Robot Experiments Research Articles

Adoption of Robotic Adrenalectomy: A Two-Institution Study of Surgeon Learning Curve.

Robotic adrenalectomy is feasible and safe, yet concerns over increased operative times and the learning curve (LC) for proficiency have limited its adoption. This study aimed to assess the LC for robotic adrenalectomy. This is a two-institution retrospective review of consecutive unilateral minimally invasive adrenalectomies performed by four high-volume adrenal surgeons between 2007 and 2022. Two surgeons transitioned from laparoscopic to robotic adrenalectomy, and two surgeons adopted the approach, with proctoring, after completion of fellowship training without robotic experience. Operative time and complications were analyzed. Multivariable regression was used to identify factors associated with operative time. The number of cases required to overcome the LC was determined using the LC-cumulative-sum (LC-CUSUM) analysis. Of 457 adrenalectomies, 182 (40%) were laparoscopic and 275 (60%) robotic. The robotic approach was associated with shorter median operative time (106 vs 119 min; p = 0.002), fewer complications (6% vs 13%; p = 0.018), and fewer conversions to open adrenalectomy (1% vs 4%; p = 0.030), with no difference between the senior and junior surgeons. On adjusted analysis, factors associated with increased operative time were male sex (p < 0.001), BMI > 30 kg/m2 (p < 0.001), and higher gland weight (p < 0.001). The LC-CUSUM analysis showed proficiency after 8-29 procedures. Compared with the first 10 cases, there was a mean reduction in operative time of 14 min after 10-20 cases, 28 min after 20-30 cases, and 29 min after > 30 cases, regardless of surgeon experience. With dedicated teams and proctoring, robotic adrenalectomy can be safely adopted at high-volume centers with a minimal LC.

Pose-Dependent Cutting Force Identification for Robotic Milling

AbstractCutting force identification is critical to improving industrial robot performance and reducing machining vibration. However, most indirect identification methods of cutting force are not applicable since the modal parameters of the robotic milling system vary with the robot pose. This paper presents a novel pose-dependent method to identify the cutting force using the acceleration signal generated by robotic milling. First, the modal parameters at different machining points are employed as a training dataset to develop the Gaussian Process Regression (GPR) model. Next, the modal parameters predicted by the GPR model are employed to optimize the cutting force estimation based on the minimum variance unbiased estimate method. Then, the Kalman filter method is employed to update the covariance matrix of the cutting force identification error and the state estimation error. Lastly, the effectiveness of the proposed method is verified with robotic milling experiments, and the results show that the identification error and time are acceptable under the condition of variable robot pose.

Autonomous Shallow Water Hydrographic Survey Using a Proto-Type USV

Maritime unmanned systems (MUS) have gained widespread usage in a diverse range of hydrographic survey activities, including harbor/port surveys, beach and coastline monitoring, environmental assessment, and military operations. The present article explains a validated, rapid, and reliable technique for processing hydrographic data that was obtained via an autonomous hydrographic survey, and which was executed by a prototype unmanned surface vessel (USV) belonging to the Unmanned Survey Solutions (USS) corporation. The experimentation was part of the annual Multinational Exercise Robotic Experimentation and Prototyping that was augmented by Maritime Unmanned Systems 22 (REPMUS22), which was held in the national waters of Portugal. The main objective of this experimentation was to assess the underwater environment over an ocean beach for an amphibious landing exercise. Moreover, the integration of the multibeam system with the autonomous prototype vessel was assessed. A short comparison between the USV survey and a traditional vessel multibeam survey is presented, whereby the advantages of performing an autonomous survey operation near the coastline is emphasized. A correlation between a known multibeam processing technique and the dissemination of a rapid but consistent product for operational use is described, highlighting the applicability of the technique for the data collected from small experimental platforms. Moreover, this study outlines the relationship between the particular errors observed in autonomous small vehicles and in conventional data processing methods. The resultant cartographic outputs from the hydrographic survey are presented, emphasizing the specific inaccuracies within the raw data and the suitability of distinct hydrographic products for various user domains.

Distributed Localization Estimation for Dynamic Multiagent Systems

This article investigates the real-time localization problem of dynamic multiagent systems with repetitive operation characteristics under directed graph. A distributed localization estimation algorithm based on iterative learning is proposed. The barycentric coordinates calculated based on the relative distance are used to estimate the real coordinates of the agent. Different from the traditional estimation methods along the time axis, the proposed method utilizes the information of iteration axis simultaneously. In this method, the current estimation coordinates are updated by using the estimation coordinates of the same sampling time in previous iteration, the estimation accuracy is improved, and the velocity constraint is removed. Additionally, the real-time localization problem of dynamic multiagent systems under arbitrary deployment is concerned. An improved distributed localization estimation algorithm with signed coefficients based on iterative learning is proposed. Meanwhile, the results are also extended to the localization estimation of multiagent systems with arbitrary deployment in 3-D space. By introducing Richardson iteration and infinite norm, the global asymptotic convergence of the proposed methods is guaranteed. Finally, numerical simulations and the Qbot-2e robot experiment are provided to show the effectiveness and validity of the obtained results.

DeepLab V2 기반 실내 시설물 손상 탐지 및 추출 알고리즘

This paper proposes an algorithm that displays the damage of main indoor facilities by detecting and extracting information. First, to extract information for each facility structure, a facility segmentation algorithm is implemented by using DeepLab V2. For efficient learning of the network, the pretrained ResNet101 network is used for transfer learning. In addition, the damage extraction algorithm of the indoor facilities is also based on DeepLab V2, the same network structure used in facility segmentation. It is confirmed through a robot experiment that the accuracy of representing the damage information regardless of near and far facilities is greatly improved when the facility segmentation algorithm is used.

Study on Head Stabilization Control Strategy of Non-Wheeled Snake Robot Based on Inertial Sensor

In this paper, the head stabilization problem of the snake robot in planar motion is studied. When the snake robot performs a planar movement with an inchworm locomotion gait, the head controller of the snake robot swings up and down due to a fluctuation in the joint angle of the neck joint. However, the snake robot usually has a laser radar and other visual instruments on the head, and the swing of the head causes the visual instrument to fail to obtain external visual information normally, which affects the navigation and detection of the snake robot. In this paper, a head stabilization method for a snake robot in planar motion is proposed. The inertial sensor is used to obtain the direction parameters to control the swing of the head when the snake robot moves, and the effectiveness of the method is verified by a simulation and an experiment of the real robot.

Study on the Multi-Level Experiment Teaching of NAO Robot Combined with Virtual and Reality

NAO is the most well-known and widely used humanoid robot in education and research in the world. With the development of computer technology and cloud computing, and the establishment of artificial intelligence specialty, robot teaching is more important in artificial intelligence specialty. The artificial intelligence specialty is a new specialty just established in the talent training program of Chinese universities. Its purpose is to cultivate application-oriented talents in China's artificial intelligence industry and promote the construction of first-class disciplines of artificial intelligence. MATLAB is the commercial mathematical software. It is a high-level technical computing language and interactive environment for algorithm development, data visualization, data analysis and numerical calculation. It is widely used in the experimental teaching of NAO robot. In the teaching of robot theory, kinematics, trajectory planning, dynamics and control are relatively abstract and complex, and there are a lot of formula derivation and differential equations. In order to improve the quality of NAO experiment teaching, this paper analyzes the current situation and existing problems in NAO experiment teaching. In the exploration and practice of robot experiment teaching, NAO robot is taken as the research object, and MATLAB is used to carry out robot simulation and control experiments. The paper presents a multi-level experimental teaching method of NAO robot based on the combination of virtual and Reality. The multi-level includes three levels: basic experiment, design experiment and innovative experiment. The paper describes the design and implementation process of the three-level experimental teaching in detail, and gives the assessment methods, characteristics and effectiveness evaluation of the experimental teaching. The teaching results show that the teaching effect is good, can stimulate students' enthusiasm for learning, and help to cultivate students' programming ability and innovation ability.

Chatter stability of robotic rotary ultrasonic countersinking

In recent years, industrial robots have received extensive attention in manufacturing field due to their high flexibility and great workspace. However, the weak stiffness of industrial robots makes it extremely easy to arouse chatter, which affects machining quality inevitably and generates noise pollution in severe cases. Compared with drilling, the chatter mechanism of robotic countersinking is more complex. The external excitation changes with cutting width and depth in countersinking. This characteristic results in time-varying and nonlinearity of robotic countersinking dynamics. Thus, it is urgent to propose a new method of chatter suppression and provide an accurate stability analysis model. As a new special machining technology, rotary ultrasonic machining has been proved to improve robotic drilling and milling stability effectively. Based on this, robotic rotary ultrasonic countersinking (RRUC) is proposed to improve the robotic countersinking stability in this paper. A three-dimensional stability domain method of RRUC is established. First, the countersinking process was divided into ρ parts. The dynamic model of every unit was constructed based on ultrasonic function angle (γ) and dynamic chip area. Then, the stability region of RRUC is obtained based on the semi-discrete method (SDM). Compared with the robotic conventional countersinking (RCC), RRUC improves the stability by 27%. Finally, the correctness and effectiveness of the stability region model are proved by robotic ultrasonic countersinking experiments.

Vision‐audio fusion SLAM in dynamic environments

AbstractMoving humans, agents, and subjects bring many challenges to robot self‐localisation and environment perception. To adapt to dynamic environments, SLAM researchers typically apply several deep learning image segmentation models to eliminate these moving obstacles. However, these moving obstacle segmentation methods cost too much computation resource for the onboard processing of mobile robots. In the current industrial environment, mobile robot collaboration scenario, the noise of mobile robots could be easily found by on‐board audio‐sensing processors and the direction of sound sources can be effectively acquired by sound source estimation algorithms, but the distance estimation of sound sources is difficult. However, in the field of visual perception, the 3D structure information of the scene is relatively easy to obtain, but the recognition and segmentation of moving objects is more difficult. To address these problems, a novel vision‐audio fusion method that combines sound source localisation methods with a visual SLAM scheme is proposed, thereby eliminating the effect of dynamic obstacles on multi‐agent systems. Several heterogeneous robots experiments in different dynamic scenes indicate very stable self‐localisation and environment reconstruction performance of our method.

Emotional experiences of service robots’ anthropomorphic appearance: a multimodal measurement method

Anthropomorphic appearance is a key factor to affect users’ attitudes and emotions. This research aimed to measure emotional experience caused by robots’ anthropomorphic appearance with three levels – high, moderate, and low – using multimodal measurement. Fifty participants’ physiological and eye-tracker data were recorded synchronously while they observed robot images that were displayed in random order. Afterward, the participants reported subjective emotional experiences and attitudes towards those robots. The results showed that the images of the moderately anthropomorphic service robots induced higher pleasure and arousal ratings, and yielded significantly larger pupil diameter and faster saccade velocity, than did the low or high robots. Moreover, participants’ facial electromyography, skin conductance, and heart-rate responses were higher when observing moderately anthropomorphic service robots. An implication of the research is that service robots’ appearance should be designed to be moderately anthropomorphic; too many human-like features or machine-like features may disturb users’ positive emotions and attitudes. Practitioner Summary: This research aimed to measure emotional experience caused by three types of anthropomorphic service robots using a multimodal measurement experiment. The results showed that moderately anthropomorphic service robots evoked more positive emotion than high and low anthropomorphic robots. Too many human-like features or machine-like features may disturb users’ positive emotions.

Robot-assisted general surgery is safe during the learning curve: a 5-year Australian experience

Robot-assisted general surgery has become increasingly common in the Australian public sector since 2003. It provides significant technical advantages compared to laparoscopic surgery. Currently, it is estimated that the learning curve for surgeons starting off with robotic surgery is complete after 15 cases. This is a retrospective case series, following the progress of four surgeons with minimal robotic experience over 5 years. Patients undergoing colorectal procedures and hernia repairs were included. 303 robotic cases were included in this study, 193 colorectal surgeries and 110 hernia repairs. 20.2% of colorectal patients experienced an adverse event and 10.0% of hernia patients had a complication. The learning curve was correlated to the average docking time, and it was found that this was complete after 2 years, or after a minimum of 12 to 15 cases. Patient length of stay decreases as surgeon experience increases. Robotic surgery is a safe approach to colorectal surgery and hernia repairs with some potential benefits in terms of patient outcomes as surgeon experience increases.

First worldwide report on Hugo RAS™ surgical platform in right and left colectomy.

The diffusion of robotic surgery is rapidly and constantly growing in different surgical specialties. Recently, novel robotic platforms have entered into the market. To date, however, most of the reports on their clinical use have specifically focused on gynecological and urological surgery. In this study, we present the first three robotic-assisted colectomies performed with the new Hugo RAS system (Medtronic, Minneapolis, MN, USA). The surgical team had previous robotic experience and completed simulation training and an official 2-day cadaver laboratory session. Operating room setting and trocar layout were planned and two full cadaver procedures were carried out (right and left colectomy). Onsite dry-run sessions were performed before tackling clinical cases. Three patients underwent robotic-assisted colectomies: one left colectomy, two right colectomies with complete mesocolic excision (CME) and high vascular ligation (HVL) at our Institution. Preoperative diagnosis was colonic adenocarcinoma in all cases. A description of the operative room setup, robotic arm configuration and docking angles is provided. Mean docking time and console time were 8 and 259min, respectively. All the surgical steps were completed without critical surgical errors or high-priority alarms. Neither intraoperative complications nor conversions to open surgery were recorded. Postoperative courses were uneventful with a mean length of stay of 5days. Further clinical data and experience are required for procedural standardization and potential integration of the system into robotic general surgery and colorectal programs.

Robot path planning based on shuffled frog leaping algorithm combined with genetic algorithm

When the shuffled frog leaping algorithm (SFLA) is used to solve the robot path planning problem in obstacle environment, the quality of the initial solution is not high, and the algorithm is easy to fall into local optimization. Herein, an improved SFLA named ISFLA combined with genetic algorithm is proposed. By introducing selection, crossover and mutation operators in genetic algorithm, the ISFLA not only improves the solution quality of the SFLA, but also accelerates its convergence speed. Moreover, the ISFLA also proposes a location update strategy based on the central frog, which makes full use of the global information to avoid the algorithm falling into local optimization. By comparing ISFLA with other algorithms including SFLA in the map environment of different obstacles, it is confirmed that ISFLA can effectively improve the minimum path optimization and robustness in the simulation experiments of mobile robots.

Foot trajectory and gait planning of quadruped robot

Foot trajectory and gait planning is one of the important research fields of foot robot. The eighth order polynomial is used to plan the trajectory of each joint of the robot leg, so that the Angle, angular velocity and angular acceleration curves of each joint during leg movement can be smooth and continuous, so as to improve the smooth and quick action of the leg movement. Through MATLAB simulation and robot experiment, it is proved that the eight-order polynomial can keep the legs of the robot continuously and smoothly in the process of movement, reduce the impact of the mutation of the joint movement on the legs of the robot, so that the running trajectory of the robot can be optimized to a certain extent.

Self-organized multi-target trapping of swarm robots with density-based interaction

AbstractThe task of multi-target trapping in swarm robots can often be solved by global shape planning and target assignment, but it still remains a challenge to achieve fully self-organized multi-target trapping behavior based on local information. In this paper, inspired by the concept of spatial density in physics and biology, we proposed a novel density-based method to enable the swarm robots to entrap multiple targets with either single-ring, multi-ring or multi-subgroup formation in a distributed and self-organized way while neither communication among robots nor encirclement function is required. Each robot’s local spatial density is considered as the main clue for the individual’s motion decision-making and the enclosed configurations emerge from such individual-level interactions rather than being explicitly designed. Numerical simulations and real robotic experiments are conducted to validate the effectiveness of the proposed method. The results show that the proposed self-organized trapping method allows a swarm of robots to entrap multiple moving targets in a stable, flexible, noise-tolerate and size-scalable fashion.

Research trends on guest experience with service robots in the hospitality industry: a bibliometric analysis

PurposeThe purpose of this study is to reveal the research trends in guest experiences of service robots in the hospitality industry.Design/methodology/approachIn this study, a review was carried out on the Web of Science (WoS) database with the assistance of bibliometric analysis techniques. Cluster analysis was also employed for this to group important data to determine the relationships and to visualize the areas in which the studies are concentrated. The thematic content analysis method was used to reveal on which customer experiences and on which methods the focuses were.FindingsOn the subject of experiences of service robots, the greatest number of publications was in 2021. In terms of country, China has come to the fore in the distribution of publications. As a result of thematic content analysis, it was determined that the leading factor was the main dimension of emotional experience. In terms of sub-dimensions, social interactions attracted more attention. Most of the studies discussed were not based on any theory. Apart from these, the Technology Acceptance Model (TAM), the Service Quality Model (SERVQUAL) and Perceived Value Theory (PVT) were featured more prominently among other studies.Research limitations/implicationsIn this study, only the WoS database was reviewed. In future studies, it would be possible to make contextual comparisons by scanning other databases. In addition to quantitative research designs, social dimensions may be examined in depth following qualitative research methods. Thus, various comparisons can be made on the subject with mixed-method research designs. Experimental research designs can also be applied to where customers have experienced human-robot interactions (HRIs).Originality/valueIn the hospitality industry, it is critical to uncover every dimension of guests' robot acceptance. This study, which presents the current situation on this basis, guides future projections for the development of guest experiences regarding service robots in the hospitality industry.

Mission specification and decomposition for multi-robot systems

Service robots are increasingly being used to perform missions comprising dangerous or tedious tasks previously executed by humans. However, their users—who know the environment and requirements for these missions—have limited or no robotics experience. As such, they often find the process of allocating concrete tasks to each robot within a multi-robot system (MRS) very challenging. Our paper introduces a framework for Multi-Robot mission Specification and decomposition (MutRoSe) that simplifies and automates key activities of this process. To that end, MutRoSe allows an MRS mission designer to define all relevant aspects of a mission and its environment in a high-level specification language that accounts for the variability of real-world scenarios, the dependencies between task instances, and the reusability of task libraries. Additionally, MutRoSe automates the decomposition of MRS missions defined in this language into task instances, which can then be allocated to specific robots for execution—with all task dependencies appropriately taken into account. We illustrate the application of MutRoSe and show its effectiveness for four missions taken from a recently published repository of MRS applications.

Adaptive Model Predictive Control for Mobile Robots with Localization Fluctuation Estimation

Mobile robots are widely employed in various fields to perform autonomous tasks. In dynamic scenarios, localization fluctuations are unavoidable and obvious. However, common controllers do not consider the impact of localization fluctuations, resulting in violent jittering or poor trajectory tracking of the mobile robot. For this reason, this paper proposes an adaptive model predictive control (MPC) with an accurate localization fluctuation assessment for mobile robots, which balances the contradiction between precision and calculation efficiency of mobile robot control. The distinctive features of the proposed MPC are three-fold: (1) Integrating variance and entropy-a localization fluctuation estimation relying on fuzzy logic rules is proposed to enhance the accuracy of the fluctuation assessment. (2) By using the Taylor expansion-based linearization method-a modified kinematics model that considers that the external disturbance of localization fluctuation is established to satisfy the iterative solution of the MPC method and reduce the computational burden. (3) An improved MPC with an adaptive adjustment of predictive step size according to localization fluctuation is proposed, which alleviates the disadvantage of a large amount of the MPC calculation and improves the stability of the control system in dynamic scenes. Finally, verification experiments of the real-life mobile robot are offered to verify the effectiveness of the presented MPC method. Additionally, compared with PID, the tracking distance and angle error of the proposed method decrease by 74.3% and 95.3%, respectively.

Model predictive optimization for imitation learning from demonstrations

“Motion generation by imitating” enables a robot to generate its trajectory in a new environment. Research works on dynamic movement primitives (DMP) has reported promising results, with good imitation effect and convergence to the target. However, DMP still has issues such as learning from multiple demonstrations for different initial conditions and achieving obstacle avoidance considering the distribution and motion of obstacles. One of the effective solutions is combining DMP and model predictive control (MPC). The imitation process was transformed into a receding horizon planning procedure, letting the robot to learn more from nearer demonstrations. It is solved as an optimization problem with obstacles modeled as constraints. However, its drawback includes the heavy computation burden, which can be even aggravated in a multi-obstacle scenario where complicated constraints occur. Thus, in this paper, we propose an enhanced MPDMP+ method that combines the advantages of MPC with potential function for both multi-demonstration imitation and multi-obstacle avoidance effect. A proximal augmented Lagrangian method is proposed to solve the optimization problem. This proposed method has a faster convergence rate and small errors. We conducted the simulation and robot experiments for imitation learning for obstacle avoidance scenarios. Our results illustrate the superior performance of the proposed method.

Design and experiments of a small resonant inchworm piezoelectric robot

Design and experiments of a small resonant inchworm piezoelectric robot