Robot Motion Research Articles

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.

Fiberbots: Robotic fibers for high-precision minimally invasive surgery.

Precise manipulation of flexible surgical tools is crucial in minimally invasive surgical procedures, necessitating a miniature and flexible robotic probe that can precisely direct the surgical instruments. In this work, we developed a polymer-based robotic fiber with a thermal actuation mechanism by local heating along the sides of a single fiber. The fiber robot was fabricated by highly scalable fiber drawing technology using common low-cost materials. This low-profile (below 2 millimeters in diameter) robotic fiber exhibits remarkable motion precision (below 50 micrometers) and repeatability. We developed control algorithms coupling the robot with endoscopic instruments, demonstrating high-resolution in situ molecular and morphological tissue mapping. We assess its practicality and safety during in vivo laparoscopic surgery on a porcine model. High-precision motion of the fiber robot delivered endoscopically facilitates the effective use of cellular-level intraoperative tissue identification and ablation technologies, potentially enabling precise removal of cancer in challenging surgical sites.

Numerical Analysis of a Drop-Shaped Aquatic Robot

Finite-dimensional equations constructed earlier to describe the motion of an aquatic drop-shaped robot due to given rotor oscillations are studied. To study the equations of motion, we use the Poincaré map method, estimates of the Lyapunov exponents, and the parameter continuation method to explore the evolution of asymptotically stable solutions. It is shown that, in addition to the so-called main periodic solution of the equations of motion for which the robot moves in a circle in a natural way, an additional asymptotically stable periodic solution can arise under the influence of highly asymmetric impulsive control. This solution corresponds to the robot’s sideways motion near the circle. It is shown that this additional periodic solution can lose stability according to the Neimark–Sacker scenario, and an attracting torus appears in its vicinity. Thus, a quasiperiodic mode of motion can exist in the phase space of the system. It is shown that quasiperiodic solutions of the equations of motion also correspond to the quasiperiodic motion of the robot in a bounded region along a trajectory of a rather complex shape. Also, strange attractors were found that correspond to the drifting motion of the robot. These modes of motion were found for the first time in the dynamics of the drop-shaped robot.

Smoothness-oriented motion planning method for milling robot considering workpiece set-up optimization

Robots are attracing increasing attention in the machining field owing to their great flexibility, vast workspace and low cost. Functional redundancy exists when robots are engaged in milling tasks. Redundancy resolution be considered not only in milling robot motion planning, but also in workpiece layout. A two-step motion planning method is proposed to optimize the trajectory and the workpiece set-up simultaneously to improve planning efficiency with less computational burden. In the first stage, a bilevel optimization technique is proposed that can plan the motion from a global perspective and search the optimal workpiece layout. In the second step, based on a novel smoothness index, a modified whale optimization algorithm combined with the differential evolution algorithm (DE-WOA) is proposed to resolve redundancy under robot and machining error constraints. Experiments demonstrate that the proposed method can enhance the quality of surface machining while avoiding the impact of path length on computational complexity.

A Generative Model to Embed Human Expressivity into Robot Motions.

This paper presents a model for generating expressive robot motions based on human expressive movements. The proposed data-driven approach combines variational autoencoders and a generative adversarial network framework to extract the essential features of human expressive motion and generate expressive robot motion accordingly. The primary objective was to transfer the underlying expressive features from human to robot motion. The input to the model consists of the robot task defined by the robot's linear velocities and angular velocities and the expressive data defined by the movement of a human body part, represented by the acceleration and angular velocity. The experimental results show that the model can effectively recognize and transfer expressive cues to the robot, producing new movements that incorporate the expressive qualities derived from the human input. Furthermore, the generated motions exhibited variability with different human inputs, highlighting the ability of the model to produce diverse outputs.

Joint torque prediction of industrial robots based on PSO-LSTM deep learning

PurposeBecause of the key role of joint torque in industrial robots (IRs) motion performance control and energy consumption calculation and efficiency optimization, the purpose of this paper is to propose a deep learning torque prediction method based on long short-term memory (LSTM) recurrent neural networks optimized by particle swarm optimization (PSO), which can accurately predict the the joint torque.Design/methodology/approachThe proposed model optimized the LSTM with PSO algorithm to accurately predict the IRs joint torque. The authors design an excitation trajectory for ABB 1600–10/145 experimental robot and collect its relative dynamic data. The LSTM model was trained with the experimental data, and PSO was used to find optimal number of LSTM nodes and learning rate, then a torque prediction model is established based on PSO-LSTM deep learning method. The novel model is used to predict the robot’s six joint torque and the root mean error squares of the predicted data together with least squares (LS) method were comparably studied.FindingsThe predicted joint torque value by PSO-LSTM deep learning approach is highly overlapped with those from real experiment robot, and the error is quite small. The average square error between the predicted joint torque data and experiment data is 2.31 N.m smaller than that with the LS method. The accuracy of the novel PSO-LSTM learning method for joint torque prediction of IR is proved.Originality/valuePSO and LSTM model are deeply integrated for the first time to predict the joint torque of IR and the prediction accuracy is verified.

A novel teacher–student hierarchical approach for learning primitive information

In this study, we focus on the challenge of robot motion generation, which is critical for improving the ability of robots to perform downstream tasks. To this end, inspired by the idea of variational inference, we propose a novel teacher–student hierarchical approach for learning continuous primitive spaces to generate robot motions. We first learn a variational auto-encoder (VAE) based on Gated Recurrent Unit (GRU) from demonstrations, the encoder is used as a teacher model to encode trajectories to primitive information, and the decoder aims to decode latent information to joint trajectories. To learn a primitive action space for the high-level model, we propose a novel learning method to train a student model. We utilize the trained teacher to encode trajectories to labels, and use the Kullback–Leibler (KL) divergence and the reconstruction errors between the label and the predicted primitive information as the loss to train a student model. We then utilize trained student models to guide high-level policy learning. We evaluate our approach on three different robot datasets. Experiments demonstrate that our method obtains the ability to generate new motion and discover common information across tasks, it also can be used to accelerate downstream task learning.

AIP-Net: An anchor-free instance-level human part detection network

Human part detection has significant research and application in computer vision fields such as human–robot interaction, motion capture, facial recognition, and human key point detection. However, the current human body part detection method encounters challenges when detecting multi-scale objects and capturing the correlation relationship between human instances and human parts. To address these problems, a new anchor-free instance-level human part detection network (AIP-Net) is proposed. AIP-Net is a “two-level” structure that consists of two lightweight anchor-free detectors: a body detector and a parts detector. AIP-Net gradually focuses the human body on the human part from top to down, effectively avoiding the interference of extraneous background and enhancing the correlation relationship between human instances and body parts. Additionally, we design a body-part multidimensional context (BPMC) model in the parts detector branch to enhance the capability of the network. We trained the AIP-Ne end-to-end and achieved a state-of-the-art (SOTA) performance of 36.2 mean average precision (mAP) on COCO Human Parts Dataset. Moreover, we successfully utilized the AIP-Net in the human–robot interaction(HRI) platform and validated its practicality.

Multiple mobile robots motion control based on screw theory for aircraft panel assembly

As a crucial component of large aircraft, the assembly efficiency of aircraft skins directly impacts production efficiency. To achieve efficient manufacturing of aircraft skins, this paper proposes a multiple mobile robot control algorithm based on screw theory. The robot arm is integrated into a rail or AGV to increase its motion space, creating a mobile robot assembly system. To address the redundant degrees of freedom problem caused by the mobile manipulator, this paper adopts the screw theory to describe the motion of multiple robots. Furthermore, to ensure the constraint of the motion between multiple robots, this paper proposes a multi-robot control method based on screw constraint. Rigid body constraints are assigned to the end of each mobile manipulator, and the motion is decomposed to the mobile platform and the robot arm. Finally, the cooperative motion control of multiple mobile manipulators is realized. The proposed algorithm is applied in the multi-mobile manipulator cooperative aircraft panel assembly task, achieving efficient assembly of aircraft panel and long truss.