Movement Of Robot Research Articles

Phase-amplitude reduction-based imitation learning

In this study, we propose the use of the phase-amplitude reduction method to construct an imitation learning framework. Imitating human movement trajectories is recognized as a promising strategy for generating a range of human-like robot movements. Unlike previous dynamical system-based imitation learning approaches, our proposed method allows the robot not only to imitate a limit cycle trajectory but also to replicate the transient movement from the initial or disturbed state to the limit cycle. Consequently, our method offers a safer imitation learning approach that avoids generating unpredictable motions immediately after disturbances or from a specified initial state. We first validated our proposed method by reconstructing a simple limit-cycle attractor. We then compared the proposed approach with a conventional method on a lemniscate trajectory tracking task with a simulated robot arm. Our findings confirm that our proposed method can more accurately generate transient movements to converge on a target periodic attractor compared to the previous standard approach. Subsequently, we applied our method to a real robot arm to imitate periodic human movements.

MOBILE ROBOT NAVIGATION BASED ON LASER RANGE-METRY METHODS

The article is devoted to solving the problem of finding people who are under the debris of destroyed buildings, which requires an urgent solution to ensure an effective rescue operation. The primary focus of this research is the development and refinement of a navigation system for a mobile rescue robot. This paper specifically explores localization techniques and methods for constructing a map of an unknown environment, which are crucial for the robot’s effective operation in complex and dynamic settings. The overarching goal of the study is to identify the most adaptable and precise navigation method that can effectively manage the movement of a mobile robot in environments characterized by unpredictable and shifting obstacles. To solve the tasks, modern methods of laser telemetry and SLAM methods (Simultaneous Localization and Mapping) are used in the work, which are used to accurately create a map of the territory and localize the robot on it. The research proposes an innovative algorithm for the navigation system of a mobile robot, which leverages laser telemetry to meticulously scan the surrounding area. It has been shown that scanning process is essential for real-time data acquisition, enabling the robot to make informed decisions regarding its movement. It is stated that, the proposed lidar-based navigation method is particularly advantageous as it facilitates the creation of a detailed spatial map, even in environments where the layout is not known in advance. This approach allows for the precise determination of the robot’s position within the mapped space, ensuring that the robot can navigate effectively and avoid potential hazards. It is proved that, the integration of these advanced techniques significantly enhances the robot’s ability to perform in search and rescue missions, where the accurate and timely localization of trapped individuals is of paramount importance.

SincNet-Based Speaker Identification for Robotic Environments with Varying Human–Robot Interaction Distance

As human–robot interaction (HRI) becomes increasingly significant, various studies have focused on speaker recognition. However, few studies have explored this topic in the specific environment of home service robots. Notably, most existing research relies on databases composed of English-language data, while studies utilizing Korean speech data are exceedingly scarce. This gap underscores the need for research on speaker recognition in robotic environments, specifically using Korean data. In response, this paper conducts experiments using a speaker recognition database tailored to the Korean language and set in a robotic context. The database includes noise generated by robot movement as well as common environmental noise, accounting for variable distances between humans and robots, which are partitioned accordingly. The deep learning model employed is SincNet, with experiments conducted under two settings for the SincNet filter parameters: one with learnable parameters and the other with fixed values. After training the model with data collected at varying distances, performance was tested across these distances. Experimental results indicate that SincNet with learnable parameters achieved a peak accuracy of 99%.

The phenomenon of spasticity - what is hidden behind the simple name?

Spasticity is one of the current conditions in people with consequences of damage to the central nervous system and makes a significant contribution to the patient’s recovery process. The purpose of the study is to verify the phenomenon of “spasticity” and study the possibility of its objective diagnosis. To search for literature sources, the PubMed and E-library databases were used. Based on an analysis of available sources, there is reason to abandon the widespread use of the term “spasticity”, replacing it with “deforming spastic paresis”, which will cover various pathophysiological features and variants of this condition, and accordingly revise both diagnostic and therapeutic approaches to this condition. The phenomenon of deforming spastic paresis can manifest itself with varying degrees of severity and in different phases of movement, and for different muscles in different ways. Despite the active development of capabilities from instrumental research methods, there are currently no unified and universal algorithms for assessing deforming spastic paresis. The most adequate methods seem to be those that use robotic test movements for testing, which makes it possible to standardize the method and make it more convenient for specialist assessment.

Operator Experience With Robot-Assisted Laparoscopic Rectal Resection and the Impact of Using an Extra Arm on Surgical Outcomes.

Although robot-assisted surgery has been rapidly gaining popularity in recent years, few reports have examined the impact of individual robotic arm movements on surgical outcomes. This study investigated the relationship between extra arm (EA) usage and experience of surgeons in robot-assisted surgery. A total of 176 robot-assisted rectal resections were performed by three surgeons from 2018 to 2022. Each surgeon's first 20 cases categorized in the Early group and the 21st-40th cases in the Late group. Sixty cases in the Early group and 60 cases in the Late group were included to compare patient background, EA motion, and surgical outcomes. More anastomoses were performed closer to the anus in the Late group (p = 0.001). Console time was significantly shorter in the Late group (196 min vs. 153 min, p = 0.020). EA injuries, improper grasping, and intraoperative problems (intraoperative bleeding, misidentification of layers) were significantly less frequent in the Late group (p = 0.001). As experience with robot-assisted surgery increases, appropriate and safe use of an EA was achieved, and operative time and intraoperative problems were reduced.

Robot Task-Constrained Optimization and Adaptation with Probabilistic Movement Primitives

Enabling a robot to learn skills from a human and adapt to different task scenarios will enable the use of robots in manufacturing to improve efficiency. Movement Primitives (MPs) are prominent tools for encoding skills. This paper investigates how to learn MPs from a small number of human demonstrations and adapt to different task constraints, including waypoints, joint limits, virtual walls, and obstacles. Probabilistic Movement Primitives (ProMPs) model movements with distributions, thus providing the robot with additional freedom for task execution. We provide the robot with three modes to move, with only one human demonstration required for each mode. We propose an improved via-point generalization method to generalize smooth trajectories with encoded ProMPs. In addition, we present an effective task-constrained optimization method that incorporates all task constraints analytically into a probabilistic framework. We separate ProMPs as Gaussians at each timestep and minimize Kullback–Leibler (KL) divergence, with a gradient ascent–descent algorithm performed to obtain optimized ProMPs. Given optimized ProMPs, we outline a unified robot movement adaptation method for extending from a single obstacle to multiple obstacles. We validated our approach with a 7-DOF Xarm robot using a series of movement adaptation experiments.

Non-holonomic constraints-based adaptive trajectory tracking controller for a multi-joint snake robot

The multi-joint snake robot is poised to become a crucial asset for investigation, surveillance, and attack in national defense and military applications. This paper addresses the trajectory tracking challenge of a multi-joint snake robot characterized by high redundancy and multiple degrees of freedom in the plane. We propose an adaptive trajectory tracking controller that accounts for non-holonomic constraints. This controller estimates unknown environmental parameters, effectively mitigating the negative impacts of uncertain and time-varying conditions during robot movement, thereby ensuring stability. A suitable Lyapunov function is identified to confirm the controller’s stability. The trajectory tracking performance of the robot is thoroughly analyzed through simulations and experiments, demonstrating the effectiveness of the proposed adaptive controller.

Exoskeleton Robot Based on Brain-Computer Interface

Brain-computer interface (BCI) technology represents a means of facilitating human-computer interaction. One of the most widely accepted paradigms of brain-computer interface is motor imagery, which enables the recognition of electroencephalogram (EEG) signals generated in a specific brain region by imagining the movement of a limb. Following the acquisition, preprocessing, feature processing, and signal classification of the EEG signals, the complex signals are accurately recognized. Therefore, by creating a control system that translates the recognized EEG signals into movement commands for the robot and transmits them to the robot, it is possible to control the robot's movements by motor imagery. The convolutional neural network is the most popular signal processing algorithm due to its high EEG recognition accuracy, excellent performance in feature extraction, and superior performance in end-to-end learning. The convolutional neural network is an optimal method for signal processing in robot control. This makes CNN an optimal choice for processing EEG signals in robot control, enhancing both the effectiveness and user experience of BCI systems by enabling more intuitive and responsive interactions with robotic devices.

Human prior knowledge estimation from movement cues for information-based control of mobile robots during search

Robotic search often involves teleoperating vehicles into unknown environments. In such scenarios, prior knowledge of target location or environmental map may be a viable resource to tap into and control other autonomous robots in the vicinity towards an improved search performance. In this paper, we test the hypothesis that despite having the same skill, prior knowledge of target or environment affects teleoperator actions, and such knowledge can therefore be inferred through robot movement. To investigate whether prior knowledge can improve human-robot team performance, we next evaluate an adaptive mutual-information blending strategy that admits a time-dependent weighting for steering autonomous robots. Human-subject experiments show that several features including distance travelled by the teleoperated robot, time spent staying still, speed, and turn rate, all depend on the level of prior knowledge and that absence of prior knowledge increased workload. Building on these results, we identified distance travelled and time spent staying still as movement cues that can be used to robustly infer prior knowledge. Simulations where an autonomous robot accompanied a human teleoperated robot revealed that whereas time to find the target was similar across all information-based search strategies, adaptive strategies that acted on movement cues found the target sooner more often than a single human teleoperator compared to non-adaptive strategies. This gain is diluted with number of robots, likely due to the limited size of the search environment. Results from this work set the stage for developing knowledge-aware control algorithms for autonomous robots in collaborative human-robot teams.

Lower limb muscle activity during neurointerface control: neurointerface based on motor imagery of walking

The question of the activity of muscles that provide the realization of imaginary movement is essential in the rehabilitation of motor disorders using neurointerfaces. The literature data on this issue are contradictory. The paper analyzes the EMG activity of the shin and thigh muscles of 40 healthy volunteers when working with a neurointerface based on kinesthetic motor imagery of walking in place and supplemented with the «Biokin» robotic limb movement device (mechanotherapy), activated in case of successful motor imagery. It is shown that working with a neurointerface, on average for subjects, leads to an increase in muscle activity when motor imagery of walking compared to rest, and activation of the mechanical training device (AM) further increases muscle activity, with its effect being more pronounced in the muscles of the leg from which motor imagery of walking begins. The nature of muscle reactions to the task of motor imagery of walking is individual. AM when working with a neurointerface, the number of subjects with pronounced EMG activity increases, as does the number of significant correlations between the activity of the muscles of the lower limbs. Thus, the use of neurointerfaces based on motor imagery of walking and the addition of AM as feedback allows activating the muscles of the lower extremities, which is important in clinical practice in the rehabilitation of movements.

The Intelligent Decision-making and Collaboration Mechanism Design of the Soccer Robot System

The robot soccer competition integrates technologies such as vision, wireless communication, control, multi-sensor fusion, strategy, and simulation, with a focus on the intelligent soccer robot path decision-making subsystem. This paper briefly analyzes decision-making methods such as role allocation, time-region control, and self-tactics, with a primary focus on the top-down hierarchical reasoning model, emphasizing robot collaboration and reflecting the concept of multi-agent cooperation. During the implementation process, the code and functions of the decision subsystem are described in detail, and examples are provided to demonstrate how to achieve robot decision-making, tactical applications, and movement. Additionally, the effectiveness and performance of the system are verified through algorithm testing.

Use of Sensorless BLDC Motors for the Hydraulic Power Unit of an Oil Pipeline Inspection Robot

The challenges encountered in the oil and gas industry require complex solutions and development effort. Related to oil lifting and gathering, it concerns severe conditions as space restraints. Given the confined space inside oil pipelines, robots for inspection requires technologies compatible with it, increasing its complexity, where the minimum required and maximum effective components of the robot is crucial for its construction. In this scenario, the use of sensorless BLDC motors becomes a key point, increasing robustness and reducing harness, being important factors in terms of feasibility, due to connector limitations. Thus, the more motors used — which contributes to the robot's movement — the more advantageous this approach becomes, considering that there is no need for sensing signals for controlling. This work presents an extended version of a paper published on COBEP/SPEC 2023, with new achievements: development of a baseboard motor controller, using TMS320F28027F MCU and DRV8353RSRGZR driver; practical results for sensorless controlling motor; hardware thermal analysis. As results, considering the set point of 5000 rpm rotation speed and a load pressure of 300 bar, the control system obtained a maximum error of 2%.

ScAlN PMUTs Based on Flexurally Suspended Membrane for Long-Range Detection.

Piezoelectric micromachined ultrasonic transducers (PMUTs) have been widely applied in distance sensing applications. However, the rapid movement of miniature robots in complex environments necessitates higher ranging capabilities from sensors, making the enhancement of PMUT sensing distance critically important. In this paper, a scandium-doped aluminum nitride (ScAlN) PMUT based on a flexurally suspended membrane is proposed. Unlike the traditional fully clamped design, the PMUT incorporates a partially clamped membrane, thereby extending the vibration displacement and enhancing the output sound pressure. Experimental results demonstrate that at a resonant frequency of 78 kHz, a single PMUT generates a sound pressure level (SPL) of 112.2 dB at a distance of 10 mm and achieves a high receiving sensitivity of 12.3 mV/Pa. Distance testing reveals that a single PMUT equipped with a horn can achieve a record-breaking distance sensing range of 11.2 m when used alongside a device capable of simultaneously transmitting and receiving ultrasound signals. This achievement is significant for miniaturized and integrated applications that utilize ultrasound for long-range target detection.

A high-response-frequency bimodal network polyacrylate elastomer with ultrahigh power density under low electric field

Dielectric elastomers, used as driver modules, require high power density to enable fast movement and efficient work of soft robots. Polyacrylate elastomers usually suffer from low power density under low electric fields due to limited response frequency. Here, we propose a bimodal network polyacrylate dielectric elastomer which breaks the intrinsic coupling relationship between dielectric and mechanical properties, featuring relatively high dielectric constant, low Young’s modulus, and wide driving frequency bandwidth (~200 Hz) like silicones. Therefore, an ultrahigh power density (154 W kg−1@20 MV m−1, 200 Hz) is realized at low electric field and high resonance frequency, 75 times greater than at 10 Hz. Further, a rotary motor is developed, reaching an impressive speed of 1245 rpm at 19.6 MV m−1 and 125 Hz, surpassing previous acrylate-based motors and entering the high-speed domain of silicone-based motors. These findings offer a versatile strategy to fabricate high-power-density dielectric elastomers for low-electric-field soft actuators.

Development of a Robot-Assisted Fused Deposition Modeling Process for Enhanced Additive Manufacturing

<div>This work aims to define a novel integration of 6 DOF robots with an extrusion-based 3D printing framework that strengthens the possibility of implementing control and simulation of the system in multiple degrees of freedom. Polylactic acid (PLA) is used as an extrusion material for testing, which is a thermoplastic that is biodegradable and is derived from natural lactic acid found in corn, maize, and the like. To execute the proposed framework a virtual working station for the robot was created in RoboDK. RoboDK interprets G-code from the slicing (Slic3r) software. Further analysis and experiments were performed by FANUC 2000ia 165F Industrial Robot. Different tests were performed to check the dimensional accuracy of the parts (rectangle and cylindrical). When the robot operated at 20% of its maximum speed, a bulginess was observed in the cylindrical part, causing the radius to increase from 1 cm to 1.27 cm and resulting in a thickness variation of 0.27 cm at the bulginess location. However, after optimizing the speed at 35% of its maximum speed, 100% dimensional accuracy was achieved. The integration resulted in collision-free robot and extrusion movement, flexibility, capability of making large parts, and enhanced dimensional accuracy.</div>

Heterogeneous multiple soft millirobots in three-dimensional lumens.

Miniature soft robots offer opportunities for safe and physically adaptive medical interventions in hard-to-reach regions. Deploying multiple robots could further enhance the efficacy and multifunctionality of these operations. However, multirobot deployment in physiologically relevant three-dimensional (3D) tubular structures is limited by the lack of effective mechanisms for independent control of miniature magnetic soft robots. This work presents a framework leveraging the shape-adaptive robotic design and heterogeneous resistance from robot-lumen interactions to enable magnetic multirobot control. We first compute influence and actuation regions to quantify robot movement. Subsequently, a path planning algorithm generates the trajectory of a permanent magnet for multirobot navigation in 3D lumens. Last, robots are controlled individually in multilayer lumen networks under medical imaging. Demonstrations of multilocation cargo delivery and flow diversion manifest their potential to enhance biomedical functions. This framework offers a solution to multirobot actuation benefiting applications across different miniature robotic devices in complex environments.

On the Optimal Distribution of Traction Forces in Cable Propulsors of Mobile Robots

The analysis of methods of distribution of traction forces in various propellers of mobile robotic complexes and transport vehicles is carried out. The problem of synthesizing a method for controlling the distribution of the total traction load between interconnected electric drives of mobile robot propellers, discretely interacting with the support surface, is considered. An underwater mobile robot is simulated with several "walking-like" anchor-cable propellers, which ensure the movement of the underwater mobile robot by pulling the body to the supports located on the bottom. A mathematical model of the rectilinear motion of a mobile robotic device with walking-type propellers has been compiled. A mathematical description of the electric drive of the propulsion of such a mobile robot is proposed, taking into account its kinetic transfer function. It is shown that the total traction effort realized by a mobile robot is the source of the moment of resistance in the electric drive of each mover. In this regard, coefficients characterizing the distribution of traction force have been introduced into the differential equation of electric drives. A feature of the functions of these coefficients is their dependence on the distance traveled, speed and strength of resistance to movement. To optimize the distribution of the total power load between the thrusters, a target functional has been compiled. It is shown that as such a target functional, a requirement for a minimum of total heat losses in an interconnected electric drive of propellers can be formulated. To find the minimum of the accepted functional, the Euler-Poisson equations are compiled. As an additional limitation, the condition of physical feasibility is introduced. The results of solving such an optimization problem are presented on the simplest calculation scheme of two DC electric drives, between which the load is distributed along the rectilinear movement of a solid. As a result of solving the formulated optimization problem, the dependences of control actions for electric drives (voltage for DC electric drives), graphs of changes in the target function providing optimal distribution of traction forces between them are obtained, and the optimality of such distribution is proven.

A high-effective swarm intelligence-based multi-robot cooperation method for target searching in unknown hazardous environments

To solve target searching problems for multi-robot cooperation with inaccurate target distance perception in unknown hazardous environments, a hybrid adaptive robotic particle swarm optimizer (RPSO) and grey wolf optimizer (GWO) based algorithm with continuous virtual target guidance is proposed for high effective path planning in the searching. In the initial searching stages, both the wolf behavior-generated position and the gbest position and the pbesti positions from RPSO are employed to guide the motions of robots. With the information provided by these initial robot movement paths, a geometric model is established to generate potential targets. The K-means cluster algorithm is introduced to estimate a virtual target position online from potential targets, with new robot-presenting route information to update the history path information. Then the virtual position is employed as one of the direction components to help the robots approach the actual target. In addition, to avoid mobile robots falling into local convergence, a heuristic moving direction determination scheme is utilized to make robots circumvent obstacles in swarm motions, as well as a mutual repulsion algorithm to keep them in a scattering state. Simulation experiments on different types of unknown environments with varied robot numbers and adaptability testing for a dynamic target are carried out to verify the feasibility of the proposed target searching method with comparisons to the other three famous target searching algorithms. It is verified from the results that the presented method can not only contribute a 100% success rate in all runs of searching for a stochastic dynamic target under a limited maximal velocity, but also produce both the shortest path length and minimum iterations in terms of statistical metrics over the comparative methods.

Path planning strategies for logistics robots: Integrating enhanced A‐star algorithm and DWA

AbstractPath planning is the key part in the process of transportation conducted by logistics robots, and there often exist some problems with it. The path designed is not always smooth enough and its search efficiency is low, for example. As a common global path planning algorithm, A‐star is based on the traditional algorithm, which is unable to solve the problem of uneven path in the movement of logistics robots. Through improving the heuristic function of the traditional A‐star algorithm, weighing the heuristic function dynamically, removing the redundant points of the traditional star algorithm path with Floyd algorithm, and setting a safe distance to prevent the logistics robot from collision at the same time, the path is finally curved to be more appropriate to the movement path of the logistics robot. The MATLAB simulation of A‐star algorithm before and after the improvement shows that the turning points of the advanced A‐star algorithm reduced 61.5% on average compared to the traditional algorithm. The path length decreased 2.4% and the traversing points reduced 58.5%. At the same time, the DWA algorithm introduces dynamic weight coefficients, which can dynamically adjust the weight coefficients when encountering obstacles, so as to safely reach the target point.

Simulation of a Bio-Inspired Flocking-Based Aggregation Behaviour in Swarm Robotics.

This paper presents a biologically inspired flocking-based aggregation behaviour of a swarm of mobile robots. Aggregation behaviour is essential to many swarm systems, such as swarm robotics systems, in order to accomplish complex tasks that are impossible for a single agent. In this work, we developed a robot controller using Reynolds' flocking rules to coordinate the movements of multiple e-puck robots during the aggregation process. To improve aggregation behaviour among these robots and address the scalability issues in current flocking-based aggregation approaches, we proposed using a K-means algorithm to identify clusters of agents. Using the developed controller, we simulated the aggregation behaviour among the swarm of robots. Five experiments were conducted using Webots simulation software. The performance of the developed system was evaluated under a variety of environments and conditions, such as various obstacles, agent failure, different numbers of robots, and arena sizes. The results of the experiments demonstrated that the proposed algorithm is robust and scalable. Moreover, we compared our proposed algorithm with another implementation of the flocking-based self-organizing aggregation behaviour based on Reynolds' rules in a swarm of e-puck robots. Our algorithm outperformed this method in terms of cohesion performance and aggregation completion time.