blackwellized particle filter algorithm is used for SLAM computations, Xbox 360 Kinect module is utilized for stereo-camera imaging, and a Linux-based microcomputer (Raspberry Pi3) was used as the main onboard processing unit. An Arduino board is used to control the DC motors for mobile robot wheels. Raspberry Pi unit was wirelessly connected to a ground station machine that processes the information sent by the robot to build the environment map and estimate its pose. ROS (Robot Operating System) is used for map visualization, data-handling, and communication between different software nodes. The system has been tested virtually on a simulator and in real indoor environments and has successfully identified objects greater than 30 cm × 30 cm × 30 cm and added it to the map. It also shows promising capability to work autonomous missions independently without aid from any external sensors and with a fraction of the cost of similar systems based on Lidars. [ABSTRACT FROM AUTHOR]

The trajectory tracking accuracy of high-clearance mobile robots could be affected by phenomena such as wheel slippage caused by changes in the working surface material and motion state. To reveal the impact of slippage factor and improve the tracking precision of wheeled mobile robots, this study proposed a sliding mode of trajectory tracking control with soft switching. Considering the slippage working environment of mobile robots, a three-degrees-of-freedom kinematic model of mobile robots containing slip rate was established. Based on the kinematic model of the system, a trajectory tracking controller utilizing soft switching sliding mode control was developed, followed by a comprehensive stability analysis. The MATLAB platform was utilized to verify the circular trajectory tracking performance of the mobile robot under both slipping and nonslipping conditions. Results demonstrate that: (1) Under ideal conditions (disregarding wheel slippage), the sliding mode tracker based on model design quickly achieves high-precision tracking of the robot's trajectory, with the tracking error controlled at 0.002 m within 0.6 s in a circular trajectory, capable of adjusting and tracking the desired circular trajectory in a short time and maintaining consistent posture. (2) When considering the effect of slippage, the uncompensated sliding mode control produces a larger periodic tracking error (the amplitude of the circular trajectory reaches 0.065 m) and a larger direction angle error at the trough of the lateral position, causing a sudden change in the direction angle. Nevertheless, the proposed sliding compensation in this study can improve its accuracy by about twice, with the tracking error controlled around 0.03 m in circular trajectories, effectively improving the system's tracking accuracy. This study expands the working environment of mobile robots and provides a basis for their high-performance operation in complex environments. [ABSTRACT FROM AUTHOR]

The modeling structures of rollers, mecanum wheels, and mecanum wheeled mobile robots presented in the literature use single contact force assumption. This assumption may give good results in a simulation environment; however, it is not strong enough to reflect reality. To make an improvement, a new aspect of mecanum wheel model is proposed in this study. The model takes the variable roller contact forces into account and investigates their effects on the performance of motion of a mecanum wheeled mobile robot. It uses all points on each roller's curved shape so that the slippage phenomena is also taken into consideration which makes it possible to get less position estimation errors in real-time operations. The modeling structure introduced aims to reflect reality both in simulation and real applications. A simulation environment is developed for this study. To make verification, an experimental setup including a four-mecanum-wheeled mobile robot, its mechanical and electrical hardware and software infrastructures, and a ground-truth system is designed and constructed. A Robot Operating System (ROS) based control system is created and integrated into the experimental system. Different types of reference trajectories including straight-line, square-shaped, Z-shaped, and wave(S)-shaped are used to test the performance of the model proposed in both simulation and experimental studies. The tests are also conducted using the model that involves single contact force assumption to make comparisons. The details of the variable contact forces model proposed, simulation environment developed, experimental setup built, simulation and experimental studies, their results, and comparisons are given in this paper. [ABSTRACT FROM AUTHOR]

Mobile robots play an important role in the industrial Internet of Things (IIoT); they need effective mutual communication between the cloud and themselves when they move in a factory. By using the sensor nodes existing in the IIoT environment as relays, mobile robots and the cloud can communicate through multiple hops. However, the mobility and delay sensitivity of mobile robots bring new challenges. In this paper, we propose a dynamic cooperative transmission algorithm with mutual information accumulation to cope with these two challenges. By using rateless coding, nodes can reduce the delay caused by retransmission under poor channel conditions. With the help of mutual information accumulation, nodes can accumulate information faster and reduce delay. We propose a two-step dynamic algorithm, which can obtain the current routing path with low time complexity. The simulation results show that our algorithm is better than the existing heuristic algorithm in terms of delay. [ABSTRACT FROM AUTHOR]

Laser SLAM (Simultaneous Localization and Mapping) and visual SLAM have been fully developed and widely used in military and civil fields. However, single sensor SLAM has limitations, for instance, laser SLAM is not suitable for scenes with a large number of dynamic objects around it, while visual SLAM has poor robustness in low-texture environments. Therefore, fusion of the two technologies has great potential to compensate each other, and it can be prospected that SLAM technology combining laser and vision or even more sensors will be the mainstream direction in the future. Here, we review the development of SLAM technology, analyze the hardware information of lidar and camera, and introduce some classical open-source algorithms and datasets. Furthermore, the multi-sensor fusion schemes are detailed from perspectives of uncertainty, feature and novel deep learning. The excellent performance of multi-sensor fusion schemes in complex scenes are summarized, and the future development trend of multi-sensor fusion is prospected. [ABSTRACT FROM AUTHOR]

With the rapid development of new-generation artificial intelligence and Internet of Things technology, mobile robot technology has been widely used in various fields. Among them, the autonomous path-planning technology of mobile robots is one of the cores for realizing their autonomous driving and obstacle avoidance. This study conducts an in-depth discussion on the real-time and dynamic obstacle avoidance capabilities of mobile robot path planning. First, we proposed a preprocessing method for obstacles in the grid map, focusing on the closed processing of the internal space of concave obstacles to ensure the feasibility of the path while effectively reducing the number of grid nodes searched by the A* algorithm, thereby improving path search efficiency. Secondly, in order to achieve static global path planning, this study adopts the A algorithm. However, in practice, algorithm A has problems such as a large number of node traversals, low search efficiency, redundant path nodes, and uneven turning angles. To solve these problems, we optimized the A* algorithm, focusing on optimizing the heuristic function and weight coefficient to reduce the number of node traversals and improve search efficiency. In addition, we use the Bezier curve method to smooth the path and remove redundant nodes, thereby reducing the turning angle. Then, in order to achieve dynamic local path planning, this study adopts the artificial potential field method. However, the artificial potential field method has the problems of unreachable target points and local minima. In order to solve these problems, we optimized the repulsion field so that the target point is at the lowest point of the global energy of the gravitational field and the repulsive field and eliminated the local optimal point. Finally, for the path-planning problem of mobile robots in dynamic environments, this study proposes a hybrid path-planning method based on a combination of the improved A* algorithm and the artificial potential field method. In this study, we not only focus on the efficiency of mobile robot path planning and real-time dynamic obstacle avoidance capabilities but also pay special attention to the symmetry of the final path. By introducing symmetry, we can more intuitively judge whether the path is close to the optimal state. Symmetry is an important criterion for us to evaluate the performance of the final path. [ABSTRACT FROM AUTHOR]

In recent years, mobile robots have been widely used in various fields such as emergency rescue, public security, inspection and detection. In the process of robot movement, the ability to perceive the environment is particularly important, especially in outdoor wide area work scenes involving dynamic, unknown, and complex factors. Obstacle detection greatly affects the functionality and efficiency of the robot. A comparison was made on the research progress of obstacle detection for ground mobile robots in such scenarios, and positive and negative obstacle detection methods were analyzed based on the characteristics of obstacles. Furthermore, the current application status of information fusion and deep learning in the current field was summarized. Finally, the current research status in the field of obstacle detection was summarized, and the exploration direction of related technologies was pointed out. [ABSTRACT FROM AUTHOR]

Due to the multiple types of objects and the uncertainty of their geometric structures and scales in indoor scenes, the position and pose estimation of point clouds of indoor objects by mobile robots has the problems of domain gap, high learning cost, and high computing cost. In this paper, a lightweight 6D pose estimation method is proposed, which decomposes the pose estimation into a viewpoint and the in-plane rotation around the optical axis of the viewpoint, and the improved PointNet + ⁣ + network structure and two lightweight modules are used to construct a codebook, and the 6d pose estimation of the point cloud of the indoor objects is completed by building and querying the codebook. The model was trained on the ShapeNetV2 dataset, and reports the ADD-S metric validation on the YCB-Video and LineMOD datasets, reaching 97.0% and 94.6% respectively. The experiment shows that the model can be trained to estimate the 6d position and pose of the unknown object point cloud with lower computation and storage cost, and the model with fewer parameters and better real-time performance is superior to other high-recision methods. [ABSTRACT FROM AUTHOR]

The need for safe, predictable, and reliable robot navigation is fundamental for mobile robots to move around in home and office environments. Shortest-path navigation is a popular robot navigation method that uses the most efficient path to get to the desired goal. This behaviour is not always easy to interpret, understand, and avoid in a congested hallway. Instead, more predictable navigation methods, such as a robot following a wall, can help increase social acceptance and help avoid the robot crossing the pedestrian path. If a robot follows along a wall, a key variable to consider is the preferred driving side of the robot (left or right) in areas such as in narrow passages, and its perceived impact on social acceptance. This international user study (n = 143) involved an online video-based test to compare robot evaluation and social acceptance for two types of mobile navigation (Wall-Following and Shortest Path), including the preferred driving side for Wall-Following. A Fetch robot navigated from start to goal position in a series of indoor scenarios with a pedestrian. Select scenarios included a hallway, doorway, and intersection. Independent Sample T-Tests results found that Wall-Following was rated significantly higher than Shortest Path for being perceived as more comfortable and predictable, regardless of robot driving side. The preference for the driver side of the robot did not match the country of residence, nor did it have a significant impact on robot ratings. There were significant interaction effects for comfort, safety and predictable scores across two timepoints. Given the popularity of Shortest Path navigation, the findings indicate that this approach might not be the most appropriate for human settings. Additional investigation into Wall-Following behaviours is recommended for social acceptance, even if the method compromises the efficiency of the robot to acheive its objective. [ABSTRACT FROM AUTHOR]

Social robots are increasingly being deployed in public environments. However, few studies have suggested ways to design robot upper body and body vertical-oscillation to enhance the robot's emotional expressions during 'walking'. This study presents a novel body-avatar interface (BoAI) enabling real-time mapping of human movements onto a robot. Using this interface, participants designed emotional upper body movements for the robot. Further, drawing inspiration from vertical oscillations in human emotional gaits, we propose emotion-specific vertical oscillation patterns for the robot. To evaluate the robot's resulting emotional expression, two video-based subjective studies were conducted with 307 and 869 participants respectively, by utilizing Mean Opinion Score (MOS) [1] and Godspeed [2] questionnaires. The results demonstrate that our proposed emotion-specific vertical oscillations significantly enhanced the robot's perceived emotional expressivity, anthropomorphism, and animacy during walking compared to neutral motion. This paper makes key contributions in designing emotional expressions for mobile robots. The BoAI and emotion-specific vertical oscillation patterns open new possibilities for improving robots' ability to express emotion during 'walking', offering possibilities to expand social robots across diverse interactive scenarios. [ABSTRACT FROM AUTHOR]

To achieve the autonomy of mobile robots, effective localization is an essential process. Among localization algorithms, the Adaptive Monte Carlo Localization (AMCL) algorithm is most commonly used in many indoor environments. However, when the initial position is unknown, the efficiency and success rate of localization based on the AMCL algorithm decrease with the increasing area of the map. In this paper, an improved MCL algorithm named off-line feature matching and improved particle swarm optimization for Monte Carlo Localization (OFM-IPSO MCL) is proposed. Feature matching is adopted to reduce the online computational burden. Compared with the AMCL algorithm, OFM-IPSO MCL shows better results in the problems of positioning without initial pose and kidnapping robot by using a small number of particles. For positioning without an initial pose, the OFM-IPSO algorithm uses the feature extraction and feature matching methods to find the possible positions of the robot. In the problem of kidnapping robot, a method for determining if the robot has been "kidnapped" is proposed, which determines whether the robot has lost its pose. The validity and efficiency of the OFM-IPSO MCL algorithm are demonstrated by the Robotic Operating System (ROS). Extensive results and comparisons are also provided in this paper. [ABSTRACT FROM AUTHOR]

This research aims to design controllers based on the hedge-algebras (HA) theory to control differential robots that track reference trajectories. First, the HA-based controller (denoted as HA controller) is synthesized by selecting a suitable qualitative control rule base for the investigated model as a rule-based optimization problem. Then, the optimal HA-based controller (denoted as oHA controller) is established based on the problem of simultaneously optimizing the rule base, the reference interval of variables, and the fuzzy parameters of the variables. Optimization problems aim to minimize the distance between the robot and the reference trajectory. The optimization problems in this study use the Balancing composite motion optimization (BCMO) algorithm. A controller based on fuzzy set theory (denoted as FC controller) with the same parameters as the HA controller is also included for comparison. The simulation results show that the HA and oHA controllers demonstrate many advantages over the FC controller regarding reference trajectory tracking ability, calculation time, and control robustness. The main contribution of this work consists of (i) The development of a novel HA, oHA approaches to control a mobile robot to follow reference trajectories accurately; (ii) Providing optimal global-based BCMO in terms of minimal tracking error with computational efficiency; (iii) The investigation of one control rule base for HA and oHA controllers, which is effective for many different reference orbits; (iv) The development of a robust controller that adapts to the robot's geometric parameters changes; (v) The proposed controllers have superior performance results compared to controllers based on fuzzy set theory in terms of position error between the robot and the reference trajectory, control action calculation time, and robust ability to change robot parameters. [ABSTRACT FROM AUTHOR]

This article presents a novel approach for generating a three‐dimensional radiation map using data collected by mobile robots, aimed at monitoring radiation distribution in environments such as nuclear power plants. The proposed approach leverages Gaussian process regression with a novel adaptation of the inverse square law as a kernel function, which accurately reflects the physical characteristics of radiation, enabling precise mapping from uncertain data. Additionally, a method is proposed for constructing a comprehensive radiation map in 3D environments by estimating the radiation source and project the radiation data from sparse data. The effectiveness of the methodology is validated through simulations and experiments. Utilizing Octomap, a 3D spatial mapping tool, the study not only successfully visualizes radiation distribution in complex settings with multiple sources but also quantitatively demonstrates the enhanced accuracy of our approach compared to other existing methods. This research offers contributions in managing radiation risks, providing essential insights into the field. [ABSTRACT FROM AUTHOR]

This article presents an Adaptive Fuzzy Logic Dynamic Surface Controller (AFDSC) combined with the A* optimal path-finding algorithm for mobile robots' following trajectory tracking with the nonlinear system changes in robot parameters and is affected by wheel sliding friction disturbances when operating on different terrains. This algorithm is built based on the DSC dynamic sliding surface control algorithm, promoting the effective advantages of DSC and using fuzzy logic to adaptively adjust the coefficients of the virtual control signal and keep The system status signal located on the sliding surface to overcome the instability of DSC when encountering this state. The stability and convergence of the closed-loop system are guaranteed based on Lyapunov analysis. The robot's path planning trajectory is performed by the A* algorithm. At the same time, the content of the article mentions programming and experimental operation for mobile robots using the ROS2 Rolling with Focal (20.04) software operating system on the Jetson Nano 4G embedded computer. The correctness, the proposed controller’s effectiveness, and the possibility of practical applications. Orbits are set as two periodic functions of period T as follows. Theoretical and experimental simulation results with position deviation-axis from 0.0038(m) to 0.0063(m), y-axis from 0.0029(m) to 0.0049(m), from 0.0021(rad) to 0.0035(rad). And experimental results with position error in the x-axis from 0.0062(m) to 0.0105(m), y-axis from 0.0042(m) to 0.0069(m), and 0.0031(rad) to 0.0053(rad)).

The primary challenge faced by a neural controller in the dynamic model of a mobile robot lies in its ability to address the inherent complexity of the system dynamics. Given that mobile robots exhibit nonlinear movements and are subject to diverse environmental conditions, they contend with a challenging dynamic environment. The neural controllers must demonstrate the capability to continuously adapt and effectively learn to manage the variability present in the dynamic of the robot. This paper presents two intelligent controllers utilizing neural networks, showcasing their relevance in the field of robotics. The first controller, referred to as the neural PID (PIDN), integrates the traditional PID controller with a neural component. The second controller leverages the dynamic model of a differential robot to improve trajectory tracking, employing a parallel architecture that combines PID with neural networks (PID+NN). Our proposals adhere to a cascading structure, where the outer loop takes the lead in reducing position errors through a kinematic controller, while concurrently, the inner loop is employed to regulate linear and angular velocities through the proposed controllers. The controllers are validated in the CoppeliaSIM simulator, offering a realistic setting for evaluating the behavior of the chosen Pioneer 3-DX robot. To comprehensively assess controller performance, three strategies are examined: PIDN, PID+NN, and the conventional PID. Through a blend of qualitative and quantitative analyses, employing diverse performance metrics, the advantages of our proposed controllers become apparent. Doi: 10.28991/ESJ-2024-08-04-01 Full Text: PDF

Abstract This paper presents a low-cost system for simultaneous localization and mapping (SLAM) for unknown indoor environments. The system is based on a low-cost mobile-robot platform. The low-cost mobile robot is designed and fabricated in our control laboratory. The Rao-Blackwellized particle filter algorithm is used for SLAM computations, Xbox 360 Kinect module is utilized for stereo-camera imaging, and a Linux-based microcomputer (Raspberry Pi3) was used as the main onboard processing unit. An Arduino board is used to control the DC motors for mobile robot wheels. Raspberry Pi unit was wirelessly connected to a ground station machine that processes the information sent by the robot to build the environment map and estimate its pose. ROS (Robot Operating System) is used for map visualization, data-handling, and communication between different software nodes. The system has been tested virtually on a simulator and in real indoor environments and has successfully identified objects greater than 30 cm × 30 cm × 30 cm and added it to the map. It also shows promising capability to work autonomous missions independently without aid from any external sensors and with a fraction of the cost of similar systems based on Lidars.

Abstract The objective of the path planning for a mobile robot is to generate a collision-free path from a starting position to a target position, aiming to realize a higher quality of path planning, an improved A* algorithm and a hybrid approach incorporating the dynamic window algorithm have been proposed for robot path planning in various environments in this paper. In global path planning, first, a bidirectional search strategy was introduced into to improve the searching efficiency, and an adaptive heuristic function was designed to reduce redundant search nodes. In the meantime, a filtering function for key path nodes and an enhanced jump point optimization method help to remove redundant nodes in the path, reduce turning angles, greatly shorten the path length, and smooth the path using cubic B-spline curves. Furthermore, in local path planning, the combination of key path nodes and the dynamic window approach (DWA) algorithm is utilized to achieve obstacle avoidance in dynamic environments and adjust the heading angle of the section enables seamless locomotion of the robot. Finally, the simulation experiments and physical experiments on the robot were conducted to validate that the proposed improved algorithm significantly improves the speed of path planning while also reducing the length of the planned path and improve the reliability of the algorithm when compared with other algorithms.

This work aims to develop a mobile robot utilizing neural network technology. The algorithm, programmed in Python on a Raspberry Pi 4B platform, is detailed across four main chapters. These chapters cover the fundamental assumptions of deep learning, the construction of the platform, and the research validating pattern recognition accuracy under various disturbances. The mobile platform employs a neural network to analyze selected traffic signs and translates the recognized patterns into corresponding motor movements.

This research paper proposes a hybrid fuzzy logic controller for achieving autonomous path navigation and obstacle avoidance through the use of the Social Spider Optimizer algorithm. The proposed controller employs kinematic modelling to determine the mobile robot’s path navigation and utilizes a fuzzy logic system for effective control. The Social Spider Optimizer algorithm optimizes the parameters of the fuzzy controller, while the FLC is responsible for obstacle avoidance. The effectiveness of the proposed controller has been analyzed, and a comparative study has been carried out with optimization techniques like particle swarm optimization (PSO) and cuckoo search optimization (CSO) controllers. The study aims to propose a hybrid fuzzy logic controller, that provides efficient navigation and obstacle avoidance for mobile robots. In a simulation, the starting point is considered as (0,0) and the destination point is set as Xk = 1.1 and Yk = 1.2. The performance of the proposed method is compared with FLC and methods like PSO and CSO. With the SSO-based FLC, the proposed mobile robot identifies the obstacle distance and travels towards the destination with smooth navigation. The results show the efficacy of the proposed controller in comparison to other controllers for mobile robots in terms of path navigation and obstacle avoidance.