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Robotics and automation are the key industries supporting Thailand's strategic initiatives toward Thailand 4.0. This paper presents the development of an industry roadmap guiding the future research and development of related technologies in Thailand. The roadmap project was recently conducted during 2021–2022. The strategic targets for the future development of robotics and automation in Thailand were set to increase productivity and reduce adoption cost for the short term (2022–2023); create market opportunities for AI and industrial IoT technologies with data platform for the medium term (2024–2026); and prepare the Industry 5.0 system supporting emerging applications of digital twins and metaverse for the long term (2027–2030). [ABSTRACT FROM AUTHOR]

Tactile sensors play a pivotal role in enhancing robotic gripping systems across various industries, allowing robots to interact with their environment with precision and adaptability. This study introduces a novel soft barometric tactile sensor designed to capture pressure data beneath the contact pad within an elastomer layer. By employing an iterative fitting process for a parameterized pressure distribution, the sensor eliminates the need for extensive calibration or complex training procedures. A novel metric was introduced to evaluate the accuracy of pressure reconstruction in tactile sensors. This was complemented by quantitative investigations. Moreover, the precision of multiple contact detection and the capabilities of force estimation were examined. Finally, the practical applications of the sensor were demonstrated through in-hand measurement of object orientation. The sensor exhibits promising results, accurately reconstructing pressure distributions for a wide range of indenters, reliably discerning two contacts when their distance exceeds 5 mm and estimating forces with a Mean Absolute Error (MAE) of 1.83 N. Additionally, the in-hand reorientation achieved a Mean Absolute Rotation Error (MARE) of 6.81°, demonstrating the sensor’s potential for real-world applications.

The Robot Operating System (ROS) has significantly gained popularity among robotic engineers and researchers over the past five years, primarily due to its powerful infrastructure for node communication, which enables developers to build modular and large robotic applications. However, ROS presents a steep learning curve and lacks the intuitive usability of vendor-specific robotic Graphical User Interfaces (GUIs). Moreover, its modular and distributed nature complicates the control and monitoring of extensive systems, even for advanced users. To address these challenges, this paper introduces a novel, highly adaptable and reconfigurable web-based GUI for intuitively controlling, monitoring, and configuring complex ROS-based robotic systems. The GUI leverages ROSBridge and roslibjs to ensure seamless communication with ROS systems via topics and services. Special attention has been paid to enable the easy reconfiguration of the GUI and to minimize the modifications required to integrate it with new robotic systems. Thus, it has been designed as a versatile platform, which allows for the selective incorporation of modular features to accommodate diverse robotic systems and applications. A predefined template has been defined to ensure the compatibility of the developed features with the GUI platform. Additionally, an initial set of commonly used features in robotic applications is presented. To demonstrate its reconfigurability, the GUI was customized and tested for four industrial use cases, receiving positive feedback. The project’s repository has been made publicly available to support the robotics community and lower the entry barrier for ROS in industrial applications.

This paper presents a methodology and generalized motion framework for quantifying passive biomechanical stability and Range of Motion of human cadaveric specimens, using a position-controlled industrial robot and a wrist-mounted force/torque sensor. Many biomechanical studies on diarthrodial joints using human cadaveric specimens are published in the literature, using various test protocols and machines to apply the loading conditions. In these studies, laxity or mobility of the joints are quantified by measuring the magnitude of translations and rotations with respect to force and torque. The protocols and anatomical motions of the specimens are usually described high-level, textually, and from a medical perspective to a broad audience. The present paper aims to describe, from a technical perspective to a robotics audience, our method to perform biomechanical studies and how existing protocols can be replicated through parameterization using the existing textual descriptions. To accomplish this, we propose a generalized task space motion framework for performing biomechanical studies on diarthrodial joints. The generalization is made by defining the robot Tool Center Point at the cadaveric joint rotation center and aligning the specimen so the anatomical motions can be modeled in world frame or tool frame. The framework was successfully evaluated in a technical pilot study on the shoulder, using one cadaveric shoulder specimen and an established protocol from the literature. The specimen was tested in the intact state and in an injury state, with increased passive instability observed for the injury state compared to intact state.

Traditional manufacturing industries are currently immersed in an automation process, integrating new techniques and tools, driven by the demands from producers to improve the manufacturing process as well as the working conditions of employees. For the footwear industry, bonding is a key operation in the manufacturing process where the outsole is assembled onto the lasted shoe. However, in this operation, workers are often subjected to hazardous substances (i.e., organic solvents) and perform repetitive tasks with limited added value. Against this background, this paper describes the results of a research project, whose aim was to obtain the maximum benefit from different technologies analyzed, such as collaborative robotics, artificial vision and multirobot control, for the manipulation of flexible/deformable objects. The main result of this project is a robotic workcell for shoe bonding that has been introduced in the production line to fully automate the operation. This workcell integrates three collaborative robots, one for (hot melt) adhesive application and another two, with two-finger electric grippers, to carry out the bonding synchronously. Different vision systems have also been embedded to conduct the various processes involved. The entire operation is controlled and coordinated through ROS (Robot Operating System). The key findings of this research showcase the automation of a process traditionally undertaken by humans. In this novel approach, two robots collaborate to manipulate flexible objects, liberating the operator from engaging in repetitive, non-value-added tasks and the handling of hazardous substances.

Industry 4.0 (I4.0) has brought transformative changes in the manufacturing sector. This paper aims to provide a comprehensive analysis of the applications of I4.0 technologies in major work functions of small and medium‐sized enterprises (SMEs). Specifically, this review focuses on the suitability of I4.0 technologies in areas such as New Product Development, Supply Chain Management, Internal Logistics Management, Production Planning Execution and Control, Quality Management, and Maintenance Management. This study employs a systematic literature review (SLR) methodology to comprehensively analyze relevant sources to present valuable perspectives and practical suggestions customized to the requirements of different essential work functions within manufacturing SMEs. The findings of SLR indicate that Big Data Analytics (BDA), Robotics, and Automation are perceived as highly sustainable, on the other hand, blockchain and cloud technology are viewed as having lower sustainability from SMEs' point of view. The findings presented in this article have several theoretical and practical implications including technology selection and integration, and considering sustainability and ethics. This will allow SMEs to seamlessly integrate distinct I4.0 technologies along three dimensions: vertical, horizontal, and end‐to‐end digital integration. This article intends to provide an unbiased assessment to ascertain the landscape occupied by I4.0 in the context of SMEs. This article highlights the connection and synergy between I4.0 and SMEs as well as the pertinence of how advanced technologies of I4.0 can influence the business processes in manufacturing SMEs. [ABSTRACT FROM AUTHOR]

Research in advanced construction and autonomous earthworks begins to explore the shaping of aggregates for construction, military, and environmental purposes. However, while current work in autonomous earthworks focuses on grading and pushing aggregates, there is limited research on moving aggregates to form specific shapes on a surface. This action can aid in gathering aggregates for on- site construction of re- configurable formations, performing architectural tasks, or piling material reservoirs for concrete production. To support aggregate-forming, an autonomous agent is required to move non-labeled aggregates from multiple locations to numerous proximate target points in a predefined desired shape. In this process, the agent needs to push the aggregates, handle material spills, and update both the material location and the outcome formation. The paper presents a planner for autonomous aggregate shaping to support this task. The path generation is first demonstrated in a simulation environment and validated in an in- lab experimental setup, showing over 90% success rate. The results show that employing the planner can assist in advancing autonomous earthworks toward on- site aggregate-forming.

Compared with rigid robots, soft robots have the advantages of strong environmental adaptability, good human-robot interaction, and simple control systems; therefore, soft actuators have been widely studied in recent years. Based on DLP 3D printing technology, this study makes three kinds of pneumatic soft actuators: the soft gripper with the auxetic structure, the Elongation Bellow Actuator (EBA) and the Omnidirectional Bending Bellow Actuator (OBBA). Through experimental testing and finite element simulation, it was found that these actuators have the characteristics of fast response, high stability and large driving force. In this paper, an out-pipe crawling soft robot and soft robotic arm using these three soft actuators are designed. The pipe robot can realize omnidirectional turning and adapt to different shapes and sizes of pipes, with a movement speed of 2.85 mm/s (0.016 body length/s) on the 44 mm diameter round cross-section pipe and 2.67 mm/s (0.015 body length/s) on the 30 mm $\mathrm {\times }30$ mm square cross-section pipe. At the same time, an innovative motion mode is designed for the pipe robot in an underwater environment, which provides new ideas for underwater pipe soft robots. And the soft robotic arm can achieve omnidirectional bending and a specific range of grasping work, and compared with the rigid body robot arm, it is small in size, low in mass and has a higher degree of freedom. Finally, by combining the robotic arm and pipe robot, this paper proposes a pipe soft robot collaboration system and simulates its function in industrial pipeline systems, which has good application prospects.

Mobile and aerial robots offer many potential applications, including warehouse logistics, surveillance, cinematography, and search and rescue. However, most such robots are task-specific and generally lack the versatility to tackle multiple scenarios, terrains, and unstructured, dynamic environments. This paper presents the Omnirotor platform, a versatile, multi-modal, coaxial, tilt-rotor, all-terrain vehicle that combines an Unmanned Aerial Vehicle (UAV) and an Unmanned Ground Vehicle (UGV) into a hybrid, all-terrain vehicle. The Omnirotor has two locomotion modes of operation (aerial and ground vehicle) and five operational configurations, as it can fly both in the Normal and Inverted configurations and drive on the ground in the Normal and Inverted configurations. It can also recover from any non-operational state to its Normal, upside-down configuration. Moreover, in addition to the locomotion modes, the continuous omnidirectional thrust vectoring enables the Omnirotor platform to perform complex manipulation of objects. This work introduces the concept and discusses in detail the design, development, and experimental validation of the Omnirotor platform. In particular, it discusses the modeling and control schemes required by the different operation modes and configurations. It experimentally validates the platform’s capabilities with experiments focusing on traversing challenging environments and unstructured, uneven terrains (e.g., a public park). Finally, the platform’s ground, pushing-based manipulation capabilities are demonstrated through the execution of a puzzle-solving experiment where the solved puzzle serves as a landing platform for the all-terrain vehicle. The versatility of the Omnirotor offers exciting prospects for use in challenging search-and-rescue scenarios, surveillance, and aerial and ground manipulation applications.

Recent research and developments in cloud robotics (CR) require appropriate knowledge representation to ensure interoperable data, information, and knowledge sharing within cloud infrastructures. As an important branch of the Internet of Things (IoT), these demands to advance it forward motivates academic and industrial sectors to invest on it. The IEEE 'Ontologies for Robotics and Automation' Working Group (ORA WG) has been developing standard ontologies for different robotic domains, including industrial and autonomous robots. The use of such robotic standards has the potential to benefit the Cloud Robotic Community (CRC) as well, supporting the provision of ubiquitous intelligent services by the CR-based systems. This paper explores this potential by developing an ontological approach for effective information sharing in cloud robotics scenarios. It presents an extension to the existing ontological standards to cater for the CR domain. The use of the new ontological elements is illustrated through its use in a couple of CR case studies. To the best of our knowledge, this is the first work ever that implements an ontology comprising concepts and axioms applicable to the CR domain. [ABSTRACT FROM AUTHOR]

Production systems using robotic manipulators have become common in the last few decades, and the trend is towards fenceless cells that save from space. Thus, the safety and flexibility of these systems have become more critical. The safety systems are based on either sensor data or camera images. Although the flexibility of the camera-based systems is better, conventional image processing methods are sensitive to the working environment. Artificial intelligence may be a powerful tool for them to adapt to change requirements quickly and improve accuracy and stability. In this study, a low-cost 2-D camera-based safety system was designed and installed in an experimental fenceless robotic work cell. The system controller was coupled with three alternative deep learning (ResNet-152, AlexNet, SqueezeNet) and three machine learning modules (support vector machine, random forest and decision tree). These modules were trained using photo images of ten distinct foreign objects penetrating the alarm zone. To include the ever-changing conditions of the industrial environment, disruptive effects including camera vibrations, shadows, reflections, illuminance variations etc. are included by using multiple images up to 550 for each class. Using the restricted data used for training and testing the six systems, the SqueezeNet deep learning model gave the best accuracy of 95% without any over-fitting. Despite this, machine learning-based models have been found to have 100 times faster prediction time than deep learning-based ones. Thus, the safety system can be adapted quickly to any possible changes and noise that may arise from working conditions is prevented, and time losses that may occur in industrial production may be prevented.

Limited flight distance and time is a common problem for multicopters. We propose a method for finding the optimal speed and sideslip angle of a multicopter flying a given path to achieve either the longest flight distance or time. Since flight speed and sideslip are often free variables in multicopter path planning, they can be changed without changing the mission. The proposed method is based on a novel multivariable extremum seeking controller with adaptive step size, which is inspired by recent work from the machine learning community on stochastic optimization. Our method (a) does not require a power consumption model of the vehicle, (b) is computationally efficient and runs on low-cost embedded computers in real-time, and (c) converges faster than the standard extremum seeking controller with constant step size. We prove the stability of this approach and validate it through outdoor experiments. The method is shown to converge with different payloads and in the presence of wind. Compared to flying at the maximum achievable speed in the experiments with a uniformly selected random sideslip angle, flying at the optimal range speed and sideslip on average increases the flight range by 14.3% without payload and 19.4% with a box payload. In addition, compared to hovering, flying at the optimal endurance speed and sideslip increases the flight time by 7.5% without payload and 14.4% with a box payload. A video can be found at https://youtu.be/aLds8LVfogk

Although industrial robotic arms are equipped with external cables to supply electricity, gases or other materials, cable path design is a difficult and demanding task. Herein, an efficient optimization method is proposed for automating cable path design under the assumption that the robot motion path is known. The contribution of this study was to reduce the considerable computation time required for the optimization, which was a concern in our previous work. The previous method represented candidates for cable paths as a set of parameter vectors (PVs) that included cable length and guide configurations, and then selected the optimal PV that satisfies stress constraints and provided the shortest cable path. The proposed method extracted critical poses, i.e., several static robot poses that are prone to applying stress to the cable, from the joint angle time series of the motion path, and then performed attachment and motion tests. The cable geometry for the static critical poses was simulated in the attachment test, while the geometry for dynamic robot motion was simulated in the motion test in an ascending order of the cable length among the PV candidates. Experimental results showed that the computation time for cable path optimization could be significantly reduced.

Given the prospect of low birth rate and aging population among developed countries, which further resulted in the shortage of workforces, service robots have been gradually applied to real-life from past academic research. This paper introduces the development of a service robot that is designed for food service in fast-food restaurants with the innovative improvement of mapping, localization, and navigation. Moreover, this research took the initiative of integrating 3D point cloud map and 2D occupancy grid map (OGM) in order to build a PC-OGM. In another word, using the sensory fusion method allows the service robot to adapt to a more complicated environment as well as enhance its positioning. In terms of its navigation function, the adaptive motion controller is refined so the service robot could navigate through narrow aisles smoothly. Finally, friendly contact-free food service robots were evaluated at fast-food restaurants in order to gain feedback from diners and waiters. Their feedback was broken down into 3 categories, availability, reliability, and satisfaction, for further analysis. As COVID-19 assails the world, we also look into future possibilities of food service robot deployment among restaurants to keep food and surface free from the virus.

Construction site preparation tasks rely on experienced operators and heavy machinery for clearing debris, earthmoving, leveling, and soil stabilization. These actions require complex collaboration between human teams to survey the site, estimate the material condition, and guide the operators accordingly. In recent years there has been a critical labor shortage due to increasing demands in construction. Integrating autonomous systems can mitigate this gap by replacing traditional methods with robotic solutions. However, while ideal conditions for automatic systems are static and highly controlled, construction sites are dynamic and unstructured environments. The ability of autonomous systems to overcome these conditions during outdoor construction site preparation tasks relies on their capacity to map the material on-site and continuously perform localization. This study suggests a solution to these problems by collaborating between an Unmanned Aerial Vehicle (UAV) and an Unmanned Ground Vehicle (UGV). In this method, the UAV produces a material map and monitors the UGV’s location relative to known static landmarks. These measurements are then sent to the ground vehicle and are added to the onboard sensors using the Extended Kalman Filter (EKF) approach. Thus, the UAV enhances the operation of the UGV by providing an accurate localization and mapping from the air and allowing it to perform a site-preparation task beyond mere sensing. This approach is examined with simulation and validated by outdoor experiments. Additionally, this method is integrated within Shepherd, a custom-developed plugin for computer-aided design applications.

Modern electrical grippers have lower life-cycle costs compared to pneumatic ones. Furthermore, they provide force control, making it possible to grasp objects with different fragility using a single device. At the same time, electrical grippers have a higher end-effector weight, installed on the robot's flange and lower closing speed, preventing them from replacing pneumatic solutions in high dynamic Pick and Place applications. This research faces both issues by synthesizing a novel gripper mechanism based on a Torque Distribution Gearbox, which makes it possible to relocate the electric motors to the static frame of a delta robot. The proposed gripper not only has a lower mass and a higher closing speed than competitive electric solutions, but it also provides unlimited rotation around the vertical axis. The performance of the gripper was tested in experimental studies, which showed that a created aluminum prototype provides a precise force control in the range from 3 N to 48 N with an accuracy not worse than 1.27 N. Moreover, its finger's speed is 3.1–56 times higher than market available electrical grippers, which makes it comparable by this parameter with pneumatic solutions used in high dynamic Pick and Place applications. [ABSTRACT FROM AUTHOR]

Typical industrial workcells are composed of a plenitude of devices from various manufacturers, which rely on their own specific control interfaces. To reduce setup and reconfiguration times, a hardware-agnostic Plug & Produce system is required. In this paper, we present a system architecture that uses generic and semantically augmented OPC UA skills for robots, tools, and other system components. Standardized skill interfaces and parameters facilitate flexible component interchange and automatic parametrization with a focus on reusability of skills across different platforms and domains. The hierarchical composition of such skills allows for additional abstraction through the grouping of functionalities. Through the extension of OPC UA discovery services, available skills are dynamically detected whenever a manufacturing system's component is updated. The introduced Plug & Produce system is evaluated in multiple industrial workcells composed of robots, tool changer, electric parallel gripper, and vacuum gripper-all controlled via the proposed OPC UA skill interface. The evaluation of our system architecture demonstrates the applicability of the Plug & Produce concept in the domain of robot-based industrial assembly. Although it is necessary to adapt existing hardware to comply with the semantic skill concept, the initial one-time effort yields reoccurring efficiency gains during system reconfiguration. In particular, small lot production benefits from reduced changeover times.

Corona Virus Disease 2019 (COVID-19) pandemic has become a global challenge faced by people all over the world. Social distancing has been proved to be an effective practice to reduce the spread of COVID-19. Against this backdrop, we propose that the surveillance robots can not only monitor but also promote social distancing. Robots can be flexibly deployed and they can take precautionary actions to remind people of practicing social distancing. In this paper, we introduce a fully autonomous surveillance robot based on a quadruped platform that can promote social distancing in complex urban environments. Specifically, to achieve autonomy, we mount multiple cameras and a three dimensional light detection and ranging sensor (3D LiDAR) on the legged robot. The robot then uses an onboard real-time social distancing detection system to track nearby pedestrian groups. Next, the robot uses a crowd-aware navigation algorithm to move freely in highly dynamic scenarios. The robot finally uses a crowd-aware routing algorithm to effectively promote social distancing by using human-friendly verbal cues to send suggestions to over-crowded pedestrians. We demonstrate and validate that our robot can be operated autonomously by conducting several experiments in various urban scenarios.

Owing to human labor shortages, the automation of labor-intensive manual waste-sorting is needed. The goal of automating waste-sorting is to replace the human role of robust detection and agile manipulation of waste items with robots. To achieve this, we propose three methods. First, we provide a combined manipulation method using graspless push-and-drop and pick-and-release manipulation. Second, we provide a robotic system that can automatically collect object images to quickly train a deep neural–network model. Third, we provide a method to mitigate the differences in the appearance of target objects from two scenes: one for dataset collection and the other for waste sorting in a recycling factory. If differences exist, the performance of a trained waste detector may decrease. We address differences in illumination and background by applying object scaling, histogram matching with histogram equalization, and background synthesis to the source target-object images. Via experiments in an indoor experimental workplace for waste-sorting, we confirm that the proposed methods enable quick collection of the training image sets for three classes of waste items (i.e., aluminum can, glass bottle, and plastic bottle) and detection with higher performance than the methods that do not consider the differences. We also confirm that the proposed method enables the robot quickly manipulate the objects.

This paper addresses the problem of target-directed exploration (TDE) in initially unknown and large-scale indoor environments. In such scenarios, the inference on an unknown space can improve the search performance under the assumption that the context of a particular space (i.e., the functional category of the space) is correlated with the existence of a target. The space inference is promising in that there is a strong statistical correlation between the semantic categories of indoor spaces and their adjacency because the spaces are designed to reflect universal human preferences. In this point of view, we propose a novel TDE scheme leveraging the semantic-spatial relations of an indoor floorplan dataset. Whereas existing works dealing with the data-driven space inferences consider only the one-to-one relation statistics of the spaces or utilize heuristic counting-based matching algorithms without building a trainable latent model, we propose the pattern cognitive Multivariate Bernoulli Distribution-based Graphical Space Inference Model (MBD-GSIM). MBD-GSIM efficiently captures the core contexts of the discrete semantic-spatial relations to predict an unknown space by using the latent Multivariate Bernoulli Distribution model. We also suggest utilizing the MBD-GSIM in a cost-utility based frontier exploration scheme for TDE problems. The proposed scheme is constructed in the Robot Operating System (ROS); its efficiency is investigated in the Gazebo simulation environment.

Code-level optimizations, which are low-level optimization techniques used in the implementation of algorithms, have generally been considered as tangential and often do not appear in published pseudo-code of Reinforcement Learning (RL) algorithms. However, recent studies suggest these optimizations to be critical to the performance of algorithms such as Proximal Policy Optimization (PPO). In this paper, we investigate the effect of one such optimization known as “early stopping” implemented for PPO in the popular openai/spinningup library but not in openai/baselines. This optimization technique, which we refer to as KLE-Stop, can stop the policy update within an epoch if the mean Kullback-Leibler (KL) Divergence between the target policy and current policy becomes too high. More specifically, we conduct experiments to examine the empirical importance of KLE-Stop and its conservative variant KLE-Rollback when they are used in conjunction with other common code-level optimizations. The main findings of our experiments are 1) the performance of PPO is sensitive to the number of update iterations per epoch ( $K$ ), 2) Early stopping optimizations (KLE-Stop and KLE-Rollback) mitigate such sensitivity by dynamically adjusting the actual number of update iterations within an epoch, 3) Early stopping optimizations could serve as a convenient alternative to tuning on $K$ .

We propose a novel iterative method for optimally placing and orienting multiple cameras in a 3D scene. Sample applications include improving the accuracy of 3D reconstruction, maximizing the covered area for surveillance, or improving the coverage in multi-viewpoint pedestrian tracking. Our algorithm is based on a block-coordinate ascent combined with a surrogate function and an exclusion area technique. This allows to flexibly handle difficult objective functions that are often expensive and quantized or non-differentiable. The solver is globally convergent and easily parallelizable. We show how to accelerate the optimization by exploiting special properties of the objective function, such as symmetry. Additionally, we discuss the trade-off between non-optimal stationary points and the cost reduction when optimizing the viewpoints consecutively. [ABSTRACT FROM AUTHOR]

This paper presents the Flying Lidar (Flydar) aerial robot for simultaneous localization and mapping (SLAM). The Flydar integrates a single laser and capitalizes on the rotating dynamics of a nature-inspired aerial robot for omnidirectional scanning. This paper presents a 2.5D SLAM approach using the Flydar and does not assume that the Flydar’s hovering plane, and thus the laser scan, is parallel to the ground plane, which is a key assumption in our previous work. A quaternion-based filter performs active correction of the Flydar’s laser scan due to its transient attitude. A dual-accelerometer approach was incorporated to estimate the Flydar’s scan rate during high angular rates flight beyond the gyroscope dynamic range. The proposed attitude filter output was experimentally evaluated statically on a bench-top setup and dynamically in flight, with an rms inclination estimate error of up to 1.1° and 0.38° respectively. The attitude-corrected lidar scan was used to estimate the robot pose for 2.5D SLAM. The 2.5D SLAM was experimentally validated on the Flydar and demonstrated to be superior to pure 2D SLAM in loop closure, position estimate drift, and rms error. Significantly, the 2.5D SLAM using the Flydar reports a low rms Euclidean error of up to 0.083 m, which is a 32.0% error reduction compared with our previous work, which uses 2D SLAM. [ABSTRACT FROM AUTHOR]

Industrial robots are increasingly used to perform tasks that require an interaction with the surrounding environment (e.g., assembly tasks). Such environments are usually (partially) unknown to the robot (in terms of dynamic characteristics), demanding the implemented controllers to suitably react to the established interaction. Standard controllers require force/torque measurements to close the loop, making it, if possible, to adapt the robot behavior to the specific environment. However, most of the industrial manipulators do not have embedded force/torque sensor(s), which entails additional effort in terms of costs and implementation for their integration in the robotic setup. To extend the use of sensorless compliant controllers to force control, a robot–environment interaction dynamics model-based methodology is presented in this article. Relying on the sensorless Cartesian impedance control, an extended Kalman filter (EKF) is proposed to estimate the stiffness of an interaction environment. Exploiting the provided estimation, the robot–environment coupled dynamic modeling is used to design an optimal LQR interaction controller to close the force loop. The control gains can be analytically computed by solving the related Riccati equation, as such gains are a function of the impedance control and environment parameters. In addition, the interaction force can be estimated to close the force loop, making the sensorless robot able to perform the target interaction task. The described approach has been validated with experiments by analyzing two scenarios: a probing task and a closing of a plastic (i.e., compliant) box with a snap-fit closure mechanism. The performance of the proposed control framework has been evaluated, highlighting the capabilities of the EKF and the optimal LQR interaction controller. Finally, the proposed control schema is enhanced by the adaptation of the EKF for the estimation of the external wrench. Two additional experiments are provided to show the improvements on the control schema (a polishing-like task and an assembly task). The Franka EMIKA panda robot has been used as the reference robotic platform for experimental validation. [ABSTRACT FROM AUTHOR]

Visual perception and modelling are essential tasks in many vision-dependent robotic tasks. This work presents a robotic vision system to perform fruit recognition, modelling, and environment modelling for autonomous apple harvesting. The fruit recognition applies a deep-learning model Dasnet to perform detection and segmentation on fruits, and segmentation on branches. Fruit modelling localises the centre and computes the grasp pose of each fruit based on Hough Transform. Environment modelling adopts Octrees to represent the occupied space within the working environment of the robot. The robot control computes the path and guide manipulator to pick the fruits based on the computed 3D model of the crop. The developed method is tested in both laboratory and orchard environments. Test results show that fruit recognition and modelling algorithm can accurately localise the fruits and compute the grasp pose in various situations. The Dasnet achieves 0.871 on F1 score of the fruit detection. Fruit modelling achieves 0.955 and 0.923 on the accuracy of the fruit centre estimation and grasp orientation, respectively. To illustrate the efficiency of the vision system in autonomous harvesting, a robotic harvesting experiment by using industry robotic arm in a controlled environment is conducted. Experimental results show that the proposed visual perception and modelling can efficiently guide the robotic arm to perform the detachment and success rate of harvesting is improved compared to the method which does not compute the grasp pose of fruits.

This paper presents a LiDAR-equipped unmanned aerial vehicle (UAV) performing semi-autonomous wind-turbine blade inspection which includes traversing to the blade tip and back, while keeping constant relative distance and heading to the blade plane. Plane detection is performed applying the RANSAC method on a subset of the gathered pointcloud. Utilizing the relative distance to the inferred plane as well as its normal vector, the UAV is able to maintain a constant distance and heading towards the plane while moving in parallel with it. The proposed procedure performs successful wind-turbine blade inspections with minimal operator involvement. Inspection results include high-resolution blade images as well as a 3D model of the wind-turbine structure. Finally, to show the feasibility of this approach, simulations are performed on a wind-turbine model and experimental results are presented for an outdoor single-blade inspection scenario both on a mock-up setup and a full-scale wind-turbine blade. It is worth noting that the system's adequacy has been fully validated in real conditions on an operational wind farm.

Six-axis force/torque sensors are transducers capable of measuring three dimensions of force and three dimensions of torque, and have a wide range of applications in robotic systems. The sensing principle behind the greatest majority of these is based on detecting strains in an elastic structure, and there are multiple modalities for strain measurement, including resistive strain gauge, piezoelectric, capacitive, and optoelectronic techniques. Developed sensors can be evaluated based on a range of parameters, including decoupling error, nonlinearity error, and measurement isotropy, where the primary design challenge is to maximise the allowable sensing range while minimising measurement errors. This literature review describes the state of the art of the field, and includes all relevant research associated with the search term “six-axis force/torque sensor” spanning 2015 - June 2021. The published literature for each sensing technique is summarised and evaluated, and a generalised sensor selection framework is proposed to assist the reader with the selection procedure for a state of the art sensor. In the period considered, there have been no significant improvements with the sensors documented in terms of measurement errors. Researchers have instead focused on features such as fault tolerance, alternative sensing techniques, cost reduction, and miniaturisation. These trends are expected to continue, and moving forward, the most significant challenge in the field is expected to be the lowering of manufacturing costs, as trends indicate measurement performance has reached maturity. [ABSTRACT FROM AUTHOR]

Modern electrical grippers have lower life-cycle costs compared to pneumatic ones. Furthermore, they provide force control, making it possible to grasp objects with different fragility using a single device. At the same time, electrical grippers have a higher end-effector weight, installed on the robot’s flange and lower closing speed, preventing them from replacing pneumatic solutions in high dynamic Pick and Place applications. This research faces both issues by synthesizing a novel gripper mechanism based on a Torque Distribution Gearbox, which makes it possible to relocate the electric motors to the static frame of a delta robot. The proposed gripper not only has a lower mass and a higher closing speed than competitive electric solutions, but it also provides unlimited rotation around the vertical axis. The performance of the gripper was tested in experimental studies, which showed that a created aluminum prototype provides a precise force control in the range from 3 N to 48 N with an accuracy not worse than 1.27 N. Moreover, its finger’s speed is 3.1–56 times higher than market available electrical grippers, which makes it comparable by this parameter with pneumatic solutions used in high dynamic Pick and Place applications.

Benchmarking is a common practice employed to quantify the performance of various approaches toward the same task. By maintaining a consistent test environment, the inherent behaviours between methods may be distinguished—which is key to progressing a research field. In robotic manipulation research there is a current lack of standardisation, making it challenging to fairly assess and compare various approaches throughout literature. This paper proposes new criteria in conjunction with a benchmarking platform to measure the effectiveness of a grasping pipeline. The proposed benchmarking template offers a testing platform for 2-fingered, vision-based grasp synthesis methodologies. A prototype system was constructed. The prototype was shown to serve as a suitable benchmarking platform for the deployment of various grasp synthesis methodologies. 4000 trials were conducted to evaluate the differing approaches. Results showed that the proposed metrics provide useful insights into the quality of grasp poses produced by a grasp synthesis methodology. Moreover, such metrics provide more comprehensive insights into grasp outcome than traditional methods used to quantify performance of a methodology and present a fair baseline for comparison between different approaches. [ABSTRACT FROM AUTHOR]

• Navigation and interaction technology of chicken farming robots are discussed. • Chicken farming robots for four types of poultry farming tasks are discussed. • Application challenges of robots in chicken houses and solutions are proposed. • Future research focus and development trend of chicken farming robots are proposed. Chicken farming plays a crucial role in the chicken industry. As the market demand for chicken products continues to grow, chicken farming is expanding on a larger scale to meet production needs. To address the challenges posed by the increased workload and labor shortages in chicken farming, chicken farming is transitioning towards standardization, automation, and intelligence. The rapid development of mobile robot technology offers solutions for the automated and intelligent execution of tasks in various scenarios. The automated and intelligent execution of farming tasks by robots holds significant importance in terms of liberating labor, implementing standardization and stable production, ensuring animal welfare, and enhancing production efficiency. In this review, the current situation in the chicken industry and the challenges encountered in implementing automation and intelligence in chicken farming tasks have been summarized to fully illustrate the important role of robots in the automation and intelligence of chicken farming. Based on the proposed categorization that considers the generality of robot technology, we review the application of general-purpose robotic technologies in chicken farming, such as localization, mapping, path planning, control, and interaction technology. Additionally, we explore the development of task-specific robotic technologies for various chicken farming tasks, including inspection, dead chicken picking, chicken house cleaning, and floor egg picking. Some tables are used to record and summarize certain technologies. We have identified that there are still many challenges to overcome in order to achieve mature and stable operation of robots in chicken farming. These challenges include significant technology homogeneity and a lack of attention to the characteristics of scenes and objects in chicken farming environments. To ultimately achieve the goal of an intelligent unmanned chicken farming system, future research priorities and development trends have been highlighted. [ABSTRACT FROM AUTHOR]

The problem discussed in this paper is how to create an integrated test automation setup, in which the test engineer does not have to use a pendant to communicate with the robotic arm and a separate test script to trigger on-device test automation for testing computer vision algorithms, such as motion-based image capture. There is no communication between the pendant to control the robotic arm and the test script that launches the on-device test automation for the motion-based image capture system executing on a mobile phone. Therefore, the test engineer needs to manually trigger the programs for the robot pendant to start the robotic arm, the automation, and launch times to keep them in sync. The solution to this problem is to design a test automation program using a middleware software (such as ORiN2 middleware software provided by Denso Robotics) to bypass the dependency on the robotic pendant, which must be manually started. The programmatic usage of the ORiN2 software also helps to establish a communication between the lab computer and a robot controller. The controller access object is used by the ORiN2 middleware software to communicate with the robotic controller of a Denso robot. It directly controls the robotic arm motion by accessing the programming variables. The image capture accuracy is calculated based on the number of snapshots and the similarity of the snapshots taken if the mobile phone under test is stationary and a motion is injected in the test subject. We compared the test results of the hardware-accelerated solution with a third-party solution, and found that the hardware accelerated solution is 12% more accurate for image capture accuracy at a medium panning speed. This paper discusses the integrated test automation framework for testing motion-based image capture system using a robotic arm.

Fiducial markers are commonly used to localize robots. Most existing systems use standard cameras to detect simple patterns on flat tags. Since depth is not directly sensed, the pose must be inferred from the tag geometry. These systems are low-cost and easy to implement on robotic systems, but their performances suffer in low-light conditions and on computationally constrained processors. We propose using 3D light detection and ranging (LiDAR) scanners to mitigate these issues. The reflectivity measurement provided by most LiDAR sensors provides a simple way to discern geometric differences on surfaces. We utilize this fact to create a custom “beacon” with reflective fiducials. Next, we design a high-performance segmentation and localization algorithm to find the 2D pose of a mobile robot. Our experiments proved that our system achieves an average euclidean error of less than 0.063 m at ranges of over 10 m while maintaining a runtime of under 3 ms on a basic single board computer. Additionally, our system is highly occlusion resistant. These results are confirmed with multiple field tests of the system.