Robot Experiments Research Articles

Rethinking the four-wing problem in plesiosaur swimming using bio-inspired decentralized control

A locomotor system that can function across different environmental conditions and produce a range of performances is one of the most critical abilities needed for extant and extinct animals in order to survive and maximise their competitive fitness. Recent engineering-inspired paleontological studies have reconstructed feasible locomotor patterns in extinct animals. However, it is still challenging to describe how extinct animals successfully adjust their locomotor patterns to new situations (e.g., changes in locomotor speed and morphology). In this study, we develop a novel reconstruction method based on a bio-inspired control system. We focus on plesiosaurs, an extinct aquatic reptile group which has two pairs of flipper-shaped limbs, and demonstrate that a highly optimised, flexible locomotor pattern for all four flippers can be reconstructed based on a decentralized control scheme formulated from extant animals’ locomotion. The results of our robotic experiments show that a simple, local sensory feedback mechanism allows the plesiosaur-like robot to exploit the fluid flow between the flippers and generate efficient swimming patterns in response to changes in locomotor conditions. Our new method provides further evidence how decentralized control systems can produce a pathway between extinct and extant animals in order to understand the how extinct animals moved and how these movement patterns may have evolved.

GRID-FAST: A Grid-based Intersection Detection for Fast Semantic Topometric Mapping

This article introduces a novel approach to constructing a topometric map that allows for efficient navigation and decision-making in mobile robotics applications. The method generates the topometric map from a 2D grid-based map. The topometric map segments areas of the input map into different structural-semantic classes: intersections, pathways, dead ends, and pathways leading to unexplored areas. This method is grounded in a new technique for intersection detection that identifies the area and the openings of intersections in a semantically meaningful way. The framework introduces two levels of pre-filtering with minimal computational cost to eliminate small openings and objects from the map which are unimportant in the context of high-level map segmentation and decision making. The topological map generated by GRID-FAST enables fast navigation in large-scale environments, and the structural semantics can aid in mission planning, autonomous exploration, and human-to-robot cooperation. The efficacy of the proposed method is demonstrated through validation on real maps gathered from robotic experiments: 1) a structured indoor environment, 2) an unstructured cave-like subterranean environment, and 3) a large-scale outdoor environment, which comprises pathways, buildings, and scattered objects. Additionally, the proposed framework has been compared with state-of-the-art topological mapping solutions and is able to produce a topometric and topological map with up to 92% fewer nodes than the next best solution. The method proposed in this article has been implemented in the robotics framework ROS and is open-sourced. The code is available at: https://github.com/LTU-RAI/GRID-FAST.

Research on the influence of cutter overhang length on robotic milling chatter stability

Serial industrial robots are widely in demand in the field of large-scale component processing and manufacturing. However, the structure characteristic of serial robots makes it easy to generate chatter during milling and the overhang length of milling cutter has an important influence on the stability of robotic milling. Milling cutter overhang length refers to the length from the tip of the cutter to the protrusion of the spindle, to study the influence of milling cutter overhang length on the stability behavior of robotic milling, the modal parameters of milling cutter under different overhang length were obtained by hammer experiment, and its dynamic characteristics were analyzed. The stability behavior for robotic milling under different cutter overhang lengths was analyzed by using the stability lobe diagrams, and the stability lobe diagrams were verified by robotic milling experiments. The results show that the rigidity and natural frequency of milling cutter decrease with the increase of overhang length. There is a big gap between the stability lobe diagrams and the actual machining state when the cutter overhang length is short, and the low frequency vibration is easy to occur in the robotic milling process when the spindle speed is low, which is most probably because that the absorbed vibration energy by the cutter has been transferred to the robot body before the milling cutter vibrates. When the cutter overhang is increased and the feed direction is along the y-axis of the robot global coordinate system, the stability lobe diagrams by considering the multi-mode coupling effect is more consistent with the actual milling state.

A concurrent learning approach to monocular vision range regulation of leader/follower systems

This paper explores range and bearing angle regulation of a leader–follower using monocular vision. The main challenge is that monocular vision does not directly provide a range measurement. The contribution is a novel concurrent learning (CL) approach, called CL Subtended Angle and Bearing Estimator for Relative pose (CL-SABER), which achieves range regulation without communication, persistency of excitation or known geometry and is demonstrated on a physical, robot platform. A history stack estimates target size which augments the Kalman filter (KF) with a range pseudomeasurement. The target is followed to scale without drift, persistency of excitation requirements, prior knowledge, or additional measurements. Finite excitation is required to achieve parameter convergence and perform steady-state regulation using CL-SABER. Evaluation using simulation and mobile robot experiments in special Euclidean planar space (SE(2)) show that the new method provides stable and consistent range regulation, as demonstrated by the inter-rater reliability, including in noisy and high leader acceleration environments.

Finite-time optimal control for a class of nonlinear systems with performance constraints via critic-only ADP: Theory and experiments

This paper addresses the optimal control problem within the framework of adaptive dynamic programming (ADP) for a class of nonlinear systems subjected to performance constraints. A new finite-time optimal control scheme is developed to stabilize the system by using the critic-only neural network ADP method. Compared with the existing ADP-based optimal control methods with uniformly ultimately bounded stability, the provided control scheme ensures that the controlled system's state and neural network weight estimation error are finite-time stable. It can ensure optimality, prescribed performance, and finite-time stability of the closed-loop control system simultaneously through an integration of ADP, the prescribed performance control technique, and Lyapunov theory. The designed adaptive neural network weight update law can relax the persisting excitation condition. The proposed control scheme is implemented on a robotic experiment platform to achieve trajectory tracking and verify its effectiveness.

Adaptive performance optimal control for flexible-joint robots with random noises: Design and experiment

This study developes a flexible performance optimal control scheme via reinforcement learning strategy and event-triggered mechanism for flexible-joint robots with random noise and non-affine input. It is notable that an event-triggered optimization mechanism is developed, which meets the optimality principle and saves communication resources. Nevertheless, the existing event-triggered strategy is unable to handle non-affine input, which limits the applicability of this method. To overcome the above problems, a modified event-triggered mechanism is proposed. At the same time, the optimal solution of the system is given by an optimized control algorithm based on the improved performance index function. In the controller design, neural network is used to deal with random disturbances and uncertainties, and an adaptive law is designed to replace the identifier structure. Besides, a flexible prescribed performance function is constructed to yield multiple prescribed performance behaviors by adjusting the key parameters, while the tracking error is stayed within a prescribed boundary. Finally, the effectiveness of the proposed control scheme is further demonstrated by simulation and the experiment of the 2-link flexible-joint robot on the Quanser platform.

Differentiable modeling and optimization of non-aqueous Li-based battery electrolyte solutions using geometric deep learning

Electrolytes play a critical role in designing next-generation battery systems, by allowing efficient ion transfer, preventing charge transfer, and stabilizing electrode-electrolyte interfaces. In this work, we develop a differentiable geometric deep learning (GDL) model for chemical mixtures, DiffMix, which is applied in guiding robotic experimentation and optimization towards fast-charging battery electrolytes. In particular, we extend mixture thermodynamic and transport laws by creating GDL-learnable physical coefficients. We evaluate our model with mixture thermodynamics and ion transport properties, where we show improved prediction accuracy and model robustness of DiffMix than its purely data-driven variants. Furthermore, with a robotic experimentation setup, Clio, we improve ionic conductivity of electrolytes by over 18.8% within 10 experimental steps, via differentiable optimization built on DiffMix gradients. By combining GDL, mixture physics laws, and robotic experimentation, DiffMix expands the predictive modeling methods for chemical mixtures and enables efficient optimization in large chemical spaces.

Analysis of Initial Outcomes of Gas-insufflation One-step Single-port Transaxillary (GOSTA) Robotic Thyroidectomy for Thyroid Cancer.

This study compared the initial outcomes of gas-insufflation one-step single-port transaxillary (GOSTA) robotic thyroidectomy with traditional open thyroidectomy for thyroid cancer at a single institution. A retrospective analysis was conducted on 77 patients who underwent thyroidectomy for differentiated thyroid cancer from January to June 2024. Exclusion criteria included benign nodules, Graves' disease, and previous thyroid surgeries. Two surgeons performed the procedures, with one having no prior robotic surgery experience. Of the 77 patients, 48 underwent open thyroidectomy and 29 underwent GOSTA thyroidectomy. The GOSTA group had a significantly lower mean age (40.1 vs. 47.6 years, p=0.002) and a higher proportion of female patients (p=0.040). The open group patients had more harvested lymph nodes than the GOSTA group patients (7.9 vs. 2.4, p<0.001). The GOSTA group patients had longer operation time (156.4 vs. 80.6 min, p<0.001), and had extended hospital stay than the open group patients (5.9 vs. 3.4 days, p<0.001). Complication rates were similar between the groups. GOSTA robotic thyroidectomy provides comparable safety and effectiveness to open thyroidectomy, with improved cosmetic outcomes despite longer operation times and hospital stays. This technique is feasible for surgeons without prior robotic experience, offering a viable alternative for patients prioritizing cosmetic results.

Hydrodynamic Simulation and Experiment of a Self-Adaptive Amphibious Robot Driven by Tracks and Bionic Fins.

Amphibious robots have broad prospects in the fields of industry, defense, and transportation. To improve the propulsion performance and reduce operation complexity, a novel bionic amphibious robot, namely AmphiFinbot-II, is presented in this paper. The swimming and walking components adopt a compound drive mechanism, enabling simultaneous control for the rotation of the track and the wave-like motion of the undulating fin. The robot employs different propulsion methods but utilizes the same operation strategy, eliminating the need for mode switching. The structure and the locomotion principle are introduced. The performance of the robot in different motion patterns was analyzed via computational fluid dynamics simulation. The simulation results verified the feasibility of the wave-like swimming mechanism. Physical experiments were conducted for both land and underwater motion, and the results were consistent with the simulation regulation. Both the underwater linear and angular velocity were proportional to the undulating frequency. The robot's maximum linear speed and steering speed on land were 2.26 m/s (2.79 BL/s) and 442°/s, respectively, while the maximum speeds underwater were 0.54 m/s (0.67 BL/s) and 84°/s, respectively. The research findings indicate that the robot possesses outstanding amphibious motion capabilities and a simplistic yet unified control approach, thereby validating the feasibility of the robot's design scheme, and offering a novel concept for the development of high-performance and self-contained amphibious robots.

A Gecko‐Inspired Robot Using Novel Variable‐Stiffness Adhesive Paw Can Climb on Rough/Smooth Surfaces in Microgravity

Space‐wall‐climbing robots face the challenge of stably attaching to and moving on spacecraft surfaces, which include smooth flat areas and rough intricate surfaces. Although adhesion‐based wall‐climbing robots demonstrate stable climbing on smooth surfaces in outer space, there is scarce research on their stable adhesion on rough surfaces within a microgravity environment. A novel adhesive material is developed inspired by the adhesion mechanism and locomotion of the Gekko gecko. This material exhibits exceptional adhesion across various materials and surface roughness. A variable‐stiffness gecko‐inspired paw is engineered, generating substantial adhesion forces while minimizing detachment forces. Impressively, this paw generates up to 180 N of adhesion force on smooth surfaces and achieves detachment without external forces. By integrating such variable‐stiffness paws with a wall‐climbing robot, a gecko‐inspired robot effectively operating in a microgravity environment is created. The robotic satellite surface climbing experiments and robotic satellite capture experiments are conducted using a simulated microgravity environment and a satellite model. The results unequivocally demonstrate the gecko‐inspired robot's proficiency in executing various functions, including stable motion and capture on both smooth and rough spacecraft surfaces within a microgravity environment. These experiments underscore the potential of adhesion‐based gecko‐inspired robots for in‐orbit services and spacecraft capture and recovery.

Initial Experience of Robot Assisted Laparoscopic Pyeloplasty for Ureteropelvic Junction Obstruction Using the Hinotori Surgical Robot System.

This study aimed to investigate the perioperative outcomes of robot-assisted laparoscopic pyeloplasty (RLP) using the recently launched hinotori surgical robot system. This retrospective study compared the perioperative outcomes of 11 consecutive patients who underwent RLP with the hinotori surgical robot system from October 2022 to March 2024 (hinotori group) and 30 consecutive patients who underwent RLP with the da Vinci system from March 2019 to September 2022 (da Vinci group). The patient characteristics of the groups were similar. The median operative times in the hinotori and da Vinci groups were 236.0 and 231.5min, respectively (p=0.480). The success rates were 100.0% and 96.7%, respectively (p=1.000). Clavien-Dindo grade ≥3 complications occurred in one patient (9.1%) in the hinotori group and one patient (3.3%) in the da Vinci group (p=0.470). The perioperative outcomes in the hinotori group were not inferior to those in the da Vinci group.

Application of Artificial Neuromolecular System in Robotic Arm Control to Assist Progressive Rehabilitation for Upper Extremity Stroke Patients

Freedom of movement of the hands is the most desired hope of stroke patients. However, stroke recovery is a long, long road for many patients. If artificial intelligence can assist human arm movement, the possibility of stroke patients returning to normal hand movement might be significantly increased. This study uses the artificial neuromolecular system (ANM system) developed in our laboratory as the core of motion control, in an attempt to learn to control the mechanical arm to produce actions similar to human rehabilitation training and the transition between different activities. This research adopts two methods. The first is hypothetical exploration, the so-called “artificial world” simulation method. The detailed approach uses the V-REP (Virtual Robot Experimentation Platform) to conduct different experimental runs to capture relevant data. Our policy is to establish an action database systematically to a certain extent. From these data, we use the ANM system with self-organization and learning capabilities to develop the relationship between these actions and establish the possibility of conversion between different activities. The second method of this study is to use the data from a hospital in Toronto, Canada. Our experimental results show that the ANM system can continuously learn for problem-solving. In addition, our three experimental results of adaptive learning, transfer learning, and cross-task learning further confirm that the ANM system can use previously learned systems to complete the delivered tasks through autonomous learning (instead of learning from scratch).

Beyond the Learning Curve of Robot-Assisted Navigation Spine Surgery: Refinement of Outcomes With Extended Experience.

Objective This study assessed whether robotic-assisted navigation (RAN) spine surgery outcomes, including operative time and pedicle screw accuracy, continue to improve with extended experience beyond 200 cases. Methods This is a retrospective review of 60 patients who underwent lumbosacral transforaminal interbody fusion using RAN. Patients were segmented into three groups of 20 consecutive cases each. The first group represented a surgical performance baseline leading up to the investigating surgeon's 200th RAN case. The subsequent two groups were selected beyond the 200th case with an average of 15 cases between groups. Pedicle screw accuracy and intraoperative outcomes were assessed. Statistical results were significant if p<0.05. Results Measures of surgical efficiency significantly improved beyond the investigating surgeon's 200th RAN case. As case number increased, the following parameters significantly decreased: registration time (group 1: 16.9±6.5, group 2: 12.9±3.0, group 3: 8.7±1.6 minutes; p<0.05), screw insertion time (group 1: 14.9±3.5, group 2: 10.9±2.0, group 3: 8.4±2.7 minutes; p<0.05), and total operative time significantly decreased from group 1 (175.9±58.2 minutes) to group 2 (135.8±23.9 minutes) (p=0.013) with a non-significant decrease to group 3 (121.5±32.3 minutes). Accuracy (Grade = A) significantly increased across groups (group 1: 87%, group 2: 94%, group 3: 98%; p=0.024). Group 1 had the highest misplacement rate of 3.7% (4/108 screws). The overall misplacement rate was 1.4% (4/290 screws) (Grade C-E). There was a higher rate of lateral screw misplacement compared to medial misplacement. Conclusion Even with a small number of initial cases, RAN spine surgery can consistently be performed with high accuracy and acceptable intraoperative outcomes. However, this study demonstrated refined outcomes with extended robotic experience.

Development of a Novel Retinal Surgery Robot Based on Spatial Variable Remote Center-of-Motion Mechanism

Abstract This article reports the design, modeling, and experiments of a novel retinal surgery robot based on spatial variable remote center-of-motion (RCM) mechanism. The general design criteria for parallel mechanisms are proposed, and the planar five-bar mechanisms are evaluated and selected. The planar-spatial evolution process, including the parallel connection of the planar mechanism and the equivalent substitution of joints, is adopted to develop a spatial variable RCM mechanism and then the robot. The mobility and singularity of the robot are analyzed, and the forward/inverse kinematics and workspace are modeled. Dimension optimization is conducted based on a comprehensive performance indicator that characterizes the motion range of linear actuators and the global dexterity performance index of robot. The prototyped robot is fabricated and assembled, and the kinematic calibration is performed. The position error of end-effector is within 34 μm, and both the position error and deviation of the RCM point are within 23 μm. The robot is demonstrated to reach the desired position and execute the RCM motion with high precision simultaneously.

Wing-strain-based flight control of flapping-wing drones through reinforcement learning

Although drone technology has advanced rapidly, replicating the dynamic control and wind-sensing abilities of biological flight is still beyond reach. Biological studies reveal that insect wings are equipped with mechanoreceptors known as campaniform sensilla, which detect complex aerodynamic loads critical for flight agility. By leveraging robotic experiments designed to mimic these biological systems, we confirm that wing strain provides crucial information about the drone’s attitude angle, as well as the direction and velocity of the wind. We introduce a wing-strain-based flight controller that employs the aerodynamic forces exerted on a flapping drone’s wings to deduce vital flight data such as attitude and airflow without accelerometers and gyroscopic sensors. The present work spans five key experiments: initial validation of the wing strain sensor system for state information provision, control in a single degree of freedom movement environment with changing winds, control in a two degrees of freedom movement environment for gravitational attitude adjustment, a test for position control in windy conditions and a demonstration of precise flight path manipulation in a windless condition using only wing strain sensors. We have successfully demonstrated control of a flapping drone in various environments using only wing strain sensors, with the aid of a reinforcement-learning-driven flight controller. The demonstrated adaptability to environmental shifts will be beneficial across varied applications, from gust resistance to wind-assisted flight for autonomous flying robots.

Design, Analysis and Experiment of a Modular Deployable Continuum Robot

Continuum robots, possessing great flexibility, can accomplish tasks in complex work scenes, regarded as an important direction in robotics. However, the current continuum robots are not satisfying enough in terms of fabrication and maintenance, and their workspace is limited by structure and other aspects. In this paper, to address the above problems, a modular deployable robot, which adopts an origami structure instead of a flexible hinge, is proposed. A fabrication method is innovated, the Spherical Linkage Parallel Mechanism (SLPM) unit is optimized, and the installation and fabrication process of the robot is simplified through modularization. The forward kinematics and inverse kinematics of the robot and its workspace are analyzed by using the screw theory. The prototype of the robot is constructed, and its folding performance, bending performance, and motion accuracy are tested, and the error analysis and compensation optimization are carried out. After the optimization, the position error of the robot is reduced by about 65%, and the standard deviation is greatly lowered, which effectively improves the motion accuracy and stability of the robot.

Analysis of structural parameters of stirred engineered composite robots in high-risk environments

Facing the safety of firefighting operators in high-risk environments, this paper investigates a robot for mixing engineering composites, which can automatically monitor the homogeneity of the mixing and, together with an automatic material feeding system, can realize automated production. This Study is based on the numerical simulation method that explored the discrete element model of engineering composites, carried out the multifactorial structural parameter orthogonal simulation experiment of the mixing robot in EDEM software, investigated the effects of different structural parameters on mixing time, mixing uniformity and productivity, proposed a mixing robot concrete uniformity intelligent identification algorithm.

Low-frequency chatter suppression for robotic milling using a novel MRF absorber

Robotic milling has a unique advantage for large and complex parts. However, it is extremely prone to low-frequency chatter due to the robot structure mode. In robotic milling, low-frequency chatter has a huge impact on machining quality and efficiency. In this paper, a novel MRF (Magnetorheological fluid) absorber is used to suppress low-frequency chatter during robotic milling based on the proposed low-frequency chatter suppression strategy. First, the GPR (Gaussian process regression) model is used to predict the impulse response function based on a few measured impulse response functions. Next, a cutting force model is developed considering the tool runout and the robot structure mode. The parameters and coefficients of the cutting force model are optimized by the measured cutting forces. Then, the low-frequency chatter frequency is predicted considering the modal coupling effect based on the predicted impulse response function and the simulated cutting force. Finally, an MRF absorber is designed based on the chatter frequency range. The controlled storage modulus of the MRF in the pre-yield region enables a wider frequency shift characteristic of the designed MRF absorber. The MRF absorber controls the input current based on the predicted low-frequency chatter frequency to achieve chatter suppression during robotic milling. Robotic milling experiments verified that the MRF absorber can effectively suppress low-frequency chatter. The experimental results show that the Energy Ratio Chatter Indicator of the industrial robot with MRF absorber is less than 0.1 under different milling conditions.

A Terminal Residual Vibration Suppression Method of a Robot Based on Joint Trajectory Optimization

Vibration problems have become one of the most important factors affecting robot performance. To this end, a terminal residual vibration suppression method based on joint trajectory optimization is proposed to improve the accuracy and stability of robot motion. Firstly, based on the characteristics of the friction nonlinearity due to joint coupling and physical feasibility of dynamic parameters, a semidefinite programming method is used to identify dynamic parameters with actual physical meaning, thereby obtaining an accurate dynamic model. Then, based on the result of the residual vibration time domain analysis, a joint trajectory optimization model with the goal of minimizing joint tracking error is established. The Chebyshev collocation method is used to discretize the optimization model. The dynamic model is used as the optimization constraint, and barycentric interpolation is used to obtain the optimized joint motion trajectory. Finally, industrial robot experiments prove that the vibration suppression method proposed in this article can reduce the maximum acceleration amplitude of residual vibration by 62% and the vibration duration by 71%. Compared with the input shaping method, the method proposed in this paper can reduce the terminal residual vibration more effectively and ensure the consistency of running time and trajectory.

Skill progress during a dedicated societal robotic surgery training curriculum including several robotic surgery platforms.

Robot-assisted procedures are increasingly common, and several systems are available for thoraco-abdominal surgery. Specific structured training is necessary, while access to these systems is still limited. This study aimed to assess surgeons' skill progress during consecutive training days of a curriculum with exposure to different robotic systems. This prospective observational study enrolled 47 surgeons with anonymized analysis of SimNow™ simulator performance scores and dedicated questionnaires after written consent. The primary outcome was the overall score, based on economy of motion, time to complete the exercise, and penalty for errors. Course participants in 2022-2023 had chosen 2 full hands-on days on Da Vinci® consoles with either virtual reality (VR) simulation training using the SimNow (n = 21, 44.7%) or digestive surgery procedures with a live animal model (n = 26, 55.3%). In all participants, training on Da Vinci® systems included console functions and principles of docking, camera, and instrument use for console and procedural training. They additionally had access to introductory dry-lab and VR simulator exercises on the Versius, HugoTMRAS, and Dexter systems and to VR exercises on the ROBOTiS simulator. The participants (16F/31M, median age 40years, range 29-58) from various surgical specialties (general/visceral/vascular) had no (n = 35, 74.5%) or little (n = 12, 25.5%) robotic experience including bedside assistance only and 20 (42.6%) had robotic simulator experience. The demographic variables fully completed by 44/47 participants (93.6%) and choice of module had no significant impact on the primary outcome. The considerable performance improvement from days 1 to 2 was exemplified by a significantly increased economy of motion and decreased amount of excessive force. Robotic surgical training is increasingly complex with several systems on the market. Within a dedicated robotic surgery curriculum and based on integrated performance metrics, a significant improvement of skill levels was observed in a relatively short period of time.