Robot Motion Research Articles

Multimodal Soft Robotic Actuation and Locomotion.

Diverse and adaptable modes of complex motion observed at different scales in living creatures are challenging to reproduce in robotic systems. Achieving dexterous movement in conventional robots can be difficult due to the many limitations of applying rigid materials. Robots based on soft materials are inherently deformable, compliant, adaptable, and adjustable, making soft robotics conducive to creating machines with complicated actuation and motion gaits. This review examines the mechanisms and modalities of actuation deformation in materials that respond to various stimuli. Then, strategies based on composite materials are considered to build toward actuators that combine multiple actuation modes for sophisticated movements. Examples across literature illustrate the development of soft actuators as free-moving, entirely soft-bodied robots with multiple locomotion gaits via careful manipulation of external stimuli. The review further highlights how the application of soft functional materials into robots with rigid components further enhances their locomotive abilities. Finally, taking advantage of the shape-morphing properties of soft materials, reconfigurable soft robots have shown the capacity for adaptive gaits that enable transition across environments with different locomotive modes for optimal efficiency. Overall, soft materials enable varied multimodal motion in actuators and robots, positioning soft robotics to make real-world applications for intricate and challenging tasks.

Mutual-cognition for proactive human–robot collaboration: A mixed reality-enabled visual reasoning-based method

Human-Robot Collaboration (HRC) is key to achieving the flexible automation required by the mass personalization trend, especially towards human-centric intelligent manufacturing. Nevertheless, existing HRC systems suffer from poor task understanding and poor ergonomic satisfaction, which impede empathetic teamwork skills in task execution. To overcome the bottleneck, a Mixed Reality (MR) and visual reasoning-based method is proposed in this research, providing mutual-cognitive task assignment for human and robotic agents’ operations. Firstly, an MR-enabled mutual-cognitive HRC architecture is proposed, with the characteristic of monitoring Digital Twins states, reasoning co-working strategies, and providing cognitive services. Secondly, a visual reasoning approach is introduced, which learns scene interpretation from the visual perception of each agent’s actions and environmental changes to make task planning strategies satisfying human–robot operation needs. Lastly, a safe, ergonomic, and proactive robot motion planning algorithm is proposed to let a robot execute generated co-working strategies, while a human operator is supported with intuitive task operation guidance in the MR environment, achieving empathetic collaboration. Through a demonstration of a disassembly task of aging Electric Vehicle Batteries, the experimental result facilitates cognitive intelligence in Proactive HRC for flexible automation.

Self-administered questionnaires enhance emotion estimation of individuals with autism spectrum disorders in a robotic interview setting.

Robots offer many unique opportunities for helping individuals with autism spectrum disorders (ASD). Determining the optimal motion of robots when interacting with individuals with ASD is important for achieving more natural human-robot interactions and for exploiting the full potential of robotic interventions. Most prior studies have used supervised machine learning (ML) of user behavioral data to enable robot perception of affective states (i.e., arousal and valence) and engagement. It has previously been suggested that including personal demographic information in the identification of individuals with ASD is important for developing an automated system to perceive individual affective states and engagement. In this study, we hypothesized that assessing self-administered questionnaire data would contribute to the development of an automated estimation of the affective state and engagement when individuals with ASD are interviewed by an Android robot, which will be linked to implementing long-term interventions and maintaining the motivation of participants. Participants sat across a table from an android robot that played the role of the interviewer. Each participant underwent a mock job interview. Twenty-five participants with ASD (males 22, females 3, average chronological age = 22.8, average IQ = 94.04) completed the experiment. We collected multimodal data (i.e., audio, motion, gaze, and self-administered questionnaire data) to train a model to correctly classify the state of individuals with ASD when interviewed by an android robot. We demonstrated the technical feasibility of using ML to enable robot perception of affect and engagement of individuals with ASD based on multimodal data. For arousal and engagement, the area under the curve (AUC) values of the model estimates and expert coding were relatively high. Overall, the AUC values of arousal, valence, and engagement were improved by including self-administered questionnaire data in the classification. These findings support the hypothesis that assessing self-administered questionnaire data contributes to the development of an automated estimation of an individual's affective state and engagement. Given the efficacy of including self-administered questionnaire data, future studies should confirm the effectiveness of such long-term intervention with a robot to maintain participants' motivation based on the proposed method of emotion estimation.

Field-enriched A* search algorithm for robot motion in path planning

Path planning in unknown or complex environments is a central issue in the fields of automation and robotics. To address this global path planning problem while maintaining the smoothness of robot motion, as well as to ensure safety by keeping away from obstacles, this paper propose an innovative method called Field-Enriched A* Planner (FEAP), which incorporates the repulsive potential field into the heuristic computation of the A* search algorithm. The research modified the heuristic mechanism of the A* algorithm by incorporating both attraction and repulsion factors from the Artificial Potential Field method, thereby enhancing the influence of obstacles in the decision-making process. The repulsive potential field enhances obstacle avoidance, ensuring smoother robot motion during the path planning process. The simulation experiments are conducted on the proposed method and compare it with common path planning algorithms. Results from the experimental trials demonstrate that the new method can effectively enhance the smoothness of robot motion while ensuring a safe path is identified.

System and Method for One or More Extruders Using a Robotic Arms to Print a 3D Model

The need for creating realistic body parts and anatomical models using additive printing was long ago recognised. The paper "Rapid prototyping approaches for anatomical modelling in medicine" by M. McGurk et al. in Ann. R. Coll. Surg. Engl. 1997; 79; 169-174, which discussed 3-D printing of models, summarised the state of the art in this subject some years ago. Models were made by sprinkling liquid over a layer of precursor powder using inkjet printer nozzles to form a solid, thin slice. For each additional slice, the printing procedure was repeated until the item was finished as a "green state" component, which was then burned in a furnace to sinter it. The product was then subjected to further processing to create a full density portion. Large item 3D printing has been simpler than in previous years because to the development of software for computer controlled robotic XY motion systems used in the semiconductor and optical sectors. Layered building of 3D things is now a comparatively cheap and quick operation for 3D printing equipment thanks to software applications like SolidWorks, AutoCAD 360, and similar software products. Larger things may be 3D printed by either attaching rails above the print nozzles for X-Y motion directed from above the nozzles, or by positioning the object to be created on an X-Y table with motion supplied below the nozzles. The current work attempts to construct a 3D printing setup that utilises two robotic arms to print a single component. Here, the bed will be swivelled to a specific angle to prevent the robotic arms from becoming entangled, and the robotic arms will also move 180 degrees from their mean positions. Additionally, the related software will be developed locally in our lab, resulting in a shorter printing time and higher level of accuracy.

Modeling of Control System for Tracked Mobile Robot Taking Into Account Kinematic and Dynamic Parameters

The paper examines the problem of constructing a motion control system for autonomous mobile tracked robots in an informal external environment. Based on the proposed mathematical model of the control system for a tracked mobile robot, which takes into account kinematic and dynamic parameters, simulation modeling of a tracked mobile robot was carried out in the dynamic modeling environments of technical systems MATLAB Simulink and SimInTech, which made it possible to control the coordinates of a tracked mobile robot along a predetermined trajectory with a certain accuracy. To increase the stability of the mobile robot motion control system, a PID controller of the armature current and electromagnetic torque was introduced into it. During the simulation study, graphical dependences on time were obtained: supply voltage; rotation angle of the robot body; track speeds; motor armature current; electromagnetic torque of engines; armature current of motors with PID controller; the path traveled by the caterpillars; electromagnetic torque of motors with a PID controller, and also the center of mass of the robot was set when setting a trajectory with a radius of 10 m for 6.2 s. Models were built in the MATLAB Simulink software package: general simulation, kinematic simulation and dynamic simulation of a tracked mobile robot, simulation subsystem of the electric drive control unit. In the SimInTech software environment, a simulation model of the dynamic part of the right electric drive of a tracked mobile robot was obtained. A comparative analysis of the graphical dependencies of the angular velocity of the roller and armature current of the motor of a tracked mobile robot, obtained in the MATLAB Simulink and SimInTech packages, was carried out, which revealed a number of advantages and disadvantages when testing the operation of the control system of a tracked mobile robot in an unformalized external environment.

Research on vision control methods based on multi-sensor fusion

This paper focuses on the application scenarios of electric power live line working robots, addressing tasks such as live wire stripping and live wire connection/disconnection. It utilizes Mixed Reality (MR) technology for rapid environment modeling and visual signal acquisition. Through the research on robot contact force sensing, real-time force feedback, and obstacle avoidance through the fusion of robot motion paths and environmental data, it establishes a novel electric power live line working robot system centered around dexterous dual arms at the operation end, force feedback teleoperation controllers, multi-sensor fusion acquisition systems, and robot motion planning and control technology. Through prototyping, a demonstration application is realized, showcasing a semi-autonomous electric power live line working robot system that employs remote operation combined with sensor-based MR for task execution. This teleoperation and Mixed Reality-based semi-autonomous electrified operation approach is poised to play a significant role in advancing and demonstrating the use of robots in the field of electrical grid operations.

Nongradient-Based Tracking of Environmental Field Isolines in a Changing Medium by Dubins-Vehicle Type Robots

A non-holonomic robot of a Dubins-vehicle type moves in a changing resistant medium, like water or air. The objective is to exhibit the extension of a spatial phenomenon (e.g., a critically contaminated area) related to a dynamic environmental field (e.g., a distribution of the contaminant concentration). Specifically, the robot should arrive at a certain level set of the field and repeatedly sweep it afterwards. The field data comes exclusively from online and point-wise measurements of the field value at the current location of the robot. An additional problem is related to the significant uncertainty on the field and the medium, including their variation with time and the effect of the medium on the robot's motion. The conditions for the accomplishability of the mission objective are found under these uncertainties and constraints on the kinematics of the robot. A navigation algorithm is presented that solves the mission with a slight enhancement of these necessary conditions. The performance of the algorithm is rigorously justified by the non-local convergence result and validated by computer simulation tests with a 6-DOF catamaran-like vessel and real-world environmental data.

Active View Planning for Visual SLAM in Outdoor Environments Based on Continuous Information Modeling

The visual simultaneous localization and mapping(vSLAM) is widely used in GPS-denied and open field environments for ground and surface robots. However, due to the frequent perception failures derived from lacking visual texture or the {swing} of robot view direction on rough terrains, the accuracy and robustness of vSLAM are still to be enhanced. The study develops a novel view planning approach of actively perceiving areas with maximal information to address the mentioned problem; a gimbal camera is used as the main sensor. Firstly, a map representation based on feature distribution-weighted Fisher information is proposed to completely and effectively represent environmental information richness. With the map representation, a continuous environmental information model is further established to convert the discrete information space into a continuous one for numerical optimization in real-time. Subsequently, the receding horizon optimization is utilized to obtain the optimal informative viewpoints with simultaneously considering the robotic perception, exploration and motion cost based on the continuous environmental model. Finally, several simulations and outdoor experiments are performed to verify the improvement of localization robustness and accuracy by the proposed approach.

Planar Walking of a Five-Link Biped Robot over a Stepped Surface with Obstacles of Different Heights and Lengths

A problem of modeling biped locomotion is an actual and complex problem of modern control theory. In the present paper, we deal with a special case when a five-link biped robot with knees moves along a given stepped surface. We describe the robot motion by a hybrid system which consists of a system of ordinary differential equations at the single support phase and an algebraic relation at the double support phase. The main contribution of this paper is the construction of a periodic motion that satisfies a set of conditions inherent in human walking. The results of the paper will be useful to those who are interested in control theory applications.

Ultrasonic sensor decision-making algorithm for mobile robot motion in maze environment

An autonomous mobile robot is one that can move from one location to another without the intervention of a human. A maze environment is a complex environment since it contains many obstacles and the major problem is moving through it. To avoid obstacles while moving through the maze, the mobile robot must be designed with an algorithm. This work proposes a decision-making system with an ultrasonic sensor to allow the developed autonomous mobile robot to avoid obstacles in any maze setting through its movements. The maze was designed with a size of 100×200 cm2 for the case study. Due to the height dimension of the barrier (20 cm) and the height dimension of the mobile robot including the ultrasonic sensor (20 cm), a distance of 20 cm was taken between the wall (obstacle) and the sensor. The result distance between the (wood wall) object and the sensor indicates that it is a reasonable distance chosen for the mobile robot to move and turn with flexibility as it travels through the maze environment from 0 cm to 300 cm. This mobile robot path took 1 minute to finish at a speed of 5 cm/sec, indicating that it is a quick algorithm as compared with related work.

Solving Robotic Trajectory Sequential Writing Problem via Learning Character's Structural and Sequential Information.

The writing sequence of numerals or letters often affects aesthetic aspects of the writing outcomes. As such, it remains a challenge for robotic calligraphy systems to perform, mimicking human writers' implicit intention. This article presents a new robot calligraphy system that is able to learn writing sequences with limited sequential information, producing writing results compatible to human writers with good diversity. In particular, the system innovatively applies a gated recurrent unit (GRU) network to generate robotic writing actions with the support of a prelabeled trajectory sequence vector. Also, a new evaluation method is proposed that considers the shape, trajectory sequence, and structural information of the writing outcome, thereby helping ensure the writing quality. A swarm optimization algorithm is exploited to create an optimal set of parameters of the proposed system. The proposed approach is evaluated using Arabic numerals, and the experimental results demonstrate the competitive writing performance of the system against state-of-the-art approaches regarding multiple criteria (including FID, MAE, PSNR, SSIM, and PerLoss), as well as diversity performance concerning variance and entropy. Importantly, the proposed GRU-based robotic motion planning system, supported with swarm optimization can learn from a small dataset, while producing calligraphy writing with diverse and aesthetically pleasing outcomes.

Design and sequential jumping experimental validation of a musculoskeletal bipedal robot based on the spring-loaded inverted pendulum model.

To effectively control a robot's motion, it is common to employ a simplified model that approximates the robot's dynamics. Nevertheless, discrepancies between the actual mechanical properties of the robot and the simplified model can result in motion failures. To address this issue, this study introduces a pneumatic-driven bipedal musculoskeletal robot designed to closely match the mechanical characteristics of a simplified spring-loaded inverted pendulum (SLIP) model. The SLIP model is widely utilized in robotics due to its passive stability and dynamic properties resembling human walking patterns. A musculoskeletal bipedal robot was designed and manufactured to concentrate its center of mass within a compact body around the hip joint, featuring low leg inertia in accordance with SLIP model principles. Furthermore, we validated that the robot exhibits similar dynamic characteristics to the SLIP model through a sequential jumping experiment and by comparing its performance to SLIP model simulation.

Quantized iterative learning control of communication-constrained systems with encoding and decoding mechanism

In practical applications, due to the limited communication bandwidth, the network control systems (NCSs) are prone to data dropouts when the load is high. In this paper, the problem of quantized iterative learning control (ILC) based on encoding and decoding mechanism for such communication-constrained systems is studied. By combining the encoding and decoding mechanism with the uniform quantizer, the network burden and the impact of quantization error on the tracking performance of the systems are significantly mitigated. Meanwhile, data dropouts are represented as the Bernoulli random variable model, and an ILC law based on gradient is designed. When data dropouts occur, the signals maintain the value of the previous trial, otherwise the signals are updated. For this kind of learning framework, the asymptotic zero-error tracking performance has been rigorously proven for the uniform quantizer. To validate the proposed design, a joint motion of an industrial robot in the horizontal plane is simulated as an example.

Deep deterministic policy gradient with constraints for gait optimisation of biped robots

In this paper, we propose a novel Reinforcement Learning (RL) algorithm for robotic motion control, that is, a constrained Deep Deterministic Policy Gradient (DDPG) deviation learning strategy to assist biped robots in walking safely and accurately. The previous research on this topic highlighted the limitations in the controller’s ability to accurately track foot placement on discrete terrains and the lack of consideration for safety concerns. In this study, we address these challenges by focusing on ensuring the overall system’s safety. To begin with, we tackle the inverse kinematics problem by introducing constraints to the damping least squares method. This enhancement not only addresses singularity issues but also guarantees safe ranges for joint angles, thus ensuring the stability and reliability of the system. Based on this, we propose the adoption of the constrained DDPG method to correct controller deviations. In constrained DDPG, we incorporate a constraint layer into the Actor network, incorporating joint deviations as state inputs. By conducting offline training within the range of safe angles, it serves as a deviation corrector. Lastly, we validate the effectiveness of our proposed approach by conducting dynamic simulations using the CRANE biped robot. Through comprehensive assessments, including singularity analysis, constraint effectiveness evaluation, and walking experiments on discrete terrains, we demonstrate the superiority and practicality of our approach in enhancing walking performance while ensuring safety. Overall, our research contributes to the advancement of biped robot locomotion by addressing gait optimisation from multiple perspectives, including singularity handling, safety constraints, and deviation learning.

Modeling and Control of a Snake Robot using SimMechanics

Snake robots are biological inspired robots designed to locomote in an unwanted environment. The mechanism comprises of serially connected links through rotational joints capable of rotating its body in one or more plans. In this paper, snake robot model is proposed. The proposed model consists of a 10-links physically connected through joints. The aim of this work is to propel a snake robot consisting of 10-link through the n-1 rotary joint on the serpentine aisle. Structure of the entire mechanical model of a snake is created in Simmechanics, MATLAB library tool within Simulink environment. The mechanics for the motion of snake robot including dynamics, kinematics and friction force models are developed in order to find the nth link location in a 3D environment. In addition, we have also established the joint angle to activate the robot’s link on a serpenoid terrain. To control the entire movement comprising position, speed and torque at each joint of the snake robot, control system is altered. This system is further composed of 3 proportional controllers having a sensor to take measurement results from an actuator. The simulation studies showing performance of the system are conducted to determine the control parameters of the system measured through joint sensor via scope, graph and display units. From simulation results it is shown that the snake robot followed the serpentine wave motion.

Development and Review of Group Rescue Robots Based on Artificial Intelligence Technology

Rescue robots can perform rescue missions in dangerous and complex environments, protect humans from harm, and improve the efficiency and effectiveness of rescue, thus playing an increasingly important role in disaster management and rescue. This article reviews the technologies and methods required to apply artificial intelligence to rescue robot teams. Firstly, the feasibility of motion control for swarm robots was explored from the perspective of biomimetic robots. Through the analysis of animal biomimetics and the comparison of commonly used topological structures, the nature of team rescue robot rescue is emphasized, and based on this, a scheme for optimizing topological networks by combining environmental intelligence is proposed. Secondly, several existing micro robots were introduced and their data loading capabilities were evaluated. On this basis, the process of robot vision and motion commands was outlined. At the meanwhile, researchers focus on the current mainstream robot motion trajectory algorithms, and study the algorithm optimization process from extending the motion path planning of a single robot to group coordinated motion. This includes traditional cell decomposition algorithms and algorithms combined with machine learning to improve path planning efficiency. Finally, the above methods were summarized, and the impact of other possible feasible methods in the field of artificial intelligence was explored and analyzed.

Connectedness of Soft-Ideal Topological Spaces

Despite its apparent simplicity, the idea of connectedness has significant effects on topology and its applications. An essential part of the intermediate-value theorem is the idea of connectedness. In many applications, such as population modeling, robotics motion planning, and geographic information systems, connectedness is significant, and it is a critical factor in differentiating between various topological spaces. This study uses soft open sets and the concept of soft ideals as a new class of soft sets to present and explore the ideas of soft connected spaces and strongly soft connected spaces with soft ideals. Also, under certain assumptions regarding the subsequent concepts—soft-ideal connectedness and stronglysoft-ideal connectedness in soft-ideal topological spaces—we characterize this new class of sets by employing soft open sets and soft ideals to examine its fundamental features. Furthermore, we look at a symmetry between our new notions and other existing ones, and this study examines the relationships between these concepts.

Design and analysis of a dual-rope crawler rope-climbing robot

Abstract. A rope-climbing robot (RCR) can reciprocate on a rope. To address the problems of poor load capacity and adaptability of the existing RCR, this study designs a dual-rope crawler type RCR, which can be used as a new type of transportation equipment in hilly, mountainous, and plateau areas. The crawler rope-climbing mechanism is a combination of a chain drive and the rope-climbing foot. Innovatively applying the parabolic theory of overhead rope to kinematically analyze the rope-climbing robot system, the robot motion trajectory model and the tilt angle equation are established. To establish the safe working interval of the rope-climbing robot, the influence of machine load and rope span on robot tilt angle is compared. Furthermore, research on the dynamic characteristics of the rope-climbing robot is carried out, establishing a time-varying system model of the dynamic tension of the rope in the rope-climbing robot system and analyzing the effects of speed and load on the dynamic tension of the rope and system stability. Finally, the prototype test results show that the RCR operates stably and has good load capacity and barrier-crossing capability.

Opportunities for Generative Artificial Intelligence to Accelerate Deployment of Human-Supervised Autonomous Robots

Autonomous robots have the potential to supplement human capabilities while reducing cognitive and physical burden. However, deploying such systems in natural settings is currently a time-consuming process that revolves around a human’s ability to research, design, test, and evaluate the robot – thereby introducing unnecessary bottlenecks and significant delays to technology adoption. The current work in the field of human-robot interaction (HRI) has historically focused on robot use even though humans play a critical role during autonomous system design and deployment. We argue that the scope of HRI must be expanded beyond that of the current views within the scientific community, to include all phases of system development, deployment, and use. Furthermore, to facilitate the pursuit of this new expanded scope, we present eight opportunities for technological advances in HRI and autonomy using Generative AI that, if realized, could have transformational impact on the fielding of human-supervised autonomous robots. Broadly speaking, our identified opportunities relate to interaction and trustworthiness, collaboration and cooperation, robot motion, robot perception, synthetic scenario generation, testing and evaluation, failure detection, and robot design.