Showing papers on "Kinematics published in 2022"

Improved Vehicle Localization Using On-Board Sensors and Vehicle Lateral Velocity

Abstract: Vehicle localization is essential for intelligent and autonomous vehicles. To improve the accuracy of vehicle stand-alone localization in highly dynamic driving conditions during GNSS (Global Navigation Satellites Systems) outages, this paper proposes a vehicle localization system based on vehicle chassis sensors considering vehicle lateral velocity. Firstly, a GNSS/On-board sensors fusion localization framework is established, which could estimate vehicle states such as attitude, velocity, and position. Secondly, when the vehicle has a large lateral motion, nonholonomic constraint in the lateral direction loses fidelity. Instead of using nonholonomic constraint, we propose a vehicle dynamics/kinematics fusion lateral velocity estimation algorithm, which combines the advantage of vehicle dynamic model in low dynamic driving conditions and the advantage of kinematic model in highly dynamic driving conditions. Thirdly, vehicle longitudinal velocity estimated by WSS (Wheel Speed Sensor) and lateral velocity estimated by proposed method are as measurements for the localization system. All information is fused by an adaptive Kalman filter. Finally, vehicle experiments in U-turn maneuver and left-turn maneuver at a traffic intersection are conducted to verify the proposed method. Four different methods are compared in the experiments, and the results show that the estimated position accuracy of our method is below half a meter during a 5s GNSS outage and could keep a sub-meter-level during a 20s GNSS outage while the vehicle has a relatively large lateral motion.

An Incremental Learning Framework for Human-Like Redundancy Optimization of Anthropomorphic Manipulators

Abstract: Recently, the human-like behavior on the anthropomorphic robot manipulator is increasingly accomplished by the kinematic model establishing the relationship of an anthropomorphic manipulator and human arm motions. Notably, the growth and broad availability of advanced data science techniques facilitate the imitation learning process in anthropomorphic robotics. However, the enormous dataset causes the labeling and prediction burden. In this article, the swivel motion reconstruction approach was applied to imitate human-like behavior using the kinematic mapping in robot redundancy. For the sake of efficient computing, a novel incremental learning framework that combines an incremental learning approach with a deep convolutional neural network is proposed for fast and efficient learning. The algorithm exploits a novel approach to detect changes from human motion data streaming and then evolve its hierarchical representation of features. The incremental learning process can fine-tune the deep network only when model drifts detection mechanisms are triggered. Finally, we experimentally demonstrated this neural network's learning procedure and translated the trained human-like model to manage the redundancy optimization control of an anthropomorphic robot manipulator (LWR4+, KUKA, Germany). This approach can hold the anthropomorphic kinematic structure-based redundant robots. The experimental results showed that our architecture could not only enhance the regression accuracy but also significantly reduce the processing time of learning human motion data.

Controlling the kinematics of a spring-pendulum system using an energy harvesting device

Abstract: This work focuses on vibration alleviation and energy harvesting in a dynamical system of a spring-pendulum. The structure of the pendulum is modified using an independent electromagnetic harvesting system. The harvesting depends on the oscillation of a magnet in a coil. An endeavor has been made to get both the energy harvesting and mitigation of vibration efficacy of the harvester. The governing kinematics equations are derived using Lagrange’s equations and are solved asymptotically using the multiple scales method to achieve the intended outcome as new and precise results. The resonance states are classified, and the influence of various parameters of the studied system is analyzed. Fixed points at steady states are categorized into stable and unstable. The time behavior of the solutions, the modified amplitudes, and phases are examined and interpreted in the light of their graphical plots. Zones of stability and instability are concerned, in which the system’s behavior is stable for a wide range of used parameters. This model has become essential in recent times as it uses control sensors in industrial applications, buildings, infrastructure, automobiles, and transportation.

A Tandem Robotic Arm Inverse Kinematic Solution Based on an Improved Particle Swarm Algorithm

Abstract: The analysis of robot inverse kinematic solutions is the basis of robot control and path planning, and is of great importance for research. Due to the limitations of the analytical and geometric methods, intelligent algorithms are more advantageous because they can obtain approximate solutions directly from the robot’s positive kinematic equations, saving a large number of computational steps. Particle Swarm Algorithm (PSO), as one of the intelligent algorithms, is widely used due to its simple principle and excellent performance. In this paper, we propose an improved particle swarm algorithm for robot inverse kinematics solving. Since the setting of weights affects the global and local search ability of the algorithm, this paper proposes an adaptive weight adjustment strategy for improving the search ability. Considering the running time of the algorithm, this paper proposes a condition setting based on the limit joints, and introduces the position coefficient k in the velocity factor. Meanwhile, an exponential product form modeling method (POE) based on spinor theory is chosen. Compared with the traditional DH modeling method, the spinor approach describes the motion of a rigid body as a whole and avoids the singularities that arise when described by a local coordinate system. In order to illustrate the advantages of the algorithm in terms of accuracy, time, convergence and adaptability, three experiments were conducted with a general six-degree-of-freedom industrial robotic arm, a PUMA560 robotic arm and a seven-degree-of-freedom robotic arm as the research objects. In all three experiments, the parameters of the robot arm, the range of joint angles, and the initial attitude and position of the end-effector of the robot arm are given, and the attitude and position of the impact point of the end-effector are set to verify whether the joint angles found by the algorithm can reach the specified positions. In Experiments 2 and 3, the algorithm proposed in this paper is compared with the traditional particle swarm algorithm (PSO) and quantum particle swarm algorithm (QPSO) in terms of position and direction solving accuracy, operation time, and algorithm convergence. The results show that compared with the other two algorithms, the algorithm proposed in this paper can ensure higher position accuracy and orientation accuracy of the robotic arm end-effector. the position error of the algorithm proposed in this paper is 0 and the maximum orientation error is 1.29 × 10–8. while the minimum position error of the other two algorithms is −1.64 × 10–5 and the minimum orientation error is −4.03 × 10–6. In terms of operation time, the proposed algorithm in this paper has shorter operation time compared with the other two algorithms. In the last two experiments, the computing time of the proposed algorithm is 0.31851 and 0.30004s respectively, while the shortest computing time of the other two algorithms is 0.33359 and 0.30521s respectively. In terms of algorithm convergence, the proposed algorithm can achieve faster and more stable convergence than the other two algorithms. After changing the experimental subjects, the proposed algorithm still maintains its advantages in terms of accuracy, time and convergence, which indicates that the proposed algorithm is more applicable and has certain potential in solving the multi-arm inverse kinematics solution. This paper provides a new way of thinking for solving the multi-arm inverse kinematics solution problem.

The SAGEX review on scattering amplitudes Chapter 2: An invitation to color-kinematics duality and the double copy

Abstract: Advances in scattering amplitudes have exposed previously-hidden color-kinematics and double-copy structures in theories ranging from gauge and gravity theories to effective field theories such as chiral perturbation theory and the Born–Infeld model. These novel structures both simplify higher-order calculations and pose tantalizing questions related to a unified framework underlying relativistic quantum theories. This introductory mini-review article invites further exploration of these topics. After a brief introduction to color-kinematics duality and the double copy as they emerge at tree and loop-level in gauge and gravity theories, we present two distinct examples: (1) an introduction to the web of double-copy-constructible theories, and (2) a discussion of the application of the double copy to calculation relevant to gravitational-wave physics.

Novel flight style and light wings boost flight performance of tiny beetles

Abstract: Flight speed is positively correlated with body size in animals1. However, miniature featherwing beetles can fly at speeds and accelerations of insects three times their size2. Here we show that this performance results from a reduced wing mass and a previously unknown type of wing-motion cycle. Our experiment combines three-dimensional reconstructions of morphology and kinematics in one of the smallest insects, the beetle Paratuposa placentis (body length 395 μm). The flapping bristled wings follow a pronounced figure-of-eight loop that consists of subperpendicular up and down strokes followed by claps at stroke reversals above and below the body. The elytra act as inertial brakes that prevent excessive body oscillation. Computational analyses suggest functional decomposition of the wingbeat cycle into two power half strokes, which produce a large upward force, and two down-dragging recovery half strokes. In contrast to heavier membranous wings, the motion of bristled wings of the same size requires little inertial power. Muscle mechanical power requirements thus remain positive throughout the wingbeat cycle, making elastic energy storage obsolete. These adaptations help to explain how extremely small insects have preserved good aerial performance during miniaturization, one of the factors of their evolutionary success.

RNN for Repetitive Motion Generation of Redundant Robot Manipulators: An Orthogonal Projection-Based Scheme

Abstract: For the existing repetitive motion generation (RMG) schemes for kinematic control of redundant manipulators, the position error always exists and fluctuates. This article gives an answer to this phenomenon and presents the theoretical analyses to reveal that the existing RMG schemes exist a theoretical position error related to the joint angle error. To remedy this weakness of existing solutions, an orthogonal projection RMG (OPRMG) scheme is proposed in this article by introducing an orthogonal projection method with the position error eliminated theoretically, which decouples the joint space error and Cartesian space error with joint constraints considered. The corresponding new recurrent neural networks (NRNNs) are structured by exploiting the gradient descent method with the assistance of velocity compensation with theoretical analyses provided to embody the stability and feasibility. In addition, simulation results on a fixed-based redundant manipulator, a mobile manipulator, and a multirobot system synthesized by the existing RMG schemes and the proposed one are presented to verify the superiority and precise performance of the OPRMG scheme for kinematic control of redundant manipulators. Moreover, via adjusting the coefficient, simulations on the position error and joint drift of the redundant manipulator are conducted for comparison to prove the high performance of the OPRMG scheme. To bring out the crucial point, different controllers for the redundancy resolution of redundant manipulators are compared to highlight the superiority and advantage of the proposed NRNN. This work greatly improves the existing RMG solutions in theoretically eliminating the position error and joint drift, which is of significant contributions to increasing the accuracy and efficiency of high-precision instruments in manufacturing production.

An Open-Source and Wearable System for Measuring 3D Human Motion in Real-Time

Abstract: Analyzing human motion is essential for diagnosing movement disorders and guiding rehabilitation for conditions like osteoarthritis, stroke, and Parkinson's disease. Optical motion capture systems are the standard for estimating kinematics, but the equipment is expensive and requires a predefined space. While wearable sensor systems can estimate kinematics in any environment, existing systems are generally less accurate than optical motion capture. Many wearable sensor systems require a computer in close proximity and use proprietary software, limiting experimental reproducibility.Here, we present OpenSenseRT, an open-source and wearable system that estimates upper and lower extremity kinematics in real time by using inertial measurement units and a portable microcontroller.We compared the OpenSenseRT system to optical motion capture and found an average RMSE of 4.4 degrees across 5 lower-limb joint angles during three minutes of walking and an average RMSE of 5.6 degrees across 8 upper extremity joint angles during a Fugl-Meyer task. The open-source software and hardware are scalable, tracking 1 to 14 body segments, with one sensor per segment. A musculoskeletal model and inverse kinematics solver estimate Kinematics in real-time. The computation frequency depends on the number of tracked segments, but is sufficient for real-time measurement for many tasks of interest; for example, the system can track 7 segments at 30 Hz in real-time. The system uses off-the-shelf parts costing approximately $100 USD plus $20 for each tracked segment.The OpenSenseRT system is validated against optical motion capture, low-cost, and simple to replicate, enabling movement analysis in clinics, homes, and free-living settings.

Harnessing the Multistability of Kresling Origami for Reconfigurable Articulation in Soft Robotic Arms

Abstract: This study examines a biology-inspired approach of using reconfigurable articulation to reduce the control requirement for soft robotic arms. We construct a robotic arm by assembling Kresling origami modules that exhibit predictable bistability. By switching between their two stable states, these origami modules can behave either like a flexible joint with low bending stiffness or like a stiff link with high stiffness, without requiring any continuous power supply. In this way, the robotic arm can exhibit pseudo-linkage kinematics with lower control requirements and improved motion accuracy. A unique advantage of using origami as the robotic arm skeleton is that its bending stiffness ratio between stable states is directly related to the underlying Kresling design. Therefore, we conduct extensive parametric analyses and experimental validations to identify the optimized Kresling pattern for articulation. The results indicate that a higher angle ratio, a smaller resting length at contracted stable state, and a large number of polygon sides can offer more significant and robust bending stiffness tuning. Based on this insight, we construct a proof-of-concept, tendon-driven robotic arm consisting of three modules and show that it can exhibit the desired reconfigurable articulation behavior. Moreover, the deformations of this manipulator are consistent with kinematic model predictions, which validate the possibility of using simple controllers for such compliant robotic systems.

An Acceleration-Level Data-Driven Repetitive Motion Planning Scheme for Kinematic Control of Robots With Unknown Structure

Abstract: It is generally considered that controlling a robot precisely becomes tough on the condition of unknown structure information. Applying a data-driven approach to the robot control with the unknown structure implies a novel feasible research direction. Therefore, in this article, as a combination of the structural learning and robot control, an acceleration-level data-driven repetitive motion planning (DDRMP) scheme is proposed with the corresponding recurrent neural network (RNN) constructed. Then, theoretical analyses on the learning and control abilities are provided. Moreover, simulative experiments on employing the acceleration-level DDRMP scheme as well as the corresponding RNN to control a Sawyer robot and a Baxter robot with unknown structure information are performed. Accordingly, simulation results validate the feasibility of the proposed method and comparisons among the existing repetitive motion planning (RMP) schemes indicate the superiority of the proposed method. This work offers sufficient theoretical and simulative solutions for the acceleration-level redundancy problem of redundant robots with unknown structure and joint limits considered.

Motion Planning for Mobile Manipulators—A Systematic Review

Abstract: One of the fundamental fields of research is motion planning. Mobile manipulators present a unique set of challenges for the planning algorithms, as they are usually kinematically redundant and dynamically complex owing to the different dynamic behavior of the mobile base and the manipulator. The purpose of this article is to systematically review the different planning algorithms specifically used for mobile manipulator motion planning. Depending on how the two subsystems are treated during planning, sampling-based, optimization-based, search-based, and other planning algorithms are grouped into two broad categories. Then, planning algorithms are dissected and discussed based on common components. The problem of dealing with the kinematic redundancy in calculating the goal configuration is also analyzed. While planning separately for the mobile base and the manipulator provides convenience, the results are sub-optimal. Coordinating between the mobile base and manipulator while utilizing their unique capabilities provides better solution paths. Based on the analysis, challenges faced by the current planning algorithms and future research directions are presented.

Human Cinematic Capture and Movement System Through Kinect: A Detailed and Innovative Review

Abstract: There are many techniques focused on capturing the kinematic movement of the human body, all these techniques help in the creation of robotic devices to achieve efficient rehabilitation, many studies provide information that helps the researcher, but these are scattered, each proposing different characteristics, solutions, and methods to obtain body movements. Based on this, the objective is to provide the researcher with a structured matrix that integrates different studies related to the capture of the kinematic movement of the human body, this will allow him to evaluate alternatives where he can choose, analyze the study or characteristics that best contribute to his research topic. As methods, specialized search engines were used that helped structure the matrix. Currently, there is little information on capturing human kinematic movements, through the interaction of different sensors so it was also incorporated into the matrix, also in the proposals is considered captured movements, recognition method, sensor, treatment, and application. In conclusion, the article will provide information to people who want to create robotic devices such as exoskeletons, prostheses, or different suits that help the rehabilitation of the human body. Where it is important to have accuracy in obtaining body movement patterns.

3D Trajectory Optimization for Energy-Efficient UAV Communication: A Control Design Perspective

Abstract: This paper studies the three-dimensional (3D) trajectory optimization problem for unmanned aerial vehicle (UAV) aided wireless communication. Existing works mainly rely on the kinematic equations for UAV’s mobility modeling, while its dynamic equations are usually missing. As a result, the planned UAV trajectories are piece-wise line segments in general, which may be difficult to implement in practice. By leveraging the concept of state-space model, a control-based UAV trajectory design is proposed in this paper, which takes into account both of the UAV’s kinematic equations and the dynamic equations. Consequently, smooth trajectories that are amenable to practical implementation can be obtained. Moreover, the UAV’s controller design is achieved along with the trajectory optimization, where practical roll angle and pitch angle constraints are considered. Furthermore, a new energy consumption model is derived for quad-rotor UAVs, which is based on the voltage and current flows of the electric motors and thus captures both the consumed energy for motion and the energy conversion efficiency of the motors. Numerical results are provided to validate the derived energy consumption model and show the effectiveness of our proposed algorithms.

The 2021 M w 7.4 Madoi earthquake: an archetype bilateral slip‐pulse rupture arrested at a splay fault

Abstract: We combine measurements of ground deformation from Synthetic Aperture Radar images, high-rate GNSS and tele-seismic waveforms to study the rupture kinematics of the Madoi earthquake, which occurred in eastern Tibet on May 21, 2021 and reached a moment magnitude Mw 7.4. The data show nearly pure left-lateral motion along a 170 km long rupture and a total duration of 36s. The earthquake initiated near the middle of the main segment and evolved in a bilateral slip pulse rupture which propagated at a sub-Rayleigh speed of 2.6-2.8 km/s. In our model, slip is concentrated at depth of less than ~15 km and reaches a maximum of 4.2 m. The rupture arrested ~10 s after branching on the extensional splay faults at both extremities. The branching onto the splay faults and the eventual arrest of the rupture is used to provide constraints on the fault frictional properties.

A Path Planning Method for Underground Intelligent Vehicles Based on an Improved RRT* Algorithm

Abstract: Path planning is one of the key technologies for unmanned driving of underground intelligent vehicles. Due to the complexity of the drift environment and the vehicle structure, some improvements should be made to adapt to underground mining conditions. This paper proposes a path planning method based on an improved RRT* (Rapidly-Exploring Random Tree Star) algorithm for solving the problem of path planning for underground intelligent vehicles based on articulated structure and drift environment conditions. The kinematics of underground intelligent vehicles are realized by vectorized map and dynamic constraints. The RRT* algorithm is selected for improvement, including dynamic step size, steering angle constraints, and optimal tree reconnection. The simulation case study proves the effectiveness of the algorithm, with a lower path length, lower node count, and 100% steering angle efficiency.

Physics-based character controllers using conditional VAEs

Abstract: High-quality motion capture datasets are now publicly available, and researchers have used them to create kinematics-based controllers that can generate plausible and diverse human motions without conditioning on specific goals (i.e., a task-agnostic generative model). In this paper, we present an algorithm to build such controllers for physically simulated characters having many degrees of freedom. Our physics-based controllers are learned by using conditional VAEs, which can perform a variety of behaviors that are similar to motions in the training dataset. The controllers are robust enough to generate more than a few minutes of motion without conditioning on specific goals and to allow many complex downstream tasks to be solved efficiently. To show the effectiveness of our method, we demonstrate controllers learned from several different motion capture databases and use them to solve a number of downstream tasks that are challenging to learn controllers that generate natural-looking motions from scratch. We also perform ablation studies to demonstrate the importance of the elements of the algorithm. Code and data for this paper are available at: https://github.com/facebookresearch/PhysicsVAE

Fish-Wearable Data Snooping Platform for Underwater Energy Harvesting and Fish Behavior Monitoring.

Abstract: Conventional approaches to studying fish kinematics pose a great challenge for the real-time monitoring of fish motion kinematics. Here, a multifunctional fish-wearable data snooping platform (FDSP) for studying fish kinematics is demonstrated based on an air sac triboelectric nanogenerator (AS-TENG) with antibacterial coating. The AS-TENG not only can harvest energy from fish swimming but also serves as the self-powered sensory module to monitor the swimming behavior of the fish. The peak output power generated from each swing of the fishtail can reach 0.74 mW, while its output voltage can reflect the real-time behavior of the fishtail. The antibacterial coating on the FDSP can improve its biocompatibility and the elastic texture of the FDSP allows it to be tightly attached to fish. The wireless communication system is designed to transmit the sensory data to a cell phone, where the detailed parameters of fish motion can be obtained, including swing angle, swing frequency, and even the typical swing gestures. This FDSP has broad application prospects in underwater self-powered sensors, wearable tracking devices, and soft robots.

Video-Based Pose Estimation for Gait Analysis in Stroke Survivors during Clinical Assessments: A Proof-of-Concept Study

Abstract: Recent advancements in deep learning have produced significant progress in markerless human pose estimation, making it possible to estimate human kinematics from single camera videos without the need for reflective markers and specialized labs equipped with motion capture systems. Such algorithms have the potential to enable the quantification of clinical metrics from videos recorded with a handheld camera. Here we used DeepLabCut, an open-source framework for markerless pose estimation, to fine-tune a deep network to track 5 body keypoints (hip, knee, ankle, heel, and toe) in 82 below-waist videos of 8 patients with stroke performing overground walking during clinical assessments. We trained the pose estimation model by labeling the keypoints in 2 frames per video and then trained a convolutional neural network to estimate 5 clinically relevant gait parameters (cadence, double support time, swing time, stance time, and walking speed) from the trajectory of these keypoints. These results were then compared to those obtained from a clinical system for gait analysis (GAITRite®, CIR Systems). Absolute accuracy (mean error) and precision (standard deviation of error) for swing, stance, and double support time were within 0.04 ± 0.11 s; Pearson’s correlation with the reference system was moderate for swing times (r = 0.4–0.66), but stronger for stance and double support time (r = 0.93–0.95). Cadence mean error was −0.25 steps/min ± 3.9 steps/min (r = 0.97), while walking speed mean error was −0.02 ± 0.11 m/s (r = 0.92). These preliminary results suggest that single camera videos and pose estimation models based on deep networks could be used to quantify clinically relevant gait metrics in individuals poststroke, even while using assistive devices in uncontrolled environments. Such development opens the door to applications for gait analysis both inside and outside of clinical settings, without the need of sophisticated equipment.

Running-Related Biomechanical Risk Factors for Overuse Injuries in Distance Runners: A Systematic Review Considering Injury Specificity and the Potentials for Future Research

Abstract: Running overuse injuries (ROIs) occur within a complex, partly injury-specific interplay between training loads and extrinsic and intrinsic risk factors. Biomechanical risk factors (BRFs) are related to the individual running style. While BRFs have been reviewed regarding general ROI risk, no systematic review has addressed BRFs for specific ROIs using a standardized methodology.To identify and evaluate the evidence for the most relevant BRFs for ROIs determined during running and to suggest future research directions.Systematic review considering prospective and retrospective studies. (PROSPERO_ID: 236,832).PubMed. Connected Papers. The search was performed in February 2021.English language. Studies on participants whose primary sport is running addressing the risk for the seven most common ROIs and at least one kinematic, kinetic (including pressure measurements), or electromyographic BRF. A BRF needed to be identified in at least one prospective or two independent retrospective studies. BRFs needed to be determined during running.Sixty-six articles fulfilled our eligibility criteria. Levels of evidence for specific ROIs ranged from conflicting to moderate evidence. Running populations and methods applied varied considerably between studies. While some BRFs appeared for several ROIs, most BRFs were specific for a particular ROI. Most BRFs derived from lower-extremity joint kinematics and kinetics were located in the frontal and transverse planes of motion. Further, plantar pressure, vertical ground reaction force loading rate and free moment-related parameters were identified as kinetic BRFs.This study offers a comprehensive overview of BRFs for the most common ROIs, which might serve as a starting point to develop ROI-specific risk profiles of individual runners. We identified limited evidence for most ROI-specific risk factors, highlighting the need for performing further high-quality studies in the future. However, consensus on data collection standards (including the quantification of workload and stress tolerance variables and the reporting of injuries) is warranted.

Inchworm Inspired Multimodal Soft Robots With Crawling, Climbing, and Transitioning Locomotion

Abstract: Although many soft robots, capable of crawling or climbing, have been well developed, integrating multimodal locomotion into a soft robot for transitioning between crawling and climbing still remains elusive. In this work, we present a class of inchworm-inspired multimodal soft crawling-climbing robots (SCCRs) that can achieve crawling, climbing, and transitioning between horizontal and vertical planes. Inspired by the inchworm’s multimodal locomotion, which depends on the “ $\Omega$ ” deformation of the body and controllable friction force of feet, we develop the SCCR by 1) three pneumatic artificial muscles based body designed to produce “ $\Omega$ ” deformation; 2) two negative pressure suckers adopted to generate controllable friction forces. Then a simplified kinematic model is developed to characterize the kinematic features of the SCCRs. Lastly, a control strategy is proposed to synchronously control the “ $\Omega$ ” deformation and sucker friction forces for multimodal locomotion. The experimental results demonstrate that the SCCR can move at a maximum speed of 21 mm/s (0.11 body length/s) on horizontal planes and 15 mm/s (0.079 body length/s) on vertical walls. Furthermore, the SCCR can work in confined spaces, carry a payload of 500 g (about 15 times the self-weight) on horizontal planes or 20 g on vertical walls, and move in aquatic environments.

Triaxial Fast Tool Servo Using Hybrid Electromagnetic–Piezoelectric Actuation for Diamond Turning

Abstract: A high-performance triaxial fast tool servo (FTS) with the hybrid electromagnetic–piezoelectric actuation and the hybrid parallel–serial-kinematic structure is reported. Featuring the balanced and uniform actuation, in this article, a novel axis-symmetric linearized reluctance actuator is proposed to generate the planar motion in parallel, and the piezoactuated vertical motion is then serially carried by the planar motion within a limited space. Verified by the finite-element analysis, a two-stage design strategy is developed to optimally determine the multiphysical system parameters for the triaxial FTS, assisted by an analytical model of the electromagnetic circuit as well as the mechanical mechanism. As for the trajectory tracking, the loop-shaping tuned PID controller with a feedforward compensator is employed for each axis, and a damping controller is additionally designed for the planar motion. Finally, both open-loop and closed-loop performance of the prototype are carefully demonstrated.

Autonomous Driving on Curvy Roads Without Reliance on Frenet Frame: A Cartesian-Based Trajectory Planning Method

Abstract: Curvy roads are a particular type of urban road scenario, wherein the curvature of the road centerline changes drastically. This paper is focused on the trajectory planning task for autonomous driving on a curvy road. The prevalent on-road trajectory planners in the Frenet frame cannot impose accurate restrictions on the trajectory curvature, thus easily making the resultant trajectories beyond the ego vehicle’s kinematic capability. Regarding planning in the Cartesian frame, selection-based methods suffer from the curse of dimensionality. By contrast, optimization-based methods in the Cartesian frame are more flexible to find optima in the continuous solution space, but the new challenges are how to tackle the intractable collision-avoidance constraints and nonconvex kinematic constraints. An iterative computation framework is proposed to accumulatively handle the complex constraints. Concretely, an intermediate problem is solved in each iteration, which contains linear and tractably scaled collision-avoidance constraints and softened kinematic constraints. Compared with the existing optimization-based planners, our proposal is less sensitive to the initial guess especially when it is not kinematically feasible. The efficiency of the proposed planner is validated by both simulations and real-world experiments. Source codes of this work are available at https://github.com/libai1943/CartesianPlanner .

Safety-Critical Kinematic Control of Robotic Systems

Abstract: Over the decades, kinematic controllers have proven to be practically useful for applications like set-point and trajectory tracking in robotic systems. To this end, we formulate a novel safety-critical paradigm by extending the methodology of control barrier functions (CBFs) to kinematic equations governing robotic systems. We demonstrate a purely kinematic implementation of a velocity-based CBF, and subsequently introduce a formulation that guarantees safety at the level of dynamics. This is achieved through a new form of CBFs that incorporate kinetic energy with the classical forms, thereby minimizing model dependence and conservativeness. The approach is then extended to underactuated systems. This method and the purely kinematic implementation are demonstrated in simulation on two robotic platforms: a 6-DOF robotic manipulator, and a cart-pole system.