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Last-mile delivery is a costly and time-consuming process in e-commerce operations. Piloted by several companies, autonomous mobile robots cooperating with trucks have been implemented for parcel delivery. In this system, a truck departs from the warehouse, carrying robots and parcels, and traverses several drop-off points to release robots for parcel delivery or to deliver parcels by itself. We focus on performance estimation and system configuration, considering the no-zoning policy, where all trucks serve the whole delivery area and the zoning policy, where each truck takes charge of one zone. A queuing network is constructed to estimate the parcel throughput time and a cost minimisation model is developed for the system with a required throughput time. The optimal number of drop-off points and batch size can be numerically found to minimise the order throughput time. The zoning policy is cheaper than no-zoning policy for a small number of drop-off points and a short required throughput time, and it is cheaper in a system with a large number of drop-off points. Moreover, we find that while the truck-based autonomous mobile robot delivery system typically has a larger order throughput time than a delivery system using only robots, it is much less costly. [ABSTRACT FROM AUTHOR]

Current studies on scheduling of machines and transporters assume that either a single transporter or an infinite number of homogeneous transporters such as AGVs or mobile robots are available to transport semi-finished jobs, which seems very restrictive in practice. This paper addresses this gap by studying a job-shop scheduling problem that incorporates a limited number of heterogeneous transporters, where the objective is to minimize the makespan. The problem is modelled using mixed-integer linear programming and constraint programming. Different structure-based branch-and-bound algorithms with two lower-bounding strategies are also developed. A comprehensive numerical study evaluates the proposed models and algorithms. The research demonstrates that the adjustment of the proposed MILP model outperforms the existing formulation when applied to the homogeneous case. The study also uncovers interesting practical implications, including the analysis of the impact of different transporter types in the system. It shows that utilizing a fleet of heterogeneous transporters can improve the overall performance of the job shop compared to a relevant homogeneous case. The importance of the study is emphasized by highlighting the negative consequences of disregarding transporters' differences during the scheduling phase. [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]

People across the globe witnessed the meteoric rise of the recently discovered coronavirus, which caught governments, healthcare systems, and civilisations off guard. Robotics, self-governing systems, and intelligent devices can contribute positively to a rapid epidemic. Smart assistive robots can help patients and healthcare staff in general and make the anticipation, containment, and exacerbation of coronavirus easier by highlighting the coronavirus burden on healthcare structures during a crisis. In this type of situation, a smart-assistive robot with aid capabilities is promising, especially for those putting their lives in jeopardy by interacting with ill patients in hygienic environments. By providing regular monitoring and patient assistance, the intelligent helper robot could assist in lowering the danger of illness spreading to others. Concerning the application this paper discusses, a robot with a wheeled base and a humanoid structure can aid isolated patients and connect wirelessly anywhere in the world (using the Internet of Things) with patients far away more effectively without any physical touch. The designed robot is incredibly light (approximately 7 kg) and easily transported to the appropriate location (any hospital or medical camp, for example). Its modular design allows quick assembly and disassembly. The robot can comfortably handle 10–15 kg of load on its holding plate. This study also explains the structural capability and method of directing the robot in an unfamiliar area using ultrasonic sensors for obstacle avoidance, a camera module for real-time vision, and a Skype call feature that may enable doctors to keep tabs on a patient's health. [ABSTRACT FROM AUTHOR]

Introduction: Service robotics is recently enhancing precision agriculture enabling many automated processes based on efficient autonomous navigation solutions. However, data generation and in-field validation campaigns hinder the progress of large-scale autonomous platforms. Simulated environments and deep visual perception are spreading as successful tools to speed up the development of robust navigation with low-cost RGB-D cameras. Materials and methods: In this context, the contribution of this work resides in a complete framework to fully exploit synthetic data for a robust visual control of mobile robots. A wide realistic multi-crops dataset is accurately generated to train deep semantic segmentation networks and enabling robust performance in challenging real-world conditions. An automatic parametric approach enables an easy customization of virtual field geometry and features for a fast reliable evaluation of navigation algorithms. Results and conclusion: The high quality of the generated synthetic dataset is demonstrated by an extensive experimentation with real crops images and benchmarking the resulting robot navigation both in virtual and real fields with relevant metrics. [ABSTRACT FROM AUTHOR]

Studies in the area of mobile robotics have advanced in recent years, mainly due to the evolution of technology and the growing need for automated and dynamic solutions in sectors such as industry, transport, and agriculture. These devices are complex and the ideal method for localizing, mapping, and navigating autonomous mobile robots changes depending on the application. Thus, the general objective of this work is to propose a simultaneous localization and mapping method for autonomous mobile robots in indoor environments, using computer vision (CV) and Petri net (PN). A landmark was placed next to each door in the analyzed region and images were acquired as the rooms in the environment were explored. The algorithm processes the images to count and identify the doors. A transition is created in the PN for each door found and the rooms connected by these doors are represented by the places in the PN. Then, one of the doors is crossed, new images are obtained and the process is repeated until all rooms are explored. The algorithm generates a PN, which can be represented by an image file (.png) and a file with the extension.pnml. The results compare the layout of four environments with the respective generated PNs. Furthermore, six evaluation criteria are proposed for validating Petri nets as a topological map of environments. It is concluded that using PN for this purpose presents originality and potential innovation, being a SLAM technique for indoor environments, which demands low computational cost. [ABSTRACT FROM AUTHOR]

Fires are one of the most dangerous hazards and the leading cause of death in construction sites. This paper proposes a video-based firefighting mobile robot (FFMR), which is designed to patrol the desired territory and will constantly observe for fire-related events to make sure the camera without any occlusions. Once a fire is detected, the early warning system will send sound and light signals instantly and the FFMR moves to the right place to fight the fire source using the extinguisher. To improve the accuracy and speed of fire detection, an improved YOLOv3-Tiny (namely as YOLOv3-Tiny-S) model is proposed by optimizing its network structure, introducing a Spatial Pyramid Pooling (SPP) module, and refining the multi-scale anchor mechanism. The experiments show the proposed YOLOv3-Tiny-S model based FFMR can detect a small fire target with relatively higher accuracy and faster speed under the occlusions by outdoor environment. The proposed FFMR can be helpful to disaster management systems, avoiding huge ecological and economic losses, as well as saving a lot of human lives. [ABSTRACT FROM AUTHOR]

Inspired by natural swarm systems, robotic swarms aim to solve complicated problems through the emergent behaviour of coordinating robots (agents). Communication among the robots is of paramount importance for their effective coordination, cooperation, and overall performance. This research presents a colour light-based communication system for miniature mobile swarm robots, on which a pre-trained supervised machine learning model runs and is responsible for effective colour recognition, enhancing inter-robot local communication. The performance of various supervised machine learning techniques was examined, and XGBoost performed best overall, with a classification accuracy of 96.66%, an execution time of 0.403 ms, an average sensing distance of 87.38 cm, and an acceptable size of 402.1 kilobytes while running on a 32-bit embedded microcontroller. The current work also demonstrates various swarming behaviours, utilising the developed communication as proof of concept. [ABSTRACT FROM AUTHOR]

Target tracking is a key component of control systems with applications in various domains. Several examples of commercial applications may be given, which benefit from this technology: the development of car collision avoidance systems which must detect and track potential obstacles and hazards, or the development of UAVs that can track and record the evolution of athletes. For the purpose of our research, this technology was developed and tested as part of a less complex application. The current work describes a simple yet practical implementation of vision-based control for a mobile robot system. In the experiment, we used a mobile robot as target and a similar robot as follower. To achieve the tracking task, the used strategy involves the detection of a specific visual object mounted on the target, by extracting its features which are then used in issuing control commands within a remotely-closed loop. A Deep Learning approach is used for object detection, incorporating a detector model into the strategy while preserving an explicit controller in the overall scheme. The carried experiment has proven that this new approach provides the expected results, which make it a suitable tool for development of larger scale applications of control systems. [ABSTRACT FROM AUTHOR]

High product diversity, dynamic market conditions, and a lack of skilled workers are current challenges in manufacturing. Industrial robots autonomously planning and completing upcoming production tasks can help companies address these challenges. In this publication, we focus on autonomous task planning within industrial robotics and investigate how to facilitate the use of automated planning techniques from the field of artificial intelligence for this purpose. First, we present a novel methodology to automatically adapt abstractly modeled planning domains to the characteristics of individual application cases a user intends to implement. A planning domain is a formalized representation of the robot's working environment that builds the basis for automated planning. Second, we integrate this approach into the procedure for developing skills-based industrial robotic applications to enable them to perform autonomous task planning. Finally, we demonstrate the use of the methodology within the application field kitting in two reference scenarios with a mobile robot and a stationary robot cell. Using our methodology, persons without expertise in automated planning can enable a robot for autonomous task planning without much extra effort. [ABSTRACT FROM AUTHOR]

The application of reinforcement learning in mobile robotics faces the challenges of real-world physical environments, in contrast to playground setups like video games. In a mobile robot motion control, it is not always possible to perform episodes of pre-training in large amounts due to time, resource limitations or other concerns. Control methods that rely on a prior explicit map may be impractical or even impossible to use for new dynamic environments. In this paper, we present a method of local navigation approach for driving a robot to a desired position without relying on an explicit map of the environment. Only the laser scan measurements were used to determine the obstacles. We focus in this work on online methods of reinforcement learning which do not require running the robot in full episodes until success or failure. However, the price for such an online capability is that some model knowledge about the environment has to be utilized. Here, we propose an algorithm called stacked Q-learning, which unifies aspects of standard reinforcement learning techniques with model-based predictive agents. We compare this algorithm to a classical model predictive controller. The comparison focuses on the accumulated cost of parking the robot avoiding obstacles. The results look promising as the stacked Q-learning beat its counterpart, model predictive control, yet being of the same computational complexity. The suggested agent design of stacked Q-learning can thus be taken as a foundation for a class of predictive reinforcement learning methods. [ABSTRACT FROM AUTHOR]

Automated colour sorting, aided by mobile robots, is widely prevalent in the current manufacturing industry. Obstacles, such as fluctuating light conditions and camera angles, frequently hinder this procedure. Creating a colour sorting robot is a complex and time-consuming task, especially due to the vulnerability of the RGB colour space to detection errors in extreme brightness or darkness. In response to these concerns, we introduce a mobile robot that operates on the robot operating system (ROS) platform and incorporates OpenCV. This robot employs the hue, saturation, and value (HSV) colour space model for its image processing capabilities in recognising the colours and Welzl's algorithm for the ball's diameter estimation. The robot's performance was assessed across various luminous fluxes and camera tilt angles. It demonstrated exceptional performance at 64 lm and a tilt angle of 40 degrees, achieving an average accuracy of 87.5% for detecting the colour of the ball, and 81.25% for determining its location based on colour. For the ball's diameter estimation, it was found that the best estimation was received at 64 lm and 30 degrees, with both 96.32%. [ABSTRACT FROM AUTHOR]

In the field of autonomous mobile robots, sampling-based motion planning methods have demonstrated their efficiency in complex environments. Although the Rapidly-exploring Random Tree (RRT) algorithm and its variants have achieved significant success in known static environment, it is still challenging in achieving optimal motion planning in unknown dynamic environments. To address this issue, this paper proposes a novel motion planning algorithm Bi-HS-RRT X , which facilitates asymptotically optimal real-time planning in continuously changing unknown environments. The algorithm swiftly determines an initial feasible path by employing the bidirectional search. When dynamic obstacles render the planned path infeasible, the bidirectional search is reactivated promptly to reconstruct the search tree in a local area, thereby significantly reducing the search planning time. Additionally, this paper adopts a hybrid heuristic sampling strategy to optimize the planned path quality and search efficiency. The convergence of the proposed algorithm is accelerated by merging local biased sampling with nominal path and global heuristic sampling in hyper-ellipsoid region. To verify the effectiveness and efficiency of the proposed algorithm in unknown dynamic environments, numerous comparative experiments with existing algorithms were conducted. The experimental results indicate that the proposed planning algorithm has significant advantages in planned path length and planning time. [ABSTRACT FROM AUTHOR]

Mobile telepresence robots can help reduce loneliness by facilitating people to visit each other and have more social presence than visiting via video or audio calls. However, using new technology can be challenging for many older adults. In this article, we examine how older adults use and want to use mobile telepresence robots, how these robots affect their social connection, and how they can be improved for older adults' use. We placed a mobile telepresence robot in the home of older adult primary participants (N = 7; age 60 \(+\)) for 7 months and facilitated monthly activities between them and a secondary participant (N = 8; age 18 \(+\)) of their choice. Participants used the robots as they liked between monthly activities. We collected diary entries and monthly interviews from primary participants and a final interview from secondary participants. Results indicate that older adults found many creative uses for the robots, including conversations, board games, and hide "n" seek. Several participants felt more socially connected with others and a few had improved their comfort with technology because of their use of the robot. They also suggested design recommendations and updates for the robots related to size, mobility, and more, which can help practitioners improve robots for older adults' use. [ABSTRACT FROM AUTHOR]

Continual learning (CL) has emerged as an important avenue of research in recent years, at the intersection of Machine Learning (ML) and Human–Robot Interaction (HRI), to allow robots to continually learn in their environments over long-term interactions with humans. Most research in CL, however, has been robot-centered to develop CL algorithms that can quickly learn new information on systematically collected static datasets. In this article, we take a human-centered approach to CL, to understand how humans interact with, teach, and perceive CL robots over the long term, and if there are variations in their teaching styles. We developed a socially guided CL system that integrates CL models for object recognition with a mobile manipulator robot and allows humans to directly teach and test the robot in real time over multiple sessions. We conducted an in-person study with 60 participants who interacted with the CL robot in 300 sessions with 5 sessions per participant. In this between-participant study, we used three different CL models deployed on a mobile manipulator robot. An extensive qualitative and quantitative analysis of the data collected in the study shows that there is significant variation among the teaching styles of individual users indicating the need for personalized adaptation to their distinct teaching styles. Our analysis shows that the constrained experimental setups that have been widely used to test most CL models are not adequate, as real users interact with and teach CL robots in a variety of ways. Finally, our analysis shows that although users have concerns about CL robots being deployed in our daily lives, they mention that with further improvements CL robots could assist older adults and people with disabilities in their homes. [ABSTRACT FROM AUTHOR]

The "Author Index Volume 18 (2024)" from the International Journal of Semantic Computing lists various authors and their respective articles published in the journal. Topics covered include AI ecosystems, video codecs, image classification, knowledge graphs, and robotic systems. The index provides a comprehensive overview of the research contributions in the field of semantic computing for the year 2024. [Extracted from the article]

Accurate and robust localization is essential for autonomous mobile robots. Map matching based on Light Detection and Ranging (LiDAR) sensors has been widely adopted to estimate the global location of robots. However, map‐matching performance can be degraded when the environment changes or when sufficient features are unavailable. Indiscriminately incorporating inaccurate map‐matching poses for localization can significantly decrease the reliability of pose estimation. This paper aims to develop a robust LiDAR‐based localization method based on map matching. We focus on determining appropriate weights that are computed from the uncertainty of map‐matching poses. The uncertainty of map‐matching poses is estimated by the probability distribution over the poses. We exploit the normal distribution transform map to derive the probability distribution. A factor graph is employed to combine the map‐matching pose, LiDAR‐inertial odometry, and global navigation satellite system information. Experimental verification was successfully conducted outdoors on the university campus in three different scenarios, each involving changing or dynamic environments. We compared the performance of the proposed method with three LiDAR‐based localization methods. The experimental results show that robust localization performances can be achieved even when map‐matching poses are inaccurate in various outdoor environments. The experimental video can be found at https://youtu.be/L6p8gwxn4ak. [ABSTRACT FROM AUTHOR]

Image dehazing and object detection are two different research areas that play a vital role in machine learning. When merged together and implemented in real‐time, it is a boon in the field of artificial intelligence, specifically robotics. Object detection and tracking are two of the major implementations in almost the entire robot's training and learning. The learning of the robot depends on the images; these images can be camera‐captured images or a pretrained data set. Real‐time outdoor images clicked in bad weather conditions, such as mist, haze, smog, and fog, often suffer from poor visibility, and the consequences are incorrect results and hence an unexpected robot's behavior. To overcome these consequences, we have presented a novel approach to object detection and identification during adverse weather conditions. This method is proposed to be implemented in a real‐time environment to monitor animal behavior near railway tracks during fog, haze, and smog. This is not limited to specific application areas but can be used to identify endangered species and take active steps to save them from mishap. The deployment is done in a real‐time indoor environment using Tortoisebot mobile robot with a robot operating system framework. [ABSTRACT FROM AUTHOR]

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Endoluminal and endocavitary intervention via natural orifices of the body is an emerging trend in medicine, further underpinning the future of early intervention and precision surgery. This motivates the development of small continuum robots to navigate freely in confined and tortuous environment. The trade-off between a large range of motion and high precision with concomitant actuation cross-talk poses a major challenge. Here, we present a submillimeter-scale fiber robot (~1 mm) capable of decoupled macro and micro manipulations for intervention and operation. The thin optical fibers, working both as mechanical tendons and light waveguides, can be pulled/pushed to actuate the macro tendon-driven continuum robot and transmit light to actuate the liquid crystal elastomer-based micro built-in light-driven parallel robot. The combination of the decoupled macro and micro motions can accomplish accurate cross-scale motion from several millimeters down to tens of micrometers. In vivo animal studies are performed to demonstrate its positioning accuracy of precise micro operations in endoluminal or endocavitary intervention. [ABSTRACT FROM AUTHOR]

Pathfinding is the process of finding the lowest cost route between a pair of points in space. The aforementioned cost can be based on time, distance, the number of required turns, and other individual or complex criteria. Pathfinding in dynamic environments is a complex issue, which has a long history of academic interest. An environment is considered dynamic when its topology may change in real time, often due to human interference. Influence mapping is a solution originating from the field of video games, which was previously used to solve similar problems in virtual environments, but achieved mixed results in real-life scenarios. The purpose of this study was to find whether the algorithm could be used in real indoor environments when combined with information collected by remote sensors. [ABSTRACT FROM AUTHOR]

ABSTRACT In this article, we develop a novel adaptive extremum‐seeking control (AdESC) algorithm with robustness guarantees and without persistence of excitation (PE). Specifically, this builds on a proportional‐integral (PI)‐like parameter estimator. A zeroth‐order optimization framework is used, where the optimizer/agent can only query the numerical value of the cost function at the current coordinate given an unmodeled bounded disturbance. Since parameter estimation plays a decisive role in the stability and convergence properties of AdESC algorithm, it is also well established in the existing literature that to ensure parameter convergence a stringent PE condition is required. Here, we eliminate the need for a stringent PE condition by utilizing a novel set of weighted integral filter dynamics, while ensuring sufficient richness using a milder condition, called initial excitation (IE). Moreover, to validate the robustness guarantees towards unmodeled bounded disturbance, a detailed Lyapunov function based analysis is performed to establish the closed‐loop stability and convergence in the form of uniform ultimate boundedness (UUB). Furthermore, an experimental study using a unicycle wheeled mobile robot (WMR) is carried out as a proof‐of‐concept considering disturbance and disturbance‐free scenarios. [ABSTRACT FROM AUTHOR]

ABSTRACT In this study, we developed a four‐wheel‐driven autonomous mobile robot to transport heavy objects on narrow alleys and steep slopes. The robot, which weighs 450 kg, is driven by four 650 W hub motors to climb 20° steep slopes and travel through a narrow alley. Two 2D‐LiDARs attached to the front and rear of the robot body are used for highly accurate driving. A method based on the least squares approach for estimating the position and posture of the robot body is proposed by using the point cloud information of the side wall of the narrow alley. Field experiments on test courses demonstrated that the autonomous robot could transport heavy objects over steep slopes and travel in a narrow alley with high accuracy. [ABSTRACT FROM AUTHOR]

ABSTRACT In this article, an event‐triggered adaptive neural network controller based on threshold band is designed for a four wheels independently steered and four wheels independently driven (4WS4WD) mobile robot. The 4WS4WD mobile robot is attracting attention for its excellent motion performance such as manipulation versatility and posture flexibility. However, its control difficulty is increased due to its characteristics of being controlled by eight motors for steering and driving respectively. Also, since the robot itself has limited computing and communication resources, real‐time control cannot be guaranteed. To copy with that, the kinematic and dynamics models are first introduced for the 4WS4WD mobile robot. Second, an adaptive neural network controller with low‐frequency learning rate is utilized to control the mobile robot since there are unknown perturbations in the model. It can maintain system stability while handing unidentified model perturbations. The stability of the controller is demonstrated by Lyapunov stability analysis. An event‐triggered based on threshold band is suggested to lessen the amount of computation in the control process. Finally, the simulation outcomes further demonstrate how the suggested approach can greatly lessen the computational and communication cost while maintaining control performance. [ABSTRACT FROM AUTHOR]

Volatility in supply and demand caused by global disruptions such as wars or pandemics requires adaptable and changeable production systems. Since assembly accounts for significant production time and costs, the demand for changeable, line-less assembly systems is advancing. Controlling mobile robots in line-less assembly depends on understanding task executability. We propose the implementation of performance maps to evaluate the executability of assembly tasks within robot workspaces. Firstly, established performance metrics and typical assembly tasks are categorized to identify which metrics evaluate the executability of which type of tasks. The assembly tasks are grouped according to the type of movement (continuous or discrete), the required execution precision (high or low), and the amount of poses for execution (reachable or dexterous). The metrics are categorized according to their range (local or global), their physics (kinematic or dynamic), task reference (intrinsic or extrinsic), and scale (absolute or relative). Metrics are then matched to task types. This matching provides a systematic way to identify metrics to assess the executability of a task. Secondly, the performance map is presented. The performance map is a discretized representation of the distribution of chosen performance metrics for a specific robot. The current implementation is restricted to calculating the manipulability, dexterity, and condition number. Based on the input of a robot model and a task type, the metrics are calculated in distributed poses for a given resolution in the robot's workspace to form the performance map. The performance map is applied to exemplary tasks and robots. Previous approaches to workspace evaluation fail to consider the suitability of performance metrics to evaluate specific tasks, as different metrics are more or less relevant for different tasks. Consequently, the paper contributes by introducing performance maps and providing quantific metrics for comparing base placements of mobile robots according to the executability of specific assembly tasks. [ABSTRACT FROM AUTHOR]

With the development of Industry 4.0, collaborative mobile robots are becoming increasingly prevalent in industrial settings, raising important safety considerations in human–robot interaction environments. This study examines the safety issues in collaborative mobile robotics through a case study of a smart factory utilizing quadruped robots. This research aims to contribute to the development of safety management strategies by identifying potential risk factors and analyzing the differences in risk perception among stakeholders. A survey was conducted among 93 operators in the factory to identify the main risk factors, followed by a Delphi study with four groups of experts: robot operators, safety management experts, robot developers, and academic experts. The Kruskal–Wallis and Mann–Whitney U tests were used to analyze the statistical significance of differences in perception between the groups. The results showed that collision and deviation from the path were the most concerning risk factors. Significant differences were found in the perceptions of several hazards between expert groups, with academic experts rating most hazards highly while robot developers rated them relatively low. The findings highlight how background knowledge and experience influence risk perception in collaborative robotics. These varying perspectives should be considered when developing safety management strategies for mobile robots in industrial settings, suggesting the importance of multi-stakeholder collaboration and targeted educational programs. [ABSTRACT FROM AUTHOR]

The controlled actuation of hydraulic and pneumatic actuators has unveiled fresh and thrilling opportunities for designing mobile robots with adaptable structures. Previously reported rolling robots, which were powered by fluidic systems, often relied on complex principles, cumbersome pump and valve systems, and intricate control strategies, limiting their applicability in other fields. In this investigation, we employed a distinct category of functional fluid identified as Electrohydrodynamic (EHD) fluid, serving as the pivotal element within the ring-shaped actuator. A short stream of functional fluid is placed within a fluidic channel and is then actuated by applying a direct current voltage aiming at shifting the center of mass of the robot and finally pushed the actuator to roll. We designed a ring-shaped fluidic robot, manufactured it using digital machining methods, and evaluated the robot's characteristics. Furthermore, we developed static and dynamic models to analyze the oscillation and rolling motion of the ring-shaped robots using the Lagrange method. This study is anticipated to contribute to the expansion of current research on EHD flexible actuators, enabling the realization of complex robotic systems. [ABSTRACT FROM AUTHOR]

Introduction: Path planning algorithms are challenging to implement with mobile robots in orchards due to kinematic constraints and unstructured environments with narrow and irregularly distributed obstacles. Methods: To address these challenges and ensure operational safety, a local path planning method for orchard mowers is proposed in this study. This method accounts for the structural characteristics of the mowing operation route and utilizes a path-velocity decoupling method for local planning based on following the global reference operation route, which includes two innovations. First, a depth-first search method is used to quickly construct safe corridors and determine the detour direction, providing a convex space for the optimization algorithm. Second, we introduce piecewise jerk and curvature restriction into quadratic programming to ensure high-order continuity and curvature feasibility of the path, which reduce the difficulty of tracking control. We present a simulation and real-world evaluation of the proposed method. Results: The results of this approach implemented in an orchard environment show that in the detouring static obstacle scenario, compared with those of the dynamic lattice method and the improved hybrid A* algorithm, the average curvature of the trajectory of the proposed method is reduced by 2.45 and 3.11 cm –1, respectively; the square of the jerk is reduced by 124 and 436 m 2/ s 6, respectively; and the average lateral errors are reduced by 0.55 cm and 4.97 cm , respectively, which significantly improves the path smoothness and facilitates tracking control. To avoid dynamic obstacles while traversing the operation route, the acceleration is varied in the range of -0.21 to 0.09 m / s 2. In the orchard environment, using a search range of 40 m × 5 m and a resolution of 0.1 m , the proposed method has an average computation time of 9.6 ms. This is a significant improvement over the open space planning algorithm and reduces the average time by 12.4 ms compared to that of the dynamic lattice method, which is the same as that of the structured environment planning algorithm. Discussion: The results show that the proposed method achieves a 129% improvement in algorithmic efficiency when applied to solve the path planning problem of mower operations in an orchard environment and confirm the clear advantages of the proposed method. [ABSTRACT FROM AUTHOR]

Summary: The event‐triggered joint sensor fault estimation and mobile robot (MR) localization (MRL) problem (MRLP) subject to the potential fluctuations of the estimator gain are investigated. From the external sensor to the estimator, to decrease the consumption of sensor energy and cut down network bandwidth resources, an event‐triggered scheme is considered. For the sake of characterizing the phenomenon induced by the inaccurate calculation of estimator gain, an uncertainty with the bounded second moment is employed. The purpose of this study is theoretically to find a feasible and effective approach to the addressed joint estimation problem such that the estimation error (EE) covariance (EEC) meets the given performance index. First, a minimum upper bound (UB) of the EEC is derived. Subsequently, in terms of the proposed joint estimation approach and the corresponding results obtained, an algorithm to address the resilient joint sensor fault estimation and MRLP is summarized. At the end, the effectiveness of the proposed algorithm is validated through conducting a set of comparisive experiments. [ABSTRACT FROM AUTHOR]

Many animals exhibit agile mobility in obstructed environments due to their ability to tune their bodies to negotiate and manipulate obstacles and apertures. Most mobile robots are rigid structures and avoid obstacles where possible. In this work, we introduce a new framework named Haptic And Visual Environment Navigation (HAVEN) Architecture to combine vision and proprioception for a deformable mobile robot to be more agile in obstructed environments. The algorithms enable the robot to be autonomously (a) predictive by analysing visual feedback from the environment and preparing its body accordingly, (b) reactive by responding to proprioceptive feedback, and (c) active by manipulating obstacles and gap sizes using its deformable body. The robot was tested approaching differently sized apertures in obstructed environments ranging from greater than its shape to smaller than its narrowest possible size. The experiments involved multiple obstacles with different physical properties. The results show higher navigation success rates and an average 32% navigation time reduction when the robot actively manipulates obstacles using its shape-changing body. [ABSTRACT FROM AUTHOR]

This paper proposes a heuristic motion planning algorithm to enhance optimal path determination and tracking for a differential drive mobile robot within a global environment. The proposed method uses the A‐star search algorithm to discover the optimal path between the start and goal vertices. This algorithm employs precise distance values and heuristic distance values to compute and expand the vertex for identifying a suitable path. When the path is generated, the movement of the robot is regulated by an attractive potential field (APF) and a repulsive potential field (RPF). The APF assists the robot in following its designed path, while the RPF is employed to avoid obstacles. Subsequently, the APF plays a role in helping the robot return to its trajectory. In case of obstacles obstruct the path, the A‐star algorithm identifies a new alternative route starting from the current position to the goal, and the robot navigates along this path. During a series of scenario evaluation, the algorithm demonstrates the ability to present optimal solutions in complex environments. To validate the effectiveness of the system, the simulation is executed with the Webots R2021a software. Then, experimental verification for the designed robot demonstrates the system's high accuracy in tracking and obstacle avoidance on a specific map. [ABSTRACT FROM AUTHOR]

This paper presents an online topological mapping method on a quadcopter with fast-growing neural gas. Recently, perceiving the real world in 3D space has become increasingly important, and robotics is no exception. Quadcopters are the most common type of robot working in 3D space. The ability to perceive 3D space is even required in order to enable real-time autonomous control. Dense maps are simply unpractical, while sparse maps are not suitable due to a lack of appropriate information. Topological maps then offer a balance between computational cost and accuracy. One of the most well-known unsupervised learning methods for topological mapping is growing neural gas (GNG). Unfortunately, it is difficult to increase the learning speed due to the traditional iterative method. Consequently, we propose a new method for topological mapping, called simplified multi-scale batch-learning GNG, by applying a mini-batch strategy in the learning process. The proposed method has been implemented on a quadcopter for indoor mapping applications. In addition, the topological maps are also combined with the tracking data of the quadcopter to generate a new global map. The combination is simple yet robust, based on rotation and translation strategies. Thus, the quadcopter is able to run the algorithms in real-time and maintain its performance above 30 fps. [ABSTRACT FROM AUTHOR]

The mecanum wheel is an unconventional wheel composed of a hub and passive rollers attached to the wheel disk. Due to this feature, mobile robots equipped with mecanum wheels can move in any direction: forward, backward, left, and right sideways, diagonally at any angle, and rotate around their axis. For this reason, they are used when high levels of maneuverability are required. However, there is a problem referred to as wheel slippage, which is associated with the roller making contact with the surface, causing significant deviations in the vehicle's orientation during its trajectory, which is undesirable. In this work, we present the experimental results of a low-level control law by introducing a ramp-like feature in the reference signal to mitigate abrupt wheel responses and reduce slippage. We compare the effectiveness of the proposed approach with traditional techniques on different surfaces. [ABSTRACT FROM AUTHOR]

This paper presents a rapidly exploring random tree (RRT) algorithm with an effective post waypoint shift, which is suitable for the path planning of a wheeled mobile robot under kinematic constraints. In the growth of the exploring tree, the nearest node that satisfies the kinematic constraints is selected as the parent node. Once the distance between the new node and the target is within a certain threshold, the tree growth stops and a target connection based on minimum turning radius arc is proposed to generate an initial complete random path. The most significant difference from traditional RRT-based methods is that the proposed method optimizes the path based on Dubins curves through a post waypoint shift after a random path is generated, rather than through parent node selection and rewiring during the exploring tree growth. Then, it is proved that the method can obtain an optimal path in terms of the shortest length. The optimized path has good convergence and almost does not depend on the state of the initial random path. The comparative test results show that the proposed method has significant advantages over traditional RRT-based methods in terms of the sampling point number, the tree node number, and the path node number. Subsequently, an efficient method is further proposed to avoid unknown obstacles, which utilizes the original path information and thus effectively improves the new path planning efficiency. Simulations and real-world tests are carried out to demonstrate the effectiveness of this method. [ABSTRACT FROM AUTHOR]

With the continuous advancement of autonomous driving technology, an increasing number of high-definition (HD) maps have been generated and stored in geospatial databases. These HD maps can provide strong localization support for mobile robots equipped with light detection and ranging (LiDAR) sensors. However, the global localization of heterogeneous robots under complex environments remains challenging. Most of the existing point cloud global localization methods perform poorly due to the different perspective views of heterogeneous robots. Leveraging existing HD maps, this paper proposes a base-map-guided heterogeneous robots localization solution. A novel co-view context descriptor with rotational invariance is developed to represent the characteristics of heterogeneous point clouds in a unified manner. The pre-set base map is divided into virtual scans, each of which generates a candidate co-view context descriptor. These descriptors are assigned to robots before operations. By matching the query co-view context descriptors of a working robot with the assigned candidate descriptors, the coarse localization is achieved. Finally, the refined localization is done through point cloud registration. The proposed solution can be applied to both single-robot and multi-robot global localization scenarios, especially when communication is impaired. The heterogeneous datasets used for the experiments cover both indoor and outdoor scenarios, utilizing various scanning modes. The average rotation and translation errors are within 1° and 0.30 m, indicating the proposed solution can provide reliable localization support despite communication failures, even across heterogeneous robots. [ABSTRACT FROM AUTHOR]

This paper presents an improved Model Predictive Control (MPC) for path tracking of a nonholonomic mobile robot with a differential drive. Nonlinear dynamics and nonholonomic constraints make the optimisation problem of MPC for the robot challenging. Nonlinear dynamics of the robots are expressed by a Linear Parameter Varying (LPV), and a Recurrent Neural Network (RNN) solves the constrained optimisation problem, providing optimal velocities. Moreover, an interpolation-based approach has been introduced to augment the region of attraction. The algorithm ensures stability in the presence of bounded disturbances through the inclusion of free control moves in the control law. The controller efficiency has been evaluated in two scenarios in a hospital setting. The simulation results illustrate that the proposed method performs better than nonlinear MPC and standard LPV-based MPC in terms of computational cost, disturbance rejection, and region of attraction. [ABSTRACT FROM AUTHOR]

• A novel redundantly actuated series-parallel manipulator is proposed. • Unified recursive kinematics and statics models of the manipulator are established. • The theoretical model is validated by comprehensive numerical results. • Load/mass ratio of the tested manipulator in non-redundant actuation mode is 1.34. • The manipulator has higher load-carrying capacity in redundant actuation mode. Construction robots with mobile systems are an integral part of automatic building processes. However, with the vigorous development of prefabricated buildings, traditional manipulators have progressively failed to satisfy the requirements of handling heavy materials on construction sites. Thus, this paper proposes a redundantly actuated series-parallel manipulator (RAS-PM). To explore the feasibility of applying the manipulator in construction tasks, this paper establishes the unified recursive kinematics and statics of the manipulator and verifies the validity and reliability of the theoretical modeling method through comprehensive numerical results. The prototype of the manipulator is developed and installed on a bionic hexapod construction robot. The test results demonstrate that the manipulator exhibits an excellent load/mass ratio in non-redundant actuation mode, and its load-carrying capacity can be further enhanced with the optimized force/position hybrid actuation. In the on-site test, the robot can freely switch between modes to handle horizontal and vertical states of materials, such as curtain walls and large panels, thereby contributing to modern prefabricated construction. [ABSTRACT FROM AUTHOR]

This paper proposes a novel control method applied to the trajectory tracking of the wheeled mobile robot. This method can solve the tracking difficulty caused by the non‐holonomic constraint and the under‐actuated properties. First, according to the kinematic and dynamic tracking error models, the desired velocities for trajectory tracking purposes are obtained. Second, the control method, consisting of an enhanced backstepping controller with fewer gains and an optimization algorithm, is designed. The actual trajectory of the mobile robot is exactly converged and kept at the predefined reference trajectory by the operation of this method. Next, this method with globally uniformly asymptotically stability is theoretically analyzed. Finally, simulation comparisons and physical experiments are conducted in different scenarios. The tracking performance is evaluated by three metrics, namely convergence speed, tracking accuracy and robustness, thus verifying the effectiveness of the novel control method. [ABSTRACT FROM AUTHOR]

Path planning has a wide range of applications in many fields of engineering. One of the widely used approaches to solve this problem is sampling-based algorithms. However, a significant limitation of these algorithms is their inability to function effectively in the presence of unknown obstacles within the environment. A feasible way of addressing this issue is by using reinforcement learning to interact with the environment. In the context of path planning, safety means choosing safe and non-optimal paths over the optimal ones and having as few collisions as possible during both the training phase and the test phase. In this paper, a novel two-stage algorithm called R3T*-MOSafeRL(λ ) is proposed capable of path planning safely in an environment with unknown dynamic obstacles. In the first stage, the Roadmap Multi-Tree RRT* (R3T*) algorithm is presented which uses an initial map of the environment and an expert's information regarding the important regions of the environment to generate a roadmap. The roadmap acts as discretization of the continuous environment to practically deploy tabular reinforcement learning algorithms on top of it. In the next stage, the algorithm uses a novel eligibility trace-based multi-objective safe reinforcement learning (MOSafeRL(λ )) to perform safe path planning on the roadmap generated by R3T*. Moreover, a heatmap algorithm based on the roadmap and the weights learned by MOSafeRL(λ ) is presented which provides an interpretable method to gain information about the regions of the map with high activity of the unknown dynamic obstacles. Hence, the proposed algorithm provides a powerful method for path planning in unknown dynamic environments. Finally, to illustrate the efficiency of the proposed algorithm and to verify it, some case studies are considered and the computational results are compared and discussed. [ABSTRACT FROM AUTHOR]

The rapidly exploring random tree star (RRT*) algorithm is widely used to solve path planning problems. However, the RRT* algorithm and its variants fall short of achieving a balanced consideration of path quality and safety. To address this issue, an improved discretized artificial potential field-QRRT* (IDAPF-QRRT*) path planning strategy is introduced. Initially, the APF method is integrated into the Quick-RRT* (Q-RRT*) algorithm, utilizing the attraction of goal points and the repulsion of obstacles to optimize the tree expansion process, swiftly achieving superior initial solutions. Subsequently, a triangle inequality-based path reconnection mechanism is introduced to create and reconnect path points, optimize the path length, and accelerate the generation of sub-optimal paths. Finally, by refining the traditional APF method, a repulsive orthogonal vector field is obtained, achieving the orthogonalization between repulsive and attractive vector fields. This places key path points within the optimized vector field and adjusts their positions, thereby enhancing path safety. Compared to the Q-RRT* algorithm, the DPF-QRRT* algorithm achieves a 37.66% reduction in the time taken to achieve 1.05 times the optimal solution, and the IDAPF-QRRT* strategy nearly doubles generated path safety. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Environmental mapping serves as a crucial element in Simultaneous Localization and Mapping (SLAM) algorithms, playing a pivotal role in ensuring the accurate representation necessary for autonomous robot navigation guided by SLAM. Current SLAM systems predominantly rely on grid-based map representations, encountering challenges such as measurement discretization for cell fitting and grid map interpolation for online posture prediction. Splines present a promising alternative, capable of mitigating these issues while maintaining computational efficiency. This paper delves into the efficiency disparities between B-Spline surface mapping and discretized cell-based approaches, such as grid mapping, within indoor environments. B-Spline Online SLAM and FastSLAM, utilizing Rao-Blackwellized Particle Filter (RBPF), are employed to achieve range-based mapping of the unknown 2D environment. The system incorporates deep learning networks in the B-Spline curve estimation process to compute parameterizations and knot vectors. The research implementation utilizes the Intel Research Lab benchmark dataset to conduct a comprehensive qualitative and quantitative analysis of both approaches. The B-Spline surface approach demonstrates significantly superior performance, evidenced by low error metrics, including an average squared translational error of 0.0016 and an average squared rotational error of 1.137. Additionally, comparative analysis with Vision Benchmark Suite demonstrates robustness across different environments, highlighting the effectiveness of B-Spline SLAM for real-world applications. [ABSTRACT FROM AUTHOR]

Purpose: Franchising contributes significantly to national economies but is overlooked in supply chain literature. This study aims to contribute to the franchising and supply chain literature by examining how the digitisation of the franchising supply chain improves firm performance. Design/methodology/approach: A single longitudinal case study approach was selected to investigate how a leading coffee brand digitised its franchising supply chain. Resource constraints theory and agency theory provide the theoretical framework. Data collection included both qualitative and quantitative data. Over two years, chronological, supply chain and thematic analyses and interpretation uncovered important findings and developed four research propositions. Findings: Findings show that digitisation can impact performance in eight areas: Resource management, Resource constraints, Efficiency, Business-to-Business (B2B)/Business-To-Customer (B2C) links, Rapid expansion, Risk mitigation, Information asymmetries and Faster supply chain responses. Four digital technologies (advanced analytics, Internet of Things, Autonomous Mobile Robots and B2B e-shop) impacted three franchisor functions (Machine maintenance, Inventory management, Franchisee and end-customer relations). The study develops four research propositions on how digitisation impacts performance in terms of (1) resource monitoring and control, (2) learning and knowledge creation, (3) coordination and collaboration and (4) competition. Originality/value: Franchising supply chains have been overlooked in the literature; this study provides insights into using resource constraints theory and agency theory complementarily to explain supply chain digitisation and provides actionable practical implications for selecting, implementing and continuously improving Industry 4.0 technologies in franchising supply chains. [ABSTRACT FROM AUTHOR]

In this paper, a double‐loop control method is proposed to improve the trajectory tracking performance of a wheeled mobile robot (WMR) with slippage properties and external disturbances. Considering the strong robustness and the ability to enable the system to converge in a short time, the predefined‐time sliding mode control (PTSMC) strategy is applied to the design of a double‐loop controller. Furthermore, a nonlinear disturbance observer (NDO) is adopted to comply with the feedforward compensation of the external disturbances. In turn, the controller only needs to deal with the internal disturbance of the system under the premise of using the NDO, thereby reducing the burden of the sliding mode controller. Based on Lyapunov methods, the stability of the double‐loop controller and the NDO is analyzed. Finally, the feasibility of the double‐loop control strategy is proven by simulations of the WMR operating along circular, sin‐shaped, and eight‐shaped roads. [ABSTRACT FROM AUTHOR]

A mobile agent, which is an autonomous device navigating in a graph, has to explore a given graph by visiting all of its nodes. We adopt the (arguably) weakest possible model of such a device: a deterministic memoryless automaton (DMA), i.e., a deterministic automaton with a single state. As expected, such a weak machine is incapable of exploring many graphs without marking nodes. Hence we allow the agent to use identical movable pebbles that can be dropped on nodes or picked from them. It turns out that this marking capability significantly enhances the exploration power of the agent. Our goal is to study how the availability of pebbles impacts the class of graphs that a DMA can explore. We first concentrate on finite graphs and show that any finite tree can be explored by a DMA without pebbles but there exist (small) finite graphs that cannot be explored by a DMA without pebbles. Then we turn attention to infinite graphs and fully characterize the class of infinite trees that can be explored by a DMA without pebbles. We also define a large class of infinite trees that can be explored by a DMA with finitely many pebbles. It turns out that many of these trees cannot be explored by a DMA without pebbles. On the other hand, we show a large class of infinite trees that cannot be explored by a DMA with finitely many pebbles. Thus, availability of pebbles yields a strict hierarchy of difficulty of exploration of infinite graphs by a DMA, and this hierarchy is strict even for the class of infinite trees: some trees can be explored without pebbles, some trees can be explored with finitely many pebbles but not without pebbles, and some trees require infinitely many pebbles. Finally, we consider exploration by a DMA with infinitely many pebbles. It turns out that all infinite trees can be explored by a DMA with infinitely many pebbles. By contrast, we construct infinite graphs that cannot be explored by any DMA, even with infinitely many pebbles. [ABSTRACT FROM AUTHOR]

Camera-based relative pose estimation (RPE) localizes a mobile robot given a view at the current position and an image at a reference location. Matching the landmarks between views is critical to localization quality. Common challenges are appearance changes, for example due to differing illumination. Indirect RPE methods extract high-level features that provide invariance against appearance changes but neglect the remaining image data. This can lead to poor pose estimates in scenes with little detail. Direct RPE methods mitigate this issue by operating on the pixel level with only moderate preprocessing, but invariances have to be achieved by different means. We propose to attain illumination invariance for the direct RPE algorithm MinWarping by integrating it with a convolutional neural network for image preprocessing, creating a hybrid architecture. We optimize network parameters using a metric on RPE quality, backpropagating through MinWarping and the network. We focus on planar movement, panoramic images, and indoor scenes with varying illumination conditions; a novel dataset for this setup is recorded and used for analysis. Our method compares favourably against the previous best preprocessing method for MinWarping, edge filtering, and against a modern deep-learning-based indirect RPE pipeline. Analysis of the trained hybrid architecture indicates that neglecting landmarks in a direct RPE framework can improve estimation quality in scenes with occlusion and few details. [ABSTRACT FROM AUTHOR]

In this study, we consider the extended optimal control problem and search for a control function in the class of feasible functions for a real control object. Unlike the classical optimal control problem, the control function should depend on the state, not time. Therefore, the control synthesis problem for the initial-state domain should be solved, instead of the optimal control problem with one initial state. Alternatively, an optimal trajectory motion stabilisation system may be constructed. Both approaches—control and trajectory motion stabilisation system syntheses—cannot be applied to real-time control, as the task is too complex. The minimum threshold of quality criteria is searched for in the space of mathematical expression codes. Among other problems, the search space is difficult to define and the gradient is hard to determine. Therefore, the advanced control object model is used to obtain a feasible control function. The advanced model is firstly obtained before solving the optimal control problem and it already includes a trajectory motion stabilisation system; in particular, this stabilisation system is synthesised in advance at the control system design stage. When the optimal control problem appears, it is solved in real time in the classical statement, and a control function is searched for as a function of time. The advanced control object model also uses the reference model to generate the optimal trajectory. The search for the optimal control function is performed in real time and considers the synthesised stabilisation system of motion along a determined trajectory. Machine learning control via symbolic regression, namely, the network operator method, is used to directly solve the control synthesis problem. An example solution of the optimal control problem, with an advanced model moving in the environment with obstacles for a group of two mobile robots, is presented. The obtained solution is a control function for a reference model that generates a trajectory from a class of trajectories stabilised with the object's control system. [ABSTRACT FROM AUTHOR]

Although grid maps help mobile robots navigate in indoor environments, some lack semantic information that would allow the robot to perform advanced autonomous tasks. In this paper, a semantic map production system is proposed to facilitate indoor mobile robot navigation tasks. The developed system is based on the employment of LiDAR technology and a vision-based system to obtain a semantic map with rich information, and it has been validated using the robot operating system (ROS) and you only look once (YOLO) v3 object detection model in simulation experiments conducted in indoor environments, adopting low-cost, -size, and -memory computers for increased accessibility. The obtained results are efficient in terms of object recognition accuracy, object localization error, and semantic map production precision, with an average map construction accuracy of 78.86%. [ABSTRACT FROM AUTHOR]