Robot Motion Research Articles

Path planning for flexible needle based on both insertion mechanism kinematics and needle bending model.

Due to the limited operating space in the magnetic resonance (MR) environment, there is coupled motion in the insertion mechanism, which not only reduces the flexibility of the robot but also challenges the insertion path planning. Meanwhile, the path planning is also restricted by the bending rule of the flexible needle, thus the bending model of the needle is also essentially built. This paper proposes a path planner for the flexible needle based on both the coupled motion kinematics of the insertion robot and the bending model of the flexible needle. A kinematic analysis for the coupled motion of insertion robot is performed. And the bending model of flexible needle is established based on the needle-tissue interactions. The position and posture of the needle insertion at the entry point are obtained by the calculation of the target position and the analysis of the bending model. And the rotation or translation coordinates of each robot joint are calculated by the inverse kinematics of the insertion robot. Then the path planning based on the coupled kinematics and the bending model is realized. The insertion experiments were performed for each target of G1 and G2. The root mean square errors were 0.83 mm and 0.74 mm, and the maximum errors were 1.1 mm and 0.9 mm for G1 and G2, respectively. The experimental results show that the effectiveness and accuracy of the path planning can meet the requirements for a general minimally invasive surgery, so the proposed path planning algorithm is feasible. This study provides a new solution for the path planning of insertion robots for the minimally invasive surgery. This method can meet the insertion mechanism working within the limited operating space in the MR environment and has a high application value in future clinical medicine.

A digital twin-driven pre-grasp path planning method for robotic deep-frame grasping of disordered workpieces

In a sheet metal parts welding shop, traditional robot path-planning methods prove inadequate for achieving automatic robot loading and unloading of disordered workpieces in the deep material frames. Therefore, this paper proposes a robotic motion planning framework in Deep-frame Disordered Workpiece Grasping (DDWG), ensuring efficient automatic loading and unloading tasks with high efficiency and collision-free operations. In conjunction with the actual DDWG scenario, an Oriented Bounding Boxes (OBB) based hierarchical enclosing box algorithm is used for collision detection. Further, this paper proposes a two-segment Middle Pre-Grasp Rapidly-exploring Random Tree (MPG-RRT) algorithm for path planning, utilizing pre-grasp point (middle point) guidance. In the DDWG process, a pre-adjustable pre-grasp point is introduced to expand the two random trees toward the intermediate points. Additionally, a probabilistic goal bias and a goal gravity strategy are incorporated with an adaptive gravity step size at each exploring stage to guide the expansion of random trees toward the goal point direction and to optimize paths by removing redundant nodes. Finally, an existing digital twins-driven robotic production line is reconfigured to simulate motion planning in two DDWG scenarios, which validates the effectiveness and superiority of the proposed method.

A single-axis tracking interferometer to measure two-dimensional error motions of machine tools and industrial robots

A single-axis tracking interferometer to measure two-dimensional error motions of machine tools and industrial robots

Safe controller design for circular motion of a bicycle robot using control Lyapunov function and control barrier function

Safe controller design for circular motion of a bicycle robot using control Lyapunov function and control barrier function

A Fractional-Order Gradient Neural Solution to Time-Variant Quadratic Programming With Application to Robot Motion Planning

A Fractional-Order Gradient Neural Solution to Time-Variant Quadratic Programming With Application to Robot Motion Planning

Design of Rolling Motion for Snake-like Robots using Center-of-Gravity (COG) Shift

Design of Rolling Motion for Snake-like Robots using Center-of-Gravity (COG) Shift

Linear-time quasi-static stability detection for modular self-reconfigurable robots

We address the problem of detecting potential instability in the planned motion of modular self-reconfigurable robots. Previous research primarily focused on determining the system’s unique physical state but overlooked the mutual compensation effects of connection constraints. We introduce a linear-time quasi-static stability detection method for modular self-reconfigurable robots. The internal connections, non-connected contacts, and environmental contacts are considered, and the problem is modeled as a second-order cone program problem, whose solving time linearly increases with the number of modules. We aim to determine the critical stable state of the system and that is achieved by finding the required minimum characteristic connection strength. By analyzing the critical stable state, we can assess the system’s stability and identify potential broken connection points. Furthermore, the internal stability margin is defined to evaluate the configuration’s stability level. The suspension and object manipulation configurations are first demonstrated in simulation to analyze the effectiveness of the proposed algorithm. Subsequently, a series of physical experiments based on FreeSN were carried out. The calculated stable motion ranges of manipulator configurations are highly consistent with the actual sampling boundaries. Moreover, the proposed algorithm successfully predicts stability and identifies broken connections in diverse configurations, encompassing quadruped and closed-chain configurations on both even and uneven terrains. The load experiment further demonstrates that the impacts from unmodeled factors and input errors under normal conditions can be on a small scale. By combining the proposed detection method and stability margin, we open the door to realizing real-time motion planning on modular self-reconfigurable robots.

A relaxor ferroelectric crystal based Two-DOF miniature piezoelectric motor with fish body structure

Multi-dimensional movements and miniaturization are required for robotic joints motion, multi-axis optical alignments and other multiple degree-of-freedom (DOF) actuation scenes. However, it is a challenge for piezoelectric motors to guarantee multi-DOF movements, miniaturization, high load capacity and high resolution simultaneously. Inspired by synergic movement of body and caudal fin of fish in nature and the construction idea of metamaterials, we proposed a two-DOF linear piezoelectric motor (4mmHeight × 3mmLength × 3mmWidth) based on the artificial elongation 31z and shear 35x/y mode by using Pb(In1/2Nb1/2)O3–Pb(Mg1/3Nb2/3)O3–PbTiO3 (PIMNT) relaxor ferroelectric single crystal as piezoelectric elements. The results demonstrate that the motor is capable of achieving bidirectional motion in both X and Y axes, with the PIMNT crystal-based motor reaching a velocity of up to 94.8 mm/s. Its mechanical loading capacity per unit volume reaches as high as 1.39 mN/mm3, which exceeds the load capacity of currently reported two-DOF motors by 10 times. Moreover, it exhibits an exceptional step resolution of 5 nm, outperforming existing two-DOF motors by 1–2 orders of magnitude in terms of precision and accuracy. Furthermore, by modulating the driving signals of this motor, it can traverse any trajectory within the XY plane. The proposed ultrasonic motor has potential for applications in micro-robots, nano-precision motion, automatic visual systems and haptic interfaces.

Reproducing the caress gesture with an anthropomorphic robot: a feasibility study

Social robots have been widely used to deliver emotional, cognitive and social support to humans. The exchange of affective gestures, instead, has been explored to a lesser extent, despite phyisical interaction with social robots could provide the same benefits as human-human interaction. Some studies that explored the touch and hugs gestures were found in literature, but there are no studies that investigate the possibility of delivering realistic caress gestures, which are, in turn, the easiest affective gestures that could be delivered with a robot. The first objective of this work was to study the kinematic and dynamic features of the caress gesture by conducting experimental acquisitions in which ten healthy volunteers were asked to caress the cheek of a mannequin in two conditions, i.e. standing and sitting. Average motion and force features were then analyzed and used to generate a realistic caress gesture with an anthropomorphic robot, with the aim of assessing the feasibility of reproducing the caress gesture with a robotic device. In addition, twenty-six healthy volunteers evaluated the anthropomorphism and perceived safety of the reproduced affective gesture by answering the Godspeed Questionnaire Series and a list of statements on the robot motion. The gesture reproduced by the robot was similar to the caress gesture performed by healthy volunteers both in terms of hand trajectory and orientation, and exchanged forces. Overall, volunteers perceived the robot motion as safe and positive emotions were elicited. The proposed approach could be adapted to humanoid robots to improve the perceived anthropomorphism and safety of the caress gesture.

Simultaneous Localization and Mapping Methods for Snake-like Robots Based on Gait Adjustment.

Snake robots require autonomous localization and mapping capabilities for field applications. However, the characteristics of their motion, such as large turning angles and fast rotation speeds, can lead to issues like drift or even failure in positioning and map building. In response to this situation, this paper starts from the gait motion characteristics of the snake robot itself, proposing an improved gait motion method and a tightly coupled method based on IMU and visual information to solve the problem of poor algorithm convergence caused by head-shaking in snake robot SLAM. Firstly, the adaptability of several typical gaits of the snake robot to SLAM methods was evaluated. Secondly, the serpentine gait was selected as the object of gait improvement, and a head stability control method for the snake robot was proposed, thereby reducing the interference of the snake robot's motion on the sensors. Thirdly, a visual-inertial tightly coupled SLAM method for the snake robot's serpentine gait and Arc-Rolling gait was proposed, and the method was verified to enhance the robustness of the visual SLAM algorithm and improve the positioning and mapping accuracy of the snake robot. Finally, experiments proved that the methods proposed in this paper can effectively improve the accuracy of positioning and map building for snake robots.

Robots in Partially Known and Unknown Environments: A Simulated Annealing Approach for Re-Planning

The path planning problem using mobile robots, also known as robot motion planning, is a key problem in robotics. The goal is to find a collision-free path from a starting point to a target point in an environment with obstacles. These obstacles can be known, partially unknown, or completely unknown to the robot. The robot’s decisions are different according to its degree of knowledge of the environment. If the environment is fully known, using an offline approach is sufficient. Otherwise, online techniques are required to find a path. They must be fast, effective, and adaptive to tackle the complexity of changing environments. In this work, we propose a framework based on a simulated annealing approach to solve the path planning problem with different degrees of knowledge of the environment for the robot. We evaluate our approach with a set of large-scale instances with different features. The results show that our framework can quickly find quality solutions and is also able to manage environmental changes.

How to arrange the robotic environment? Leveraging experience in both motion planning and environment optimization.

This study presents an experience-based hierarchical-structure optimization algorithm to address the robotic system environment design problem, which combines motion planning and environment arrangement problems together. The motion planning problem, which could be defined as a multiple-degree-of-freedom (m-DOF) problem, together with the environment arrangement problem, which could be defined as a free DOF problem, is a high-dimensional optimization problem. Therefore, the hierarchical structure was established, with the higher layer solving the environment arrangement problem and lower layer solving the problem of motion planning. Previously planned trajectories and past results for this design problem were first constructed as datasets; however, they cannot be seen as optimal. Therefore, this study proposed an experience-reuse manner, which selected the most "useful" experience from the datasets and reused it to query new problems, optimize the results in the datasets, and provide better environment arrangement with shorter path lengths within the same time. Therefore, a hierarchical structural caseGA-ERTC algorithm was proposed. In the higher layer, a novel approach employing the case-injected genetic algorithm (GA) was implemented to tackle optimization challenges in robot environment design, leveraging experiential insights. Performance indices in the arrangement of the robot system's environment were determined by the robotic arm's motion and path length calculated using an experience-driven random tree (ERT) algorithm. Moreover, the effectiveness of the proposed method is illustrated with the 12.59% decrease in path lengths by solving different settings of hard problems and 5.05% decrease in easy problems compared with other state-of-the-art methods in three small robots.

Research on 3D humanoid robot pose estimation based on HRNet-Epipolar and CRF robot model by multiple view

Purpose This paper aims to present an unmarked method including entire two-dimensional (2D) and three-dimensional (3D) methods to recover absolute 3D humanoid robot poses from multiview images. Design/methodology/approach The method consists of two separate steps: estimating the 2D poses in multiview images and recovering the 3D poses from the multiview 2D heatmaps. The 2D one is conducted by High-Resolution Net with Epipolar (HRNet-Epipolar), and the Conditional Random Fields Humanoid Robot Pictorial Structure Model (CRF Robot Model) is proposed to recover 3D poses. Findings The performance of the algorithm is validated by experiments developed on data sets captured by four RGB cameras in Qualisys system. It illustrates that the algorithm has higher Mean Per Joint Position Error than Direct Linear Transformation and Recursive Pictorial Structure Model algorithms when estimating 14 joints of the humanoid robot. Originality/value A new unmarked method is proposed for 3D humanoid robot pose estimation. Experimental results show enhanced absolute accuracy, which holds important theoretical significance and application value for humanoid robot pose estimation and motion performance testing.

A Comprehensive Review of Mobile Robot Navigation Using Deep Reinforcement Learning Algorithms in Crowded Environments

Navigation is a crucial challenge for mobile robots. Currently, deep reinforcement learning has attracted considerable attention and has witnessed substantial development owing to its robust performance and learning capabilities in real-world scenarios. Scientists leverage the advantages of deep neural networks, such as long short-term memory, recurrent neural networks, and convolutional neural networks, to integrate them into mobile robot navigation based on deep reinforcement learning. This integration aims to enhance the robot's motion control performance in both static and dynamic environments. This paper illustrates a comprehensive survey of deep reinforcement learning methods applied to mobile robot navigation systems in crowded environments, exploring various navigation frameworks based on deep reinforcement learning and their benefits over traditional simultaneous localization and mapping-based frameworks. Subsequently, we comprehensively compare and analyze the relationships and differences among three types of navigation: autonomous-based navigation, navigation based on simultaneous localization and mapping, and planning-based navigation. Moreover, the crowded environment includes static, dynamic, and a combination of obstacles in different typical application scenarios. Finally, we offer insights into the evolution of navigation based on deep reinforcement learning, addressing the problems and providing potential solutions associated with this emerging field.

The application of Deep Learning in the field of Robotics

Abstract. As a new field with rapid development in the past two decades, deep learning has received more and more attention from researchers. Neural network adopts widely interconnected structure and effective learning mechanisms to simulate the process of information processing in the human brain, which is an important method in the development of artificial intelligence. Compared with shallow models, deep learning can achieve stronger unsupervised autonomous learning capabilities by increasing the number of layers of the network. In the past, the robots needed to be controlled by the humans; they controlled the robot to finish different tasks. In recent years, the researchers have tried to apply deep learning in the field of robots. Deep neural networks can simulate the complex cognitive laws of the human brain, so many researchers apply deep learning to robots to make robots have the thinking ability of humans. This article will review the cases of combining robot and in-depth learning in recent decades, Summarizing the achievements and problems faced in the three fields of robot vision, trajectory planning and motion control.

A modified extended Kalman filter based fusion of auto-correlation data for robot SLAM estimation

Abstract. With the popularization of robot technology, various technological developments related to robots have gradually entered a stage of rapid development. Especially path planning technology, which has been a very important and worthwhile part of robot motion and behavior since the concept of robots emerged. In this technology, various obstacles, environmental changes, and efficiency factors usually need to be considered. Whether in the past, present, or future, robot behavior pursues greater precision, reliability, adaptability to the environment, and even anthropomorphism. In order to achieve these goals, the idea of data fusion has emerged in the development of robot data processing. Therefore, the research direction is to use Kalman filtering for noise filtering and data correction to improve the accuracy of fused data. By using a feasible and strongly correlated data fusion method, combined with an improved extended Kalman filter, various data generated by robots can be fused in multiple dimensions and have correlations, reducing the negative impact of data differences and improving the quality and reliability of data fusion.

Nonlinear model predictive control with set terminal constraint for safe robot motion planning via speed and separation monitoring

This paper proposes a methodology for safely planning the motion of a robot manipulator sharing its workspace with a human operator. The motion of the robot is continuously re-planned via nonlinear model predictive control (NMPC), imposing the so-called speed and separation monitoring (SSM) condition to guarantee human safety. Contrary to previous works in the field, the NMPC algorithm is designed with an ellipsoidal terminal constraint, to enlarge the domain of attraction compared to the case in which a point terminal constraint was imposed. This is a very important aspect in real-world applications, allowing the robot to plan its motion from initial configurations that are relatively far from the goal point. Theoretical results are proved on recursive feasibility and closed-loop stability for both cases of NMPC with point and set terminal constraints, under the simplifying assumption of a static human. The effectiveness of the proposed approach is verified via numerical evaluation of the domain of attraction and with experiments on a UR5 manipulator.

Controller design and experimental validation of walking for a musculoskeletal bipedal lower limb robot based on the spring-loaded inverted pendulum model.

In the study of PAM (McKibben-type pneumatic artificial muscle)-driven bipedal robots, it is essential to investigate whether the intrinsic properties of the PAM contribute to achieving stable robot motion. Furthermore, it is crucial to determine if this contribution can be achieved through the interaction between the robot's mechanical structure and the PAM. In previous research, a PAM-driven bipedal musculoskeletal robot was designed based on the principles of the spring-loaded inverted pendulum (SLIP) model. The robot features low leg inertia and concentrated mass near the hip joint. However, it is important to note that for this robot, only the design principles were based on the SLIP model, and no specialized controller was specifically designed based on the model. To address this issue, based on the characteristics of the developed robot, a PAM controller designed also based on the SLIP model is developed in this study. This model-based controller regulates ankle flexion PAM to adjust the direction of the ground reaction force during robot walking motion. The results indicate that the proposed controller effectively directs the leg ground reaction force towards the center of mass during walking.

Motion controller for multi-joint robotic arm with deep cascade gated Bayesian broad learning system

Intelligent controllers based on the broad learning system can simplify the process of model parameter adjustment, finding wide applications in the motion control of multi-joint robotic arms. However, motion controllers for multi-joint robotic arms based on broad learning system exhibit insufficient precision and overlook the impact of joint motion commonalities on controller design. Therefore, this paper proposes a novel motion control strategy for a multi-joint robotic arm based on a deep cascade feature-enhancement gated Bayesian broad learning system. Firstly, the motion controller of the deep cascade feature-enhancement Bayesian broad learning system is constructed to enhance the robotic arm motion control precision. Secondly, an incremental node generation module with an attention-gated mechanism is constructed to capture the unique motion characteristics of the target joints, which is further combined with model generalization to simplify the motion control process of the multi-joint robotic arm. Finally, controller convergence is enhanced by combining it with the Lyapunov theory to constrain the learning parameters. Simulations and physical experiments are designed to verify the feasibility and superiority of the proposed motion control strategy. The results demonstrated that the strategy improved the accuracy of robotic arm motion control, with the root mean square error in position tracking reduced to 0.0019 rad. This represents a 93.39% reduction in error compared to existing techniques.

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.