Robot Experiments Research Articles

Fault Detection and Diagnosis of Three-Wheeled Omnidirectional Mobile Robot Based on Power Consumption Modeling

Three-wheeled omnidirectional mobile robots (TOMRs) are widely used to accomplish precise transportation tasks in narrow environments owing to their stability, flexible operation, and heavy loads. However, these robots are susceptible to slippage. For wheeled robots, almost all faults and slippage will directly affect the power consumption. Thus, using the energy consumption model data and encoder data in the healthy condition as a reference to diagnose robot slippage and other system faults is the main issue considered in this paper. We constructed an energy model for the TOMR and analyzed the factors that affect the power consumption in detail, such as the position of the gravity center. The study primarily focuses on the characteristic relationship between power consumption and speed when the robot experiences slippage or common faults, including control system faults. Finally, we present the use of a table-based artificial neural network (ANN) to indicate the type of fault by comparing the modeled data with the measured data. The experiments proved that the method is accurate and effective for diagnosing faults in TOMRs.

Modeling, simulation, and experiments of a flexible track robot over rigid horizontal and inclined surfaces

This paper studies the locomotion of a robot fitted with flexible rubber tracks while driving and climbing over rigid horizontal and inclined surfaces. We developed a model that accounts for sliding during rotation and the distribution of the normal forces along the track. On horizontal surfaces, we found that the size of the robot (width and length) and the normal force distribution along the track had a substantial influence on its turning speed and radius, whereas the coefficient of friction (COF), the robot's mass and inertia had a much lesser influence. The model shows that on inclined surfaces, even on relatively small slopes, the robot substantially slides downwards as it tries to rotate. The results of the model and the simulations were validated using a tracked vehicle that was built and tested on a solar panel. The experimental results of the turning radius and rotational speed were within 2% of the model (see video).

Design and Experiment of an Ankle Rehabilitation Robot After Fracture Surgery

Abstract In order to address the problem of functional rehabilitation after ankle fracture surgery, this paper presented a novel ankle fracture rehabilitation robot. The robot adopted R-3RRS-P hybrid structure, which was simple in structure and had two working modes: rehabilitation training and motion axis switching. Compared with the existing ankle rehabilitation robot, the proposed robot could simulate more realistic kinematics of the ankle joint complex. Additionally, different body types of patients could be adapted. The kinematic and static models were established in detail using geometric method and screw theory. The coverage of the healthy ankle motion ability was formulated as an optimization problem to improve the robot's performance. Multi-objective optimal design was carried out to determine the dimensional parameters. The interference-free working space was calculated by numerical method. A prototype of the proposed robot was developed, and a series of experiments were performed to evaluate the function and feasibility of the proposed robot.

Fully neuromorphic vision and control for autonomous drone flight.

Biological sensing and processing is asynchronous and sparse, leading to low-latency and energy-efficient perception and action. In robotics, neuromorphic hardware for event-based vision and spiking neural networks promises to exhibit similar characteristics. However, robotic implementations have been limited to basic tasks with low-dimensional sensory inputs and motor actions because of the restricted network size in current embedded neuromorphic processors and the difficulties of training spiking neural networks. Here, we present a fully neuromorphic vision-to-control pipeline for controlling a flying drone. Specifically, we trained a spiking neural network that accepts raw event-based camera data and outputs low-level control actions for performing autonomous vision-based flight. The vision part of the network, consisting of five layers and 28,800 neurons, maps incoming raw events to ego-motion estimates and was trained with self-supervised learning on real event data. The control part consists of a single decoding layer and was learned with an evolutionary algorithm in a drone simulator. Robotic experiments show a successful sim-to-real transfer of the fully learned neuromorphic pipeline. The drone could accurately control its ego-motion, allowing for hovering, landing, and maneuvering sideways-even while yawing at the same time. The neuromorphic pipeline runs on board on Intel's Loihi neuromorphic processor with an execution frequency of 200 hertz, consuming 0.94 watt of idle power and a mere additional 7 to 12 milliwatts when running the network. These results illustrate the potential of neuromorphic sensing and processing for enabling insect-sized intelligent robots.

First report of robotic retromuscular incisional hernia repair with Hugo Ras™ surgical system.

Treatment of incisional hernia is a rapidly evolving field of surgery, with actual trends being oriented toward retromuscular/preperitoneal mesh placement. The diffusion of robotic surgery is constantly growing in different surgical specialties and is gaining widespread acceptance for abdominal wall reconstruction. Recently, novel robotic platforms have entered into the market. In this study, we present the first transabdominal retromuscular incisional hernia repair performed with the new Hugo RAS™ system (Medtronic, Minneapolis, MN, USA). The surgical team had previous robotic experience and completed an official 2-day session running incisional hernia repair on human cadaver lab. Operating room setting and trocar layout were planned. The patient presented a 4 × 4cm midline incisional hernia and was scheduled for transabdominal retromuscular incisional hernia repair at our Institution. A description of the operative room setup, robotic arm configuration and docking/tilt angles is provided. Docking time, operative time, and console time were 15, 95, and 75min, respectively. All the surgical steps were completed without critical surgical errors or high-priority alarms. Neither intraoperative complications nor conversion to open surgery was recorded. Postoperative course was uneventful and the patient was discharged on postoperative day 2. The safety and the feasibility of these procedures will require further analysis and larger patients' sample sizes for procedural standardization and potential integration into minimally invasive abdominal wall reconstruction programs.

Design and experiment of multi-locomotion tensegrity mobile robot

Multi-locomotion tensegrity mobile robots have garnered significant attention in recent research due to their strong terrain adaptability and high stiffness-to-mass ratio. However, the lack of effective design method limits the application range of these robots. In this article, a mechanism design method for the multi-locomotion tensegrity mobile robot is proposed. Firstly, based on the characteristics of worms, mechanism design objectives for the tensegrity robot are put forward from three perspectives, namely bionics, motion law and tensegrity characteristics. Accordingly, the mechanism design process is conducted from two sections in terms of the design of multi-locomotion tensegrity units and the splicing of the identical units. In the mechanism design for the tensegrity units, the rod distribution is firstly determined on compliance with the proposed design objectives, followed by the determination of cable distribution with force-balance analysis method. On this basis, two identical multi-locomotion tensegrity units are axially spliced, and a series of multi-locomotion tensegrity robotic mechanisms can be acquired naturally. Finally, examples are provided, and simulations and experiments are conducted to demonstrate the effectiveness of the proposed mechanism design method.

Autonomous Robots Traverse Multi-Terrain Environments via Hierarchical Reinforcement Learning with Skill Discovery

Although deep reinforcement learning (DRL) has been widely used in robotic mapless navigation tasks, most of the current research focuses on structured environments such as indoor or maze scenes, and little is targeted on outdoor environments. Unlike indoor environments and mazes, outdoor fields tend to be unstructured with complex landforms and sparse rewards for robotic navigation. The performance of most DRL-based strategies is directly affected by the design of the reward function, which greatly deteriorates its generalizability to outdoor environments. To this end, here we propose a two-stage learning paradigm based on skill discovery and hierarchical reinforcement learning (HRL) to cope with this challenge. Specifically, we implement skill discovery through a pre-training stage to acquire diverse skills with terrain-adaptive exploration strategies; then we select multiple skills using HRL to cope with more complex scenarios. We carry out the robotic multi-terrain traverse task based on a high-fidelity robotic simulation platform, Webots, and implement extensive comparative experiments and ablation studies to demonstrate the effectiveness of our approach.

Laparoscopic vs robotic inguinal hernia repair: a comparison of learning curves and skill transference in general surgery residents.

There is no consensus on whether laparoscopic experience should be a prerequisite for robotic training. Further, there is limited information on skill transference between laparoscopic and robotic techniques. This study focused on the general surgery residents' learning curve and skill transference within the twominimally invasive platforms. General surgery residents were observed during the performance of laparoscopic and robotic inguinal hernia repairs. The recorded data included objective measures (operative time, resident participation indicated by percent active time on console or laparoscopy relative to total case time, number of handoffs between the resident and attending), and subjective evaluations (preceptor and trainee assessments of operative performance) while controlling for case complexity, patient comorbidities, and residents' prior operative experience. Wilcoxon two-sample tests and Pearson Correlation coefficients were used for analysis. Twenty laparoscopic and forty-four robotic cases were observed. Mean operative times were 90min for robotic and 95min for laparoscopic cases (P = 0.4590). Residents' active participation time was 66% on the robotic platform and 37% for laparoscopic (P = < 0.0001). On average, hand-offs occurred 9.7 times during robotic cases and 6.3 times during laparoscopic cases (P = 0.0131). The mean number of cases per resident was 5.86 robotic and 1.67 laparoscopic (P = 0.0312). For robotic cases, there was a strong correlation between percent active resident participation and their prior robotic experience (r = 0.78) while there was a weaker correlation with prior laparoscopic experience (r = 0.47). On the other hand, prior robotic experience had minimal correlation with the percent active resident participation in laparoscopic cases (r = 0.12) and a weak correlation with prior laparoscopic experience (r = 0.37). The robotic platform may be a more effective teaching tool with a higher degree of entrustability indicated by the higher mean resident participation. We observed a greater degree of skill transference from laparoscopy to the robot, indicated by a higher degree of correlation between the resident's prior laparoscopic experience and the percent console time in robotic cases. There was minimal correlation between residents' prior robotic experience and their participation in laparoscopic cases. Our findings suggest that the learning curve for the robot may be shorter as prior robotic experience had a much stronger association with future robotic performance compared to the association observed in laparoscopy.

Dynamic path planning for mobile robots based on artificial potential field enhanced improved multiobjective snake optimization (APF‐IMOSO)

AbstractWith the widespread adoption of mobile robots, effective path planning has become increasingly critical. Although traditional search methods have been extensively utilized, meta‐heuristic algorithms have gained popularity owing to their efficiency and problem‐specific heuristics. However, challenges remain in terms of premature convergence and lack of solution diversity. To address these issues, this paper proposes a novel artificial potential field enhanced improved multiobjective snake optimization algorithm (APF‐IMOSO). This paper presents four key enhancements to the snake optimizer to significantly improve its performance. Additionally, it introduces four fitness functions focused on optimizing path length, safety (evaluated via artificial potential field method), energy consumption, and time efficiency. The results of simulation and experiment in four scenarios including static and dynamic highlight APF‐IMOSO's advantages, delivering improvements of 8.02%, 7.61%, 50.71%, and 12.74% in path length, safety, energy efficiency, and time‐savings, respectively, over the original snake optimization algorithm. Compared with other advanced meta‐heuristics, APF‐IMOSO also excels in these indexes. Real robot experiments show an average path length error of 1.19% across four scenarios. The results reveal that APF‐IMOSO can generate multiple viable collision‐free paths in complex environments under various constraints, showcasing its potential for use in dynamic path planning within the realm of robot navigation.

Flow interactions lead to self-organized flight formations disrupted by self-amplifying waves

Collectively locomoting animals are often viewed as analogous to states of matter in that group-level phenomena emerge from individual-level interactions. Applying this framework to fish schools and bird flocks must account for visco-inertial flows as mediators of the physical interactions. Motivated by linear flight formations, here we show that pairwise flow interactions tend to promote crystalline or lattice-like arrangements, but such order is disrupted by unstably growing positional waves. Using robotic experiments on “mock flocks” of flapping wings in forward flight, we find that followers tend to lock into position behind a leader, but larger groups display flow-induced oscillatory modes – “flonons” – that grow in amplitude down the group and cause collisions. Force measurements and applied perturbations inform a wake interaction model that explains the self-ordering as mediated by spring-like forces and the self-amplification of disturbances as a resonance cascade. We further show that larger groups may be stabilized by introducing variability among individuals, which induces positional disorder while suppressing flonon amplification. These results derive from generic features including locomotor-flow phasing and nonreciprocal interactions with memory, and hence these phenomena may arise more generally in macroscale, flow-mediated collectives.

Dynamic 3D Point-Cloud-Driven Autonomous Hierarchical Path Planning for Quadruped Robots.

Aiming at effectively generating safe and reliable motion paths for quadruped robots, a hierarchical path planning approach driven by dynamic 3D point clouds is proposed in this article. The developed path planning model is essentially constituted of two layers: a global path planning layer, and a local path planning layer. At the global path planning layer, a new method is proposed for calculating the terrain potential field based on point cloud height segmentation. Variable step size is employed to improve the path smoothness. At the local path planning layer, a real-time prediction method for potential collision areas and a strategy for temporary target point selection are developed. Quadruped robot experiments were carried out in an outdoor complex environment. The experimental results verified that, for global path planning, the smoothness of the path is improved and the complexity of the passing ground is reduced. The effective step size is increased by a maximum of 13.4 times, and the number of iterations is decreased by up to 1/6, compared with the traditional fixed step size planning algorithm. For local path planning, the path length is shortened by 20%, and more efficient dynamic obstacle avoidance and more stable velocity planning are achieved by using the improved dynamic window approach (DWA).

Studying animal locomotion with multiple data loggers: quantifying time drift between tags

Temporal accuracy is a fundamental characteristic of logging technology and is needed to correlate data streams. Single biologgers sensing animal movement (accelerometers, gyroscope, magnetometers, collectively inertial measurement unit; IMU) have been extensively used to study the ecology of animals. To better capture whole body movement and increase the accuracy of behavior classification, there is a need to deploy multiple loggers on a single individual to capture the movement of multiple body parts. Yet due to temporal drift, accurately aligning multiple IMU datasets can be problematic, especially as deployment duration increases. In this paper we quantify temporal drift and errors in commercially available IMU data loggers using a combination of robotic and animal borne experiments. The variance in drift rate within a tag is over an order of magnitude lower (σ = 0.001 s h−1) than the variance between tags (σ = 0.015 s·h−1), showing that recording frequency is a characteristic of each tag and not a random variable. Furthermore, we observed a large offset (0.54 ± 0.016 s·h−1) between two groups of tags that had differing recording frequencies, and we observed three instances of instantaneous temporal jumps within datasets introducing errors into the data streams. Finally, we show that relative drift rates can be estimated even when deployed on animals displaying various behaviors without the tags needing to be simultaneously moved. For the tags used in this study, drift rates can vary significantly between tags, are repeatable, and can be accurately measured in the field. The temporal alignment of multiple tag datasets allows researchers to deploy multiple tags on an individual animal which will greatly increase our knowledge of movement kinematics and expand the range of movement characteristics that can be used for behavioral classification.

Effects of robots’ appearance on guest service experiences

ABSTRACT Robots are starting to replace human employees in the accommodation industry. It remains unclear how guests perceive robots. The study aims to evaluate the effectiveness of the humanoid and non-humanoid robots and to compare the guests’ perceptions. The data was collected by web digging and analyzed using text mining and machine learning. The results show that guests experience more positive emotions in non-humanoid robotic experiences. They also stated that they found both types of robot fun and interesting. However, guests have negative perceptions about the perceived usefulness and appearance of humanoid robots. According to the findings of the machine learning analysis, the neural network and logistic regression models show the best statistical performance in both datasets compared to other machine learning models. The study contributes to the literature through an investigation of guests’ opinions regarding robot types with the use of machine learning and text mining methods.

Probabilistic movement primitives based multi-task learning framework

With the increasing complexity of industrial production and manufacturing tasks, industrial robots are expected to learn intricate operations from simple actions easily and quickly with adaption to dynamic environment. In this paper, a task-parameterized multi-task learning framework is proposed to facilitate rapid learning of operational skills for industrial robots. In this framework, a conditional Probabilistic Movement Primitives (ProMP) is firstly employed to the single-task learning. Using the conditional probability calculation, the extrapolation issue in Learning from Demonstration (LfD) is addressed, enabling robots to learn beyond teaching. Subsequently, the single-task is extended to multi-task scenario by proposing a multi-task learning approach where each single task executes an extrapolation learning. The learned skill can meet the multiple task requirements through an iterative modulation manner. The effectiveness of the proposed framework is validated through both the simulation and a 7-DoF Franka-Emika robot experiment in a predefined task scenario. Furthermore, the outperformance of the proposed method is demonstrated by comparing with the state-of-art movement primitives based learning method.

Teaming with a Robot in Mixed Reality: Dynamics of Trust, Self-Efficacy, and Mental Models Affected by Information Richness

This study employs an innovative mixed-reality game to investigate trust, self-efficacy, and collaboration willingness with a mobile robot in relation to information and mental models. Previous research has demonstrated the significant impact of introductory information on technology use and trust, which further evolves over the course of interactions. Expanding on this, we explore new facets of information content through comprehensive measurements in two empirical studies: In study 1, 68 participants watched one of two video tutorials (high or low information richness) before collaborating with a physical robot in a shared virtual 3D environment. Repeated measures served to investigate temporal dynamics. In study 2, 37 participants additionally engaged in qualitative interviews to better understand how their mental models changed with increasing robot experience. Both tutorials were found to influence user trust and self-efficacy over time, with no significant differences between the two variants. Users valued information on how to communicate with the robot, but still relied on trial-and-error approaches. We argue for a moderating effect of information content and richness and highlight the need for carefully designed tutorials to enhance human-robot interaction.

Active vibration control in robotic grinding using six-axis acceleration feedback

Industrial robots endure severe vibrations in the grinding process due to factors such as joint flexibility, low damping, and contact force fluctuations. Additionally, the motion control of the robot primarily relies on feedback signals from the motor-side encoders, while lacking motion state feedback from the load side. This exacerbates the robot vibrations, severely compromising the surface quality of the workpieces. To address this problem, an active damping control method for robotic grinding using six-axis acceleration feedback is proposed. This method realizes a simple, reliable, and efficient active vibration control without altering the robot body. A multi-sensor fusion Cartesian space six-axis accelerometer is designed and integrated into the robot end effector, achieving accurate feedback on the robot load side vibration state. The strategy of six-axis acceleration decoupling and principle of the vibration suppression with the acceleration feedback are explained. Compared with conventional sensing schemes, this reduces joint acceleration approximation errors and end vibrations. The robotic grinding experiments are carried out on aeronautic structural parts. Extensive case studies demonstrate that the proposed approach can effectively control the vibration caused by joint flexibility in the robot grinding process, and greatly improve the surface quality of workpieces. This method has great potential application in vibration suppression during robotic grinding and polishing.

IHUVS: Infinite Homography-Based Uncalibrated Methodology for Robotic Visual Servoing

The construction of task functions in robotic manipulation is of paramount importance for uncalibrated visual servoing. The existing methods generally use image information as control variables and estimate the image Jacobian matrix online, thus possessing issues relating to convergence, and image Jacobian matrix singularities. Therefore, this work proposes a novel methodology dubbed infinite homography-based uncalibrated visual servoing (IHUVS), in which the visual control of the robot end-effector pose is decomposed into its rotational and translational components. The corresponding rotational controller designs the visual servoing task function using the relationship between the infinite homography matrix and rotation matrix, and employs the Kronecker product to derive linear equations for rotational control, as well as to conduct the associated task error analysis. Meanwhile, the translational controller utilizes Kalman filtering for online estimation of the Jacobian matrix that is required by the proportional control scheme. The robot end-effector motion in Cartesian space is generated via the IHUVS method, without knowing the camera's intrinsic parameters and the robot hand-eye relationship. A simulation analysis is carried out to assess the algorithm's numerical performance, while robotic visual servoing experiments are also conducted to verify the accuracy and efficacy of the proposed IHUVS method.

Randomized controlled trial of the CMR immersive virtual reality (IVR) headset training compared to e-learning for operating room configuration of the CMR versius robot

Robotic surgery offers potential advantages over laparoscopic procedures, but the training for configuring robotic systems in the operating room remains underexplored. This study seeks to validate immersive virtual reality (IVR) headset training for setting up the CMR Versius in the operating room. This single-blinded randomized control trial randomised medical students with no prior robotic experience using an online randomiser. The intervention group received IVR headset training, and the control group, e-learning modules. Assessors were blinded to participant group. Primary endpoint was overall score (OS): Likert-scale 1–5: 1 reflecting independent performance, with increasing verbal prompts to a maximum score of 5, requiring physical assistance to complete the task. Secondary endpoints included task scores, time, inter-rater reliability, and concordance with participant confidence scores. Statistical analysis was performed using IBM SPSS Version 27. Of 23 participants analysed, 11 received IVR and 12 received e-learning. The median OS was lower in the IVR group than the e-learning group 53.5 vs 84.5 (p < 0.001). VR recipients performed tasks independently more frequently and required less physical assistance than e-learning participants (p < 0.001). There was no significant difference in time to completion (p = 0.880). Self-assessed confidence scores and assessor scores differed for e-learning participants (p = 0.008), though not IVR participants (p = 0.607). IVR learning is more effective than e-learning for preparing robot-naïve individuals in operating room set-up of the CMR Versius. It offers a feasible, realistic, and accessible option in resource-limited settings and changing dynamics of operating theatre teams. Ongoing deliberate practice, however, is still necessary for achieving optimal performance. ISCRTN Number 10064213.

Multimodal zero-shot learning for tactile texture recognition

Tactile sensing plays an irreplaceable role in robotic material recognition. It enables robots to distinguish material properties such as their local geometry and textures, especially for materials like textiles. However, most tactile recognition methods can only classify known materials that have been touched and trained with tactile data, yet cannot classify unknown materials that are not trained with tactile data. To solve this problem, we propose a tactile Zero-Shot Learning framework to recognise materials when they are touched for the first time, using their visual and semantic information, without requiring tactile training samples. The biggest challenge in tactile Zero-Shot Learning is to recognise disjoint classes between training and test materials, i.e., the test materials that are not among the training ones. To bridge this gap, the visual modality, providing tactile cues from sight, and semantic attributes, giving high-level characteristics, are combined together and act as a link to expose the model to these disjoint classes. Specifically, a generative model is learnt to synthesise tactile features according to corresponding visual images and semantic embeddings, and then a classifier can be trained using the synthesised tactile features for zero-shot recognition. Extensive experiments demonstrate that our proposed multimodal generative model can achieve a high recognition accuracy of 83.06% in classifying materials that were not touched before. The robotic experiment demo and the FabricVST dataset are available at https://sites.google.com/view/multimodalzsl.

Design and Experiment of an Agricultural Field Management Robot and Its Navigation Control System

The application of robotics has great implications for future food security, sustainable agricultural development, improving resource efficiency, reducing chemical pesticide use, reducing manual labor, and maximizing field output. Aiming at the problems of high labor intensity and labor shortage in the fields of pesticide application, weeding, and field information collection, a multifunctional and electric field management robot platform is designed, which has four switching steering modes (Ackermann steering, four-wheel steering, crab steering, and zero-radius steering), and its wheel-track can be automatically adjusted. Commonly used spraying booms, weeders, crop information collectors, and other devices can be easily installed on the robot platform. A multi-sensor integrated navigation system including a satellite positioning system, an RGB camera, and a multi-line lidar is designed to realize the unmanned driving of the robot platform in a complex field environment. Field tests have shown that the robot can follow the set route, and tests under simulated conditions have indicated that it can also dynamically correct paths based on crop rows by using a visual system. Results from multiple trials showed that the trajectory tracking accuracy meets the requirements of various field management operations.