Motion Constraints Research Articles

Uncertainty-aware error modeling and hierarchical redundancy optimization for robotic surface machining

Industrial robots are commonly utilized in in-situ machining, but their inherent limitations in stiffness and positioning accuracy can pose challenges in achieving precise freeform surface milling. Previous research primarily focused on robot stiffness optimization and positioning error prediction separately, neglecting the uncertainty of robot errors and the modeling of surface profile errors. To address these issues, this work introduces a novel evaluation method for surface profile errors in robotic milling. It combines geometric error prediction models, based on kinematic and stiffness models, with non-geometric errors using Gaussian process regression. Moreover, collaborative optimization of multiple redundancies in robotic surface milling systems is crucial for reducing machining errors effectively. To achieve this, this work proposes a collaborative optimization method for optimizing the robot’s posture change sequence and the workpiece’s orientation, considering the motion constraints and error requirements of the robot. Among them, this work introduces the improved Whale Optimization Star Algorithm (WOA*) method to address the optimization problem of coupling robot posture and workpiece’s orientation. The proposed method’s effectiveness is confirmed through simulations and experiments, which clearly demonstrate its capability in improving error prediction and machining accuracy in robotic milling.

Fast monocular visual-inertial calibration and initialization with weak environmental control and motion control

Most existing visual-inertial systems require control of extrinsic environment and extrinsic motion to obtain calibration results and simplify the initialization model. The former limits the usage scenarios of the method, while the latter reduces the estimation accuracy of the state parameters of the navigation system. Therefore, a visual-inertial calibration and initialization method is necessary for weak environmental and motion control conditions. Firstly, to overcome the reliance on environmental control, we establish a motion constraint equation based on the relative consistency of visual-inertial sensing spatial motion and implement a visual-inertial online calibration method. Secondly, to eliminate the reliance on motion control, a dual graph optimization structure is be constructed. The initial optimization outcome serves as the foundation for achieving improved calibration results in the refined optimization. Thirdly, to improve initialization accuracy, a complete initial state model is considered to decouple and calculate the translation extrinsic parameters. These parameters are compensated into subsequent initialization, enhancing the robustness of the initialization process. The proposed method is validated on the EuRoC dataset, and experimental results demonstrate that it achieves more accurate calibration results at a faster rate than VINS-Mono in scenarios without calibration target and with limited vehicle motion. The navigation results of different scenarios demonstrate that our method can more effectively integrate visual-inertial data due to improved calibration and initialization estimates.

THE DEVELOPMENT AND VALIDATION OF A TACTICAL GRADE EGI SYSTEM FOR LAND VEHICULAR NAVIGATION APPLICATIONS

Abstract. Over recent years, the utilization of commercially available integrated navigation systems for the development of navigation algorithms has become increasingly commonplace. Nevertheless, the wide range of sensor quality on the market complicates system customization and restricts the evolution of navigation algorithms. This study aims to address these issues by creating an affordable, tactical-grade, real-time integrated navigation system, EGI-500 (Embedded GNSS and INS), encompassing both hardware and software components. EGI-500 incorporates a tactical-grade IMU500 and a Septentrio Mosaic-X5 GNSS receiver module. The integration process is segmented into three distinct stages. The first involves hardware integration, with an illustrative architecture diagram of the real-time navigation system. Second, we focus on data preprocessing, where a cross-correlation approach is proposed to tackle multi-sensor time synchronization issues, specifically to determine potential time lags in IMU data. The final phase covers the fusion of multi-sensor data and motion constraints. The Extended Kalman Filter (EKF) forms the backbone of this part, with Zero Velocity Update (ZUPT) and Non-Holonomic Constraints (NHC) being integrated into the Loosely Coupled (LC) scheme. Furthermore, the IMU calibration process is performed to ascertain necessary algorithmic parameters. Experimental results, conducted in diverse environments (open sky, GNSS challenging, and GNSS denied), will be presented in this paper. Comparisons with reference data indicate that the navigation accuracy of the developed integrated system, both in terms of hardware and navigation algorithm, achieves expected meter-level accuracy, fulfilling the "Which Lane" and "Which Road" level criteria in varied environments. Furthermore, outcomes from the GNSS denied environment align with predictions based on propagation error theory, demonstrating the feasibility of our navigation algorithm for tactical integrated navigation systems.

Safety of rope-guided conveyance systems

ABSTRACT The guide rope factor of safety (FOS) currently set in regulations in various countries is unduly conservative and constrains the maximum attainable depth for shafts using rope guides. It limits the amount of guide rope tension that can be imposed, which is important for controlling the lateral motion of conveyances. A lower FOS would lead to more constraint of lateral conveyance motion and therefore safer winding, but this implies a design trade-off of apparent rope safety versus conveyance guidance safety. This paper considers the safety of rope-guided shaft systems and questions whether the above trade-off is appropriately set. Rope safety, the role of guide ropes, and constraints on increasing guide rope tension are discussed. Precedents for review of rope FOS are recalled. The paper provides a motivation for reducing the regulated guide rope static FOS, which will give designers more flexibility in configuring rope-guided systems and will reduce capital costs—potentially significantly if the design is on the boundary of these constraints. Following precedents for a lower rope FOS, it is proposed that, if adopted, this reduction is made with the obligation to meet a code of practice for guide rope installation, inspection, and maintenance.

Pose estimation of an aerial construction robot based on motion and dynamic constraints

High accuracy pose estimation with high data rate of an aerial construction robot is the prerequisite for aerial construction robot control. A dynamic and motion constrained robust extended Kalman filter is developed for robot localization in aerial construction environment which is characterized by radio signal occlusion and few visual features. The motion constraints of the gondola are derived to estimate constrained pose of the gondola while the dynamic constraints of the gondola are introduced to inhibit spike-like data mutation when the gondola shakes back to the measurement range of the self-developed laser spot vision system. Moreover, the robust factor technique is adopted to inhibit instantaneous large outliers. The experimental results show that the pose estimation based on the dynamic and motion constrained robust extended Kalman filter achieves pose measurement of the aerial construction robot with mean position accuracy less than 0.015 m and mean attitude accuracy less than 0.003 rad.

Modeling and Control of an Octopus Inspired Soft Arm under Prescribed Spatial Motion Constraints

Modeling and Control of an Octopus Inspired Soft Arm under Prescribed Spatial Motion Constraints

Mechanically induced temperature oscillations in the coupled thermo-elasticity analysis of articulated dynamical systems

This article discusses a new approach for predicting and quantifying mechanically induced temperature oscillations in the coupled thermo-elasticity analysis of articulated mechanical systems (AMS). In this approach, the constrained equations of motion are solved simultaneously with discrete temperature equations obtained by converting heat partial-differential equation to a set of first-order ordinary differential equations. Dependence of the temperature gradients and their spatial derivatives on the position gradients, spinning motion, and curvatures is discussed. The approach captures dependence of the temperature-oscillation frequencies on the mechanical-displacement frequencies. The temperature field can be selected to ensure continuity of the temperature gradients at the nodal points. To generalize the AMS coupled thermo-elasticity formulation and capture the effect of the boundary and motion constraints (BMC) on the thermal expansion, the proposed method is based on integrating thermodynamics and Lagrange-D’Alembert principles. The absolute nodal coordinate formulation (ANCF) is used to describe continuum displacement and obtain accurate description of the reference-configuration geometry and change of this geometry due to deformations. A thermal-analysis large-displacement formulation is used to allow converting heat energy to kinetic energy, ensuring stress-free thermal expansion in case of unconstrained uniform thermal expansion. Cholesky heat coordinates are used to define an identity coefficient matrix for the efficient solution of the discretized heat equations. The approach presented is applicable to the two different forms of the heat equation used in the literature; one form is explicit function of the stresses while the other form does not depend explicitly on the stresses. Because of the need for using ANCF finite elements to achieve a higher degree of continuity in the coupled thermomechanical approach introduced in this article, the concept of the ANCF mesh topology is discussed.

A game-theoretic approach of cyberattack resilient constraint-following control for cyber–physical systems

Cyber–physical systems (CPSs) integrate network and physical components. The relevant network security issues have been receiving consistent attention. This paper considers cyber–physical systems whose control resilience is characterized by uncertainty, mechanical motion constraints, and exposure to cyber-attacks. To address these problems, this paper proposes a game-theoretic method based on constraint following theory where the moving trajectory requirements are considered as constraints. There can be adversary network environment which may launch cyberattacks and even deny providing service. With a creative control design, it is validated that the controlled system is resilient to cyberattack disturbance and other spoofing or mixed threat attacks. Three game rules, one non-cooperative game and two Stackelberg competitions, are adopted in this study to obtain the optimal control design parameter choice. It is proven that the optimal choice is the same from the three rules, thus making this optimal choice generic (i.e., nonspecific) and important. The superiority of the optimal control design parameter is demonstrated in simulations.

Motion-Control Strategy for a Heavy-Duty Transport Hexapod Robot on Rugged Agricultural Terrains

Legged agricultural transportation robots are efficient tools that can autonomously transport goods over agricultural terrain, and their introduction helps to improve the efficiency and quality of agricultural production. Their effectiveness depends on their adaptability to different environmental conditions, which is especially true for heavy-duty robots that exert ground forces. Therefore, this study proposes a motion-control strategy for a heavy-duty transport hexapod robot. Two critical tasks were accomplished in this paper: (1) estimating the support surface angle based on the robot’s foot position and body posture, and accordingly determining the motion constraint conditions on this support surface and the body posture based on energy optimization; (2) proposing an adaptive fuzzy impedance algorithm for real-time force–position composite control for adjusting foot position, in order to reduce the steady-state force tracking error caused by terrain stiffness, thus ensuring body stability through tracking of variable foot-end forces. An element of hardware in the loop control platform for a 3.55-ton device was designed and compared with the current popular force-control methods under different external contact terrains. The results show that the proposed control method can effectively reduce force errors, establish support forces faster on less-stiff environments, and reduce the torso tilt during phase switching.

Kinetic- and Strain-Energy Approaches in the Thermal Analysis of Constrained Mechanical Systems: A Comparative Study

Abstract Despite the unconstrained thermal expansion is assumed stress-free, the conventional FE approach requires formulating elastic forces, and this in turn leads to elastic stresses. A displacement-based formulation, on the other hand, can be used to address this limitation by converting the thermal energy to kinetic energy instead of strain energy. The fundamental differences between the strain- and kinetic-energy approaches are discussed. It is shown that the unconstrained thermal expansion predicted using the kinetic-energy approach is independent of the continuum constitutive model, and consequently, such a formulation can be used for both solids and fluids. The displacement (kinetic) and strain (stress) formulations are discussed to shed light on the mechanism of thermal expansion at the macroscopic level. The thermal-expansion displacement formulation (TEDF) and position-gradient multiplicative decomposition into thermal and mechanical parts are used to compute the thermal stresses due to boundary and motion constraints (BMC). TEDF implementation issues are discussed and constant matrices evaluated at a preprocessing stage after applying sweeping matrix technique to eliminate rigid-body thermal-displacement translational modes are identified. Furthermore, the softening effect due to the constitutive-model dependence on the temperature is investigated at high temperatures. Numerical results are presented to show fundamental differences between the TEDF approach that converts heat energy to kinetic energy and conventional FE approach that converts heat energy to strain energy that produces elastic stresses.

Unmanned surface vehicles (USVs) scheduling method by a bi-level mission planning and path control

To enhance the operational efficiency of container terminals, we propose a bi-level scheduling method that employs unmanned surface vehicles (USVs) to transport containers exclusively between different berths on the waterside of terminals. In the upper level, Considering time windows, berth coordination, energy replenishment, etc., we formulate the mission decision model to obtain a cost-effective USVs shipping solution by minimizing unplanned delay and total execution cost. In the lower level, considering path distance, path smoothness, and motion constraints, we develop the USV path control model to achieve the USV path planning and reduce path tracking errors. Finally, we integrate the advantages of the chaotic Electron Search and multi-population Genetic Algorithm (ES-mGA) to solve the bi-level USVs scheduling model. Experimental results demonstrate the effectiveness of our approach and algorithm in guiding the USVs to efficiently accomplish container transshipment and acquire stable USV sailing states.

Modeling and Implementation of Probability-Based Underwater Docking Assessment Index

The goal of underwater docking is to safely insert an autonomous underwater vehicle (AUV) into the docking sleeve of a docking station (DS). However, AUVs frequently experience disturbances in their operating environment under motional constraints owing to their shapes, which can significantly impede successful docking missions. Therefore, it is essential to develop an assessment method and corresponding index representing feasibility. In this study, we suggest a new assessment method and a probability-based assessment index to assess the underwater docking process, considering aforementioned motional constraints. The assessment is made for both the position and heading angle of the AUV, with the results presented in probabilistic figures. These figures are used to estimate the assessment index, which represents the probability of successful docking. The final decision on whether to dock or not can be made based on this index. When the index exceeds a predefined threshold, it indicates that the current docking process is reliable, and the docking will be successful. The suggested assessment method and the index were validated through tests conducted in various underwater environments. The results show that the probability-based index estimated through the proposed method can be grounds for successful docking.

Learning full context feature for human motion prediction

Human motion prediction aims to predict the target poses given the previous poses. Most existing methods are devoted to extracting richer motion features from only the given previous poses to predict the target poses. However, we consider that the post poses after the target poses are helpful in acquiring the context feature and constraint between neighbor motions, which is also important for motion prediction. In this paper, we explore to make use of the post motion information for a powerful human motion prediction method. Specifically, we propose a human motion prediction model which learns the motion constraint from both the previous and post poses, in order to fully utilize the context features of the target poses. During training, the proposed memory dictionary module is used to learn the mapping from previous features to post features. In testing, the proposed memory dictionary module fully exploits the learned mapping to calculate the future motion feature according to the input previous feature. Thus, the context feature of human motion is enriched in our method. We evaluate the proposed method on two large-scale datasets, Human3.6M and CMU-Mocap. The experimental results demonstrate that our method improves the motion prediction performance, especially for long-term human motion.

Trajectory Deformation With Constrained Optimization for Bilateral Rehabilitation Robots

Robot-aided bilateral treatment has been verified to be an effective training program for hemiplegic rehabilitation. In this article, a reference-free active control framework based on optimal trajectory deformation is proposed to ensure the safety requirements in the leader–follower paradigm of bilateral treatment. A constrained optimization method is developed to handle the motion constraints, which are constructed by quantitative assessments of typical impairment in stroke patients, such as reduced range of motion, resistance to passive movement, and disturbed quality of movement. Then, by optimally deforming the robotic trajectory, abnormal motion patterns that lead to safety issues can be rectified. Furthermore, the physically interactive trajectory deformation is employed to achieve active control without a predefined trajectory. At last, all approaches are verified on a robotic platform with a 2-DoF lower-limb exoskeleton. Experimental results demonstrate the effectiveness of proposed control scheme in rectifying abnormal motion patterns and enhancing human–robot interaction.

Developing an Ovine Model of Impact Traumatic Brain Injury

<div>Traumatic brain injury is a leading cause of global death and disability. Clinically relevant large animal models are a vital tool for understanding the biomechanics of injury, providing validation data for computation models, and advancing clinical translation of laboratory findings. It is well-established that large angular accelerations of the head can cause TBI, but the effect of head impact on the extent and severity of brain pathology remains unclear. Clinically, most TBIs occur with direct head impact, as opposed to inertial injuries where the head is accelerated without direct impact. There are currently no active large animal models of impact TBI. Sheep may provide a valuable model for studying TBI biomechanics, with relatively large brains that are similar in structure to that of humans. The aim of this project is to develop an ovine model of impact TBI to study the relationships between impact mechanics and brain pathology. An elastic energy impact injury device has been developed to apply scalable head impacts to rapidly rotate the head without causing hard tissue damage. A motion constraint device has been developed to limit the head motion to a single plane of rotation. The apparatus has been tested using deceased animals to assess the controllability of impact velocities, the repeatability of head kinematics, and the dynamic response of the head to impact. Impact velocities are effectively controlled by modulating the elastic energy stored in the impact piston. The resulting head kinematics are somewhat variable, and are influenced by impact location, time-dependent postmortem tissue changes, and specimen head and neck physiology. Model development will continue, and in vivo testing will be conducted to assess the brain pathology following impacts of varying severity.</div>

Low-Field Nuclear Magnetic Resonance Investigation on Early Hydration Characterization of Cement Paste Mixed with Mineral Admixtures

Mineral admixtures (MA), like fly ash (FA), silica fume (SF), and slag (S), are usually added to cement-based materials to improve their compactness and further enhance their mechanical properties, permeability resistance, and durability. In this study, low-field nuclear magnetic resonance (LF-NMR) is adopted to explore the evolution of the early hydration characterization of cement-based materials with MA by testing the transverse relaxation time T2. Meanwhile, the effect of MA on mechanical properties is analyzed by measuring compressive and flexural strength. The results show that, in the early hydration (0–7 days), the T2 distribution shows a trend of gradually moving to a short relaxation time and changes from a double peak to a single main peak. The decrease in T2i (main peak vertex) means that the evaporated water is gradually distributed in smaller pores with more motion constraints. However, the type and content of MA have little effect on T2i. Porosity gradually decreases in the period of early hydration. The addition of MA causes the porosity to decrease, and the order influence is FA > S > SF, i.e., the porosities of cement paste with 0%MA, 10%FA + 10%SF, 10%FA + 10%S, and 20%FA at 7 days are 48%, 44.5%, 40.7%, and 40.2%, respectively. Additionally, the addition of MA to cement-based materials also decreases the early strength, and the influence order is FA > S > SF, i.e., the compression strength values of cement paste with 0%MA, 10%FA + 10%SF, 10%FA + 10%S, and 20%FA at 7 days are 47.8 MPa, 40.1 MPa, 38.6 MPa, and 37 MPa, respectively.

MCBM-SLAM: An Improved Mask-Region-Convolutional Neural Network-Based Simultaneous Localization and Mapping System for Dynamic Environments

Current research on SLAM can be divided into two parts according to the research scenario: SLAM research in dynamic scenarios and SLAM research in static scenarios. Research is now relatively well established for static environments. However, in dynamic environments, the impact of moving objects leads to inaccurate positioning accuracy and poor robustness of SLAM systems. To address the shortcomings of SLAM systems in dynamic environments, this paper develops a series of solutions to address these problems. First, an attention-based Mask R-CNN network is used to ensure the reliability of dynamic object extraction in dynamic environments. Dynamic feature points are then rejected based on the mask identified by the Mask R-CNN network, and a preliminary estimate of the camera pose is made. Secondly, in order to enhance the picture matching quality and efficiently reject the mismatched points, this paper proposes an image mismatching algorithm incorporating adaptive edge distance with grid motion statistics. Finally, static feature points on dynamic objects are re-added using motion constraints and chi-square tests, and the camera’s pose is re-estimated. The SLAM algorithm of this paper was run on the KITTI and TUM-RGBD datasets, respectively, and the results show that the SLAM algorithm of this paper outperforms the ORB-SLAM2 algorithm for sequences containing more dynamic objects in the KITTI dataset. On the TUM-RGBD dataset, the Dyna-SLAM algorithm increased localization accuracy by an average of 71.94% when compared to the ORB-SLAM2 method, while the SLAM algorithm in this study increased localization accuracy by an average of 78.18% when compared to the ORB-SLAM2 algorithm. When compared to the Dyna-SLAM technique, the SLAM algorithm in this work increased average positioning accuracy by 6.24%, proving that it is superior to Dyna-SLAM.

Cooperation of robot manipulators with motion constraint by real-time RNN-based finite-time fault-tolerant control

The nonlinear cooperative dynamics of m robots with motion constraint and faulty input saturation is addressed. To the best of our knowledge, the motion constraint for the cooperative control of multiple robots is first discussed as one of the designed concepts such that the proposed cooperative control system is more practical and its performance is also improved. Together with a finite-time concept, a real-time recurrent-neural-network-based finite-time fault-tolerant cooperation constraint control (RT-RNN-FTFTCCC) is designed to tackle the aggregately dynamic uncertainties brought about by motion constraint error, cooperation uncertain dynamics, disturbance torque, faulty input saturation, unsatisfying skew-symmetric matrix condition, and others. Moreover, the stabilized learning law includes two terms to cover all the range of filtering cooperation errors such that learning weights converge even in uncertain circumstances. Finally, the stability analysis of the overall cooperation control system is achieved by Lyapunov stability theory. In comparison to adaptive compensation control, recurrent-neural-network-based control without the motion constraint compensation, and different stabilized learning laws, the cooperative control of bi-robotic arms of 3-link in-plane with new constraint validates the improvement and robustness of the proposed control.

Multi-source motion constrained model predictive control for tractor-trailer trucks with coupled dynamics

This paper proposes a constrained model predictive control (MPC) for tractor-trailer trucks with the complicated physical characteristics, where motion constraints can be considered to govern the vehicle maneuver. Firstly, a targeted theoretical derivation method is developed to establish a more accurate nonlinear model for articulated vehicles, with expressly considering the articulation coupling effects. As an essential basis, the kinematic coupling behavior is further investigated. Secondly, multi-source motion constraints are introduced in particular with the analysis of nonholonomic constraints on the underactuated trailer: (i) keeping trucks traveling in the feasible domain away from collision; (ii) maintaining the vehicle states in a stable field through the steady-state response and side-slip thresholds. Thirdly, a constrained MPC controller is constructed for the automatic tracking operation, wherein vehicle demands of position security and yaw stability will be taken into reasonable consideration. For this purpose, vehicle states information can be fully utilized in a parallel approach to optimize quadratic programming (QP) in MPC: model prediction and updated constraints. In the end, the contrasted-simulation analysis is carried out to verify the accuracy of derived model and effectiveness of the designed controller, along with the feasibility of the additive multi-source constraints.

Quadratically Constrained Linear Programming-based energy-efficient driving for High-speed Trains with neutral zone and time window

Among the numerous challenges that are faced during the daily operation of high-speed rail, the management of the neutral zones in the catenary during unexpected disturbances remains underinvestigated. This study proposes a computerised method for managing the operations of a high-speed train with regenerative braking for passing neutral zones under disturbance to minimise energy consumption. For the first time, different mechanism-based Automatic Passing Neutral Zone systems, including the Magnetic Induction System and Automatic Train Protection, are analysed and modelled as location-based and time-based constraints, respectively. Motion constraints caused by disturbances are described by time windows. Forced coasting and air brake-allowed passing neutral zone rules are considered in these models. The original nonlinear model with location-based constraints is transcribed as a quadratically constrained linear model and then solved. The optimality consistency and its establishment condition between the original and transcribed models are analysed based on the Karush–Kuhn–Tucker conditions. A high-quality solution is obtained when the establishment condition holds. The model with time-based constraints is novelly transformed into an optimal switching point problem. A series of sub-problems are iteratively and efficiently solved. Comprehensive experiments are conducted based on practical data from a high-speed rail system in China. The proposed method is significantly beneficial when compared to Mixed-integer Linear Programming and Artificial Driving Algorithms. Moreover, the impacts of different mechanism-based automatic passing neutral zone systems, operation rules, and a combination of time window settings are extensively analysed.