Adaptive Motion Research Articles

Merit-Based Motion Planning for Autonomous Vehicles in Urban Scenarios.

Safe and adaptable motion planning for autonomous vehicles remains an open problem in urban environments, where the variability of situations and behaviors may become intractable using rule-based approaches. This work proposes a use-case-independent motion planning algorithm that generates a set of possible trajectories and selects the best of them according to a merit function that combines longitudinal comfort, lateral comfort, safety and utility criteria. The system was tested in urban scenarios on simulated and real environments, and the results show that different driving styles can be achieved according to the priorities set in the merit function, always meeting safety and comfort parameters imposed by design.

UWB Radio-Based Motion Detection System for Assisted Living.

Because of the ageing population, the demand for assisted living solutions that can help prolonging independent living of elderly at their homes with reduced interaction with caregivers is rapidly increasing. One of the most important indicators of the users’ well-being is their motion and mobility inside their homes, used either on its own or as contextual information for other more complex activities such as cooking, housekeeping or maintaining personal hygiene. In monitoring users’ mobility, radio frequency (RF) communication technologies have an advantage over optical motion detectors because of their penetrability through the obstacles, thus covering greater areas with fewer devices. However, as we show in this paper, RF links exhibit large variations depending on channel conditions in operating environment as well as the level and intensity of motion, limiting the performance of the fixed motion detection threshold determined on offline or batch measurement data. Thus, we propose a new algorithm with an online adaptive motion detection threshold that makes use of channel impulse response (CIR) information of the IEEE 802.15.4 ultra-wideband (UWB) radio, which comprises an easy-to-install robust motion detection system. The online adaptive motion detection (OAMD) algorithm uses a sliding window on the last 100 derivatives of power delay profile (PDP) differences and their statistics to set the threshold for motion detection. It takes into account the empirically confirmed observation that motion manifests itself in long-tail samples or outliers of PDP differences’ probability density function. The algorithm determines the online threshold by calculating the statistics on the derivatives of the 100 most recent PDP differences in a sliding window and scales them up in the suitable range for PDP differences with multiplication factors defined by a data-driven process using measurements from representative operating environments. The OAMD algorithm demonstrates great adaptability to various environmental conditions and exceptional performance compared to the offline batch algorithm. A motion detection solution incorporating the proposed highly reliable algorithm can complement and enhance various assisted living technologies to assess user’s well-being over long periods of time, detect critical events and issue warnings or alarms to caregivers.

High accuracy adaptive motion control for a robotic manipulator with model uncertainties based on multilayer neural network

AbstractA robotic manipulator is widely used nowadays, and its high‐accuracy motion control has become a hot topic. However, since it is a very complicated control plant, there exist many structural and unstructured uncertainties in its mathematical model, which would degrade the manipulator control performance when the traditional model‐based control method such as feedback linearization control is adopted. In this paper, a practical method that combines feedback linearization control with multilayer neural network (MNN)‐based model uncertainties observer is proposed for high‐accuracy motion control of a robotic manipulator. The proposed controller not only accounts for the parametric uncertainties but also for the external disturbance. A multilayer neural network capable of online learning is designed to estimate all of the model uncertainties, which can be compensated through the feedforward cancellation technique. A weight adaption law for the online learning neural network is derived based on Lyapunov stability theory. Finally, a feedback linearization method with the MNN‐based observer is designed to stabilize the whole system. The stability proof shows that bounded stability of a manipulator system with the proposed controller is guaranteed. Extensive simulation results show that this control strategy achieves high‐accuracy tracking control in the presence of the manipulator parametric uncertainties and external disturbance.

Assessment of Different File Systems for Working Time Based on Glide Path, Operating Kinetics, and the Fracture Resistance

This study was done to assess the time to achieve the working distance based on the size of the glide path, operating kinetics, and the fracture resistance of different file systems. One hundred and eighty mandibular premolars were divided into two groups of 90 each. Group I was subjected to continuous 360° rotary motion and group II to adaptive motion. Twisted File (TF) and Endostar E3 file methods were practiced in groups. The time (seconds) to achieving desired working length was recorded. Failures were classified as torsional failure or flexural failure. The time taken by glide path size 15 in group I was 5.90 ± 4.06 seconds and in group II was 6.12 ± 4.16 seconds. The time taken by glide path size 20 in group I was 5.86 ± 3.12 seconds and in group II was 4.22 ± 2.10 seconds, with 25 size the time taken in group I was 5.32 ± 2.48 seconds and in group II was 3.16 ± 3.14 seconds. The time taken by group I was less as compared to group II, and the difference was significant (p < 0.05). There was a significant difference in time taken with different number files in both groups (p < 0.05). The mean time taken reaching the working length for continuous rotation was less as compared to TF adaptive motion; however, the difference was nonsignificant (p > 0.05). We recorded higher instrument separation and deformation with the TF method and adaptive gesture. The TF system showed additional time to achieve the working distance as compared to the Endostar E3 system. The TF system showed higher instrument separation and deformation, and it requires additional time to achieve the working distance compared to the Endostar E3 system. Hence, the Endostar E3 system is effective in achieving required clinical results.

Customizing a self-healing soft pump for robot

Recent advances in soft materials enable robots to possess safer human-machine interaction ways and adaptive motions, yet there remain substantial challenges to develop universal driving power sources that can achieve performance trade-offs between actuation, speed, portability, and reliability in untethered applications. Here, we introduce a class of fully soft electronic pumps that utilize electrical energy to pump liquid through electrons and ions migration mechanism. Soft pumps combine good portability with excellent actuation performances. We develop special functional liquids that merge unique properties of electrically actuation and self-healing function, providing a direction for self-healing fluid power systems. Appearances and pumpabilities of soft pumps could be customized to meet personalized needs of diverse robots. Combined with a homemade miniature high-voltage power converter, two different soft pumps are implanted into robotic fish and vehicle to achieve their untethered motions, illustrating broad potential of soft pumps as universal power sources in untethered soft robotics.

PID-type sliding mode-based adaptive motion control of a 2-DOF piezoelectric ultrasonic motor driven stage

This paper presents a new scheme of adaptive sliding mode control (ASMC) for a piezoelectric ultrasonic motor driven X–Y stage to meet the demand of precision motion tracking while addressing the problems of unknown nonlinear friction and model uncertainties. The system model with Coulomb friction and unilateral coupling effect is first investigated. Then the controller is designed with adaptive laws synthesized to obtain the unknown model parameters for handling parametric uncertainties and offsetting friction force. The robust control term acts as a high gain feedback control to make the output track the desired trajectory fast for guaranteed robust performance. Based on a PID-type sliding mode, the control scheme has a simple structure to be implemented and the control parameters can be easily tuned. Theoretical stability analysis of the proposed novel ASMC is accomplished using a Lyapunov framework. Furthermore, the proposed control scheme is applied to an X–Y stage and the results prove that the proposed control method is effective in achieving excellent tracking performance.

A Novel Approach for Model-Based Pedestrian Tracking Using Automotive Radar

Pedestrians represent acutely complex and agile targets whose accident prevention has the highest priority concerning highly automated vehicles in urban traffic. Consequently, detection and tracking methods are necessary to determine motion behavior quickly and with high precision. With advancing microelectronics, powerful radar sensors and novel antenna structures are available for pedestrian detection systems, which can utilize unique features such as micro-Doppler signatures in several dimensions and high resolution. This paper presents a micro-Doppler based leg tracking framework that enables behavioral indications in a few sensor cycles. An adaptive motion model representing the kinematic locomotion of a human is derived to propagate the foot motion. Joint probabilistic data association assigns the detections to the respective leg and allows its temporal filtering of the position, micro-Doppler velocity, and kinematic parameters using an extended Kalman filter yielding real-time implementation capability. The presented approach provides the entire signal chain from raw radar data processing to extended multi-object state estimation based on highly relevant safety-critical motion maneuvers measured with radar parameterization corresponding to current serial short-range sensors.

Adaptive Motion Cueing Algorithm Based on Fuzzy Logic Using Online Dexterity and Direction Monitoring

Motion cueing algorithm (MCA) is used to reproduce the realistic driving motion feeling for the user of the virtual vehicle using linear acceleration and angular velocity motion signals while respecting the physical constrains of the simulation-based motion platform. Classical washout filter is the most common and commercial type of the MCA because of easy tuning, short processing time, and simplicity. The classical washout filter is a conservative method in using workspace because of the worst-case scenario tuning technique. Also, the existing adaptive washout filter based on time-varying cut-off frequency does not consider the dexterity of the motion platform as considering this factor leads to use the high acceleration areas of the platform workspace. In this article, an adaptive washout filter is designed and developed based on time-varying cut-off frequency using dexterity and direction monitoring factors to use the available high acceleration areas of the platform for better generation of the high-frequency part of the motion signal. The proposed adaptive washout filter is composed of three fuzzy logic units for each mode including longitudinal, lateral, yaw, and heave and each unit has two inputs including motion sensation error between the real vehicle driver and user of the motion platform and the weight of the end-effector which is a parameter based on dexterity and current position of the end-effector. Each fuzzy logic unit has also one output which is the selected cut-off frequency of the related washout filter. The proposed adaptive washout filter uses the high dexterous areas of the workspace because it considers the dexterity of the motion platform in calculation of the end-effector weight as the second input of the fuzzy logic units. The proposed adaptive washout filter is validated in MATLAB/Simulink programming language and the experimental results indicate that the proposed adaptive washout filter performed better than existing adaptive and classical washout filters in terms of motion sensation error between the driver of real vehicle and the user of motion platform as well as usage of the platform workspace.

New Adaptive Control Methods for $n$-Link Robot Manipulators With Online Gravity Compensation: Design and Experiments

For robot manipulators subject to unmeasurable/uncertain plant parameters, this article designs a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">new</i> adaptive motion controller, which ensures positioning errors to converge to zero and provides accurate gravity compensation. Meanwhile, specific motion constraints are also satisfied during the entire control process. Additionally, the proposed controller is further extended to address output feedback control without velocity measurement/numerical differential operations. A useful feature of this article is that <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">neither</i> complicated gain constraints <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">nor</i> the upper/lower bounds of model parameters/matrices in the dynamics are required in controller design and analysis, which greatly facilitates practical applications. Meanwhile, by introducing a nonlinear auxiliary term (related to motion constraints and error signals) into the proposed controllers, all links accurately reach their desired positions <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">without</i> exceeding the preset constraints, while the gravity vector is estimated online to eliminate static errors. Additionally, the asymptotic stability of the system equilibrium point is strictly proven; more importantly, the difficulty of stability analysis is significantly decreased based on the elaborately constructed Lyapunov function candidate. Compared with existing controllers, the main merits of the designed control schemes include <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">fewer</i> control gain conditions, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">more concise</i> closed-loop stability analysis, and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">higher</i> safety satisfying specific constraints. Finally, some hardware experiments are carried out to validate the performance of the presented controllers.

Application of optimization algorithms to adaptive motion control for repetitive process

The application of optimization algorithms to adaptive motion control is proposed in this paper. In order to provide optimal system response, optimization algorithm is used as adjustment mechanism of controller coefficients. Most of optimization algorithms are not able to work in continuous optimization mode and with non-constant search space (i.e. dataset). For this reason, the introduction of a novel approach, Adaptive Procedure for Optimization Algorithms (APOA), that allows to apply most of optimization algorithms to adaptation process seems to be necessary. Such an algorithm is innovative, due to its universality in terms of applied optimization algorithm. Its application allows to obtain optimal motion control in modern electric drives. The proposed APOA is presented together with the novel desired-response adaptive system (DRAS) approach for repetitive processes. This solution provides unchanged system response regardless of plant parameters variation or external disturbances. The developed adaptive approach based on optimization algorithm is implemented in permanent magnet synchronous motor (PMSM) drive to adapt state feedback speed controller during moment of inertia variations. Since the proposed DRAS is model-free approach, the adaptation procedure is immune to issues related to plant parameters mismatch. The obtained results prove that proposed adaptive speed controller for PMSM assures desired system response regardless of the moment of inertia variation.

Postoperative Pain After Using Reciprocating Motion with Reciproc Files Versus Adaptive Motion with Twisted File Adaptive in Instrumentation of Necrotic Mandibular Molars: A Randomized Clinical Trial.

Objective:The objective of the present study was to assess the effect of using Reciproc (RC), (VDW GmbH, Munich, Germany) files with reciprocating motion versus Twisted File Adaptive (TFA, Kerr, Orange, California, USA) system with adaptive motion on post-instrumentation and post-obturation pain of necrotic mandibular molars.Methods:Fifty-eight patients with mandibular molar assessed at 3 intervals; 6, 12 and, 24 hrs. Mann Whitney U and Friedman test was used for data analysis, and the significance level was set to (P≤0.05).Results:There was no statistically significant difference in the mean values of post-instrumentation pain at each time interval for the RC and TFA groups (P>0.05). Pain decreased in each group with a statistically significant difference from preoperative condition to all six post-instrumentation time intervals (P<0.001). In each group, post-instrumentation mean pain values at 6, 12, and 24 hrs were higher than post-obturation pain values at 6, 12, and 24 hrs with a statistically significant difference (P<0.001).Conclusion:TFA and RC had a similar impact on post-instrumentation and post-obturation pain. The post-instrumentation pain was higher than post-obturation pain in both groups.

Structural-parametric synthesis of the tracking filter based on decomposition by the target functional with adaptation to trajectory disturbances

Traditional Kalman-type tracking filters are based on a kinematic motion model, which leads to the occurrence of dynamic errors, which significantly increase during target maneuvering. One of the solutions to this problem is to develop a model of motion dynamics with the ability to adapt its structure to external influences. It is shown that the use of a dynamic model of motion in the filter, which takes into account the inertia of the target and the forces acting on it, makes it possible to significantly increase the efficiency of the state assessment. Purpose is to development of an algorithm for assessing the position of a maneuvering object, effective in terms of accuracy criterion. The use of an adaptive motion model as part of the filter provides an increase in the estimation accuracy in comparison with the classical Kalman filter, which is confirmed by the performed numerical modeling.

Development and Evaluation of an Adaptive Multi-DOF Finger with Mechanical-Sensor Integrated for Prosthetic Hand.

To realize the adaptive grasping of objects with diverse shapes and to capture the joint angles of the finger, a multi degree of freedom (DOF) adaptive finger for prosthetic hand is proposed in this paper. The fingers are designed with three joints. The maximum rotation angle of the finger joints is 90°. The angle at which the finger joints bend can be captured. Firstly, the prototype design, forward kinematics and force analysis of phalanges are described in detail. In order to achieve an adaptive motion pattern similar to that of the human hand, this paper investigates the optimization of the torsion spring stiffness coefficient so that the metacarpophalangeal (MCP) joints, proximal interphalangeal (PIP) joints, and distal interphalangeal (DIP) joints of the bionic finger meet a motion ratio of approximately 3:3:1. Then, in order to realize the joint angle measurement in the process of grasping an object, the mechanical-sensor integrated finger joint is designed, and the composition, angle measurement principle and measurement circuit are introduced in detail. Finally, joint angle measurement, movement law evaluation and object grasping experiments are performed to verify the validity of the designed finger. The experimental results show that the root-mean-square (RMS) of the DIP, PIP and MCP angle measurement errors are 0.36°, 0.59° and 0.32°, respectively. The designed finger is able to grasp objects with different shapes stably.

An Adaptive Motion Planning Technique for On-Road Autonomous Driving

This paper presents a hierarchical motion planning approach based on discrete optimization method. Well-coupled longitudinal and lateral planning strategies with adaptability features are applied for better performance of on-road autonomous driving with avoidance of both static and moving obstacles. In the path planning level, the proposed method starts with a speed profile designing for the determination of longitudinal horizon, then a set of candidate paths will be constructed with lateral offsets shifting from the base reference. Cost functions considering driving comfort and energy consumption are applied to evaluate each candidate path and the optimal one will be selected as tracking reference afterwards. Re-determination of longitudinal horizon in terms of relative distance between ego vehicle and surrounding obstacles, together with update of speed profile, will be triggered for re-planning if candidate paths ahead fail the safety checking. In the path tracking level, a pure-pursuit-based tracking controller is implemented to obtain the corresponding control sequence and further smooth the trajectory of autonomous vehicle. Simulation results demonstrate the effectiveness of the proposed method and indicate its better performance in extreme traffic scenarios compared to traditional discrete optimization methods, while balancing computational burden at the same time.

A Novel AMS-DAT Algorithm for Moving Vehicle Detection in a Satellite Video

Satellite videos have recently served as a new data source for a wide range of applications in traffic management and military surveillance. Due to its wider coverage, satellite videos show more advantages in large-scale monitoring than ground surveillance videos. However, pseudomotion background and low-resolution targets pose new challenges to moving vehicle detection in satellite videos, resulting in poor performance of conventional target detection methods when applied to satellite videos. To overcome this difficulty, we propose a novel moving vehicle detection approach using adaptive motion separation and difference accumulated trajectory. Specifically, a new indicator is designed to assist adaptive separation of moving targets and background, considering the scale invariance of vehicles in satellite videos. Meanwhile, we offer a vehicle discrimination algorithm based on a differential accumulated trajectory to distinguish the moving vehicles from the pseudomotion background. Experimental results on two satellite video data sets demonstrate that the proposed approach achieves better detection performance over the state-of-the-art moving vehicle detection methods.

Adaptive Control of a Nonlinear Convertiplane under Conditions of Uncertainty

Purpose of research. The article deals with the problem of monitoring water areas in order to control their physical and chemical conditions using a flying laboratory (FL) which includes an aircraft with attachable water intake equipment and a software and hardware system. A specific feature of the monitoring of surface waters is the unpredictable behavior of air and water, periodic absence of visual contact with the aircraft, the uncertainty of tricopter characteristics. Therefore, the purpose of this article is to study the parameters of the control system (CS) to meet the requirements for the accuracy of aircraft positioning in conditions of uncertainty of external parameters. Methods. Theoretical mechanics and robot mechanics methods were used to solve the set tasks. Methods for mathematical modeling of dynamic systems were used to study the patterns of convertiplane movement. Adaptive control with a reference model were used to plan and control the movement of the aircraft. Results. The use of adaptive FL motion control made it possible to ensure convergence to zero of the tracking errors i.e., the difference between the output signals and the reference model. The proposed control system gives a good result with small disturbing effects. The parameters of the regulator that ensure the quality indicators of the ACS within the specified limits are determined. Conclusion. A mathematical model was developed and mathematical modeling of the convertiplane movement under conditions of uncertainty of external influences was performed. The problem of parameter control of a convertiplane was considered when the control coefficients were available for setting. The developed algorithms in the adaptive control system made it possible to provide faster suppression of external disturbances in comparison with the traditional PID control system for the case of a linear description of the controlled object.

Robust Hierarchical Adaptive Fuzzy Relative Motion Coordination for Feature Points of Two Rigid Bodies With Input and Output Constraints

A model-based six-degrees-of-freedom relative motion coordinated control approach is proposed based on the adaptive hierarchical fuzzy logic system and the adaptive backstepping technique for the relative position tracking and attitude synchronization between feature points of two rigid bodies subject to control input constraints, output constraints, and model uncertainties. The control input saturation is compensated by the nonlinear anti-windup compensator, and the output constraints are handled by the barrier Lyapunov function-based backstepping design. The unknown misalignment vector of the feature point with respect to the center of the mass for the chaser is estimated by the element-wise adaptive law, while the model uncertainties and unknown dynamical couplings are compensated by the adaptive hierarchical fuzzy logic system to decrease the computational burden with respect to the traditional adaptive fuzzy systems. The ultimately uniformly bounded convergence of the relative pose and relative velocities is analyzed in the Lyapunov framework and the effectiveness of the proposed approach is validated by the numerical simulations.

Adaptive control of manipulator based on neural network

With the development of economic science and technology, the development of computer vision has undergone rapid changes, and various products relying on computer vision are also more and more, such as smart home, robot technology, and so on. At present, robot technology has become a very important part of the development of human science and technology, and in the field of industrial robots, the most rapid development is the robot with robot arm adaptive motion. It is very necessary to study the adaptive motion control of the manipulator based on machine learning. The robot with the adaptive motion of the manipulator can carry out logistics express sorting, operate in the doors and windows outside the building, and pick fruits in the orchard, which can ensure the effective implementation of hard work. Therefore, this paper proposes a mechanical adaptive control method based on a neural network. According to the motion model of the manipulator, the RBF neural network model is used to judge the stability of the system according to the Lyapunov function. The related algorithms of machine learning and multi-degree of freedom manipulator are studied and improved. The RBF neural network model approximates the unknown function infinitely and then establishes the complex motion model. Aiming at the adaptive neural network of a manipulator, a network adaptive terminal control method is proposed. Firstly, a stable manipulator motion system is designed by using a neural network, and then the terminal synovial controller is designed by using backstepping control technology. The stability of the method is proved by using the approximation virtual control technology of the neural network. The adaptive control is realized by using the learning and self-adaptability of the neural network; thus, the stability analysis of the closed-loop system is realized.

Root Canal Transportation after Root Canal Preparation with ProTaper Next, WaveOne Gold, and Twisted Files.

Background: Root canal preparation during endodontic treatment may be associated with various complications, including a change in the original pathway of the root canal lumen. The aim of our study was to determine whether files of similar sizes that use various movement kinematics (rotary, reciprocal, adaptive motion) cause root canal transportation, and whether the differences between such systems are statistically significant. Methods: The degree of root canal transportation (DT) was calculated with the use of computed tomography scans for 3 groups of teeth (for each group: n = 20) in which the root canals were prepared using either rotary (ProTaper Next—PTN), reciprocal (WaveOne Gold—WOG), or adaptive movement (Twisted Files—TF) instruments. Results: For rotary ProTaper Next instruments, the mean value of the DT index was 0.0795 (SD = 0.0179) for 3 mm from the apex, 0.09 (SD = 0.0262) for 6 mm from the apex, and 0.106 (SD = 0.0221) for 9 mm from the apex. For reciprocal WaveOne Gold Primary instruments, the mean value of the DT index was 0.0355 (SD = 0.015) for 3 mm from the apex, 0.061 (SD = 0.02) for 6 mm from the apex, and 0.08 (SD = 0.25) for 9 mm from the apex. For Twisted Files, the mean value of the DT index was 0.05 (SD = 0.03) for 3 mm from the apex, 0.092 (SD = 0.17) for 6 mm from the apex, and 0.08 (SD = 0.02) for 9 mm from the apex. Conclusions: The use of PTN, WOG, and TF files resulted in root canal transportation to a different degree. The use of rotary PTN files produced the most transported preparation, whereas the use of WOG files produced the conservative root canal preparation that allowed the retention of the original shape of the root canal.

Adaptive Feedforward Control of a Pressure Compensated Differential Cylinder

This paper presents the design, simulation and experimental verification of adaptive feedforward motion control for a hydraulic differential cylinder. The proposed solution is implemented on a hydraulic loader crane. Based on common adaptation methods, a typical electro-hydraulic motion control system has been extended with a novel adaptive feedforward controller that has two separate feedforward states, i.e, one for each direction of motion. Simulations show convergence of the feedforward states, as well as 23% reduction in root mean square (RMS) cylinder position error compared to a fixed gain feedforward controller. The experiments show an even more pronounced advantage of the proposed controller, with an 80% reduction in RMS cylinder position error, and that the separate feedforward states are able to adapt to model uncertainties in both directions of motion.