•Critical issues and practices for myoelectric interfaces are discussed.•Robust classification in real use is the main challenge of sEMG interfaces.•Expected trend is toward regression and ...factorization methods and sensor fusion.•Simplified sEMG setup may be adequate in real-life environment.•Potential uses of sEMG interfaces are rapidly increasing.
This review discusses the critical issues and recommended practices from the perspective of myoelectric interfaces. The major benefits and challenges of myoelectric interfaces are evaluated. The article aims to fill gaps left by previous reviews and identify avenues for future research. Recommendations are given, for example, for electrode placement, sampling rate, segmentation, and classifiers. Four groups of applications where myoelectric interfaces have been adopted are identified: assistive technology, rehabilitation technology, input devices, and silent speech interfaces. The state-of-the-art applications in each of these groups are presented.
Nonlinear model predictive control (NMPC) is a control strategy based on finding an optimal control trajectory that minimizes a given objective function. The optimization is recalculated at each ...control cycle and only the first control values are actually used. The dynamics of the system can be nonlinear and there can be constraints on states and controls. A new toolkit called VIATOC has been developed that can be used to automatically generate the code needed to implement NMPC. The generated code is self-contained ANSI C and the compiled program has a small footprint. In VIATOC, the gradient projection method is used to solve the nonlinear optimization problem. Barzilai–Borwein type step length selection for the gradient method has also been implemented. The performance of the controllers generated with the toolkit is compared with those solved with the ACADO toolkit and HQP. The performance of the optimization is compared with two different test cases with different numbers of controls and states. The first one is based on a model of a pendulum hanging freely on a movable platform. The second one is a more complex model of a chain of three masses connected by springs. Seven different prediction horizons between 10 and 100 steps are used. When the time to achieve a near optimum solution is measured, VIATOC is in most cases the fastest one when the length of the prediction horizon is shorter than 70 steps.
•A new toolkit for nonlinear model predictive control has been developed.•The toolkits generate the required C code for a given control problem.•Optimization algorithm is based on the gradient projection method.•The presented methods are compared with other toolkits using two different test cases.•Results show a good performance with short and medium prediction horizons.
An attitude estimation algorithm is developed using an adaptive extended Kalman filter for low-cost microelectromechanical-system (MEMS) triaxial accelerometers and gyroscopes, that is, inertial ...measurement units (IMUs). Although these MEMS sensors are relatively cheap, they give more inaccurate measurements than conventional high-quality gyroscopes and accelerometers. To be able to use these low-cost MEMS sensors with precision in all situations, a novel attitude estimation algorithm is proposed for fusing triaxial gyroscope and accelerometer measurements. An extended Kalman filter is implemented to estimate attitude in direction cosine matrix (DCM) formation and to calibrate gyroscope biases online. We use a variable measurement covariance for acceleration measurements to ensure robustness against temporary nongravitational accelerations, which usually induce errors when estimating attitude with ordinary algorithms. The proposed algorithm enables accurate gyroscope online calibration by using only a triaxial gyroscope and accelerometer. It outperforms comparable state-of-the-art algorithms in those cases when there are either biases in the gyroscope measurements or large temporary nongravitational accelerations present. A low-cost, temperature-based calibration method is also discussed for initially calibrating gyroscope and acceleration sensors. An open source implementation of the algorithm is also available.
This article proposes a Rao-Blackwellized particle filter (RBPF) for fully mixing state-space models that replace the Kalman filter within the RBPF method with a noise-adaptive Kalman filter. This ...extension aims to deal with unknown time-varying measurement variances. Consequently, a variational Bayesian (VB) adaptive Kalman filter estimates the conditionally linear states and the measurement noise variances whereas the nonlinear (or latent) states are handled by sequential Monte Carlo (MC) sampling. Thus, by modifying the underlying mathematical framework of RBPF, we construct the Monte Carlo Variational Bayesian (MCVB) filter. A stopping criterion for VB approximations is proposed by employing Tikhonov regularization. Additionally, an analysis of the numerical stability of the proposed filtering mechanism is presented. The performance of the MCVB filter is illustrated in simulations and mobile robot tracking experiments in the presence of measurement model uncertainties.
A priori knowledge about the height profile of the path is vital for rollover avoidance in the context of autonomous driving through uneven forest ground. The forest ground is usually covered with ...either soft vegetation in summertime, or by snow in winter. Thus, the exact solid form of the forest ground cannot be detected by camera or LiDAR. This article, we propose height-odometry and aided height-odometry methods for ground height estimation. The height-odometry method depends solely on interoceptive and proprioceptive sensor data, while the aided height-odometry combines height-odometry output with the existing 3D map information. Thus, the central idea is to build a reference 3D path for autonomous forest machines where the spatial positioning – based on the RTK-GNSS or Forest SLAM method – is fused with the output of (aided) height-odometry method(s). We evaluate the proposed height-odometry methods in two separate environments that are accurately (3D) mapped by a UAV using the advanced machine-vision-based SfM method and the LiDAR-based SLAM algorithms. Through comprehensive data analysis, we demonstrate that the proposed 3D path estimation methods are practical and simple to implement, yet sufficient to estimate the height profile of the path with desired accuracy.
•A priori knowledge of the ground’s elevation profile is crucial for autonomous harvesting operations.•The height-odometry and aided height-odometry methods are introduced to compute the ground’s elevation profile.•Our methods rely on the vehicle’s geometry, IMU data, wheel height measurement, and a sparse a priori 3D map.•The reference elevation models are created using sophisticated methods involving drone mapping.•The performance of the proposed height estimation methods is evaluated against reference methods in both open and covered areas.
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•A hydraulic forestry crane was controlled using nonlinear model predictive control.•Path tracking and anti-sway control were done simultaneously.•Tests with three different target paths and ...different velocities were conducted.•The average tracking error in the tests was between 2cm and 11cm.•Anti-sway control was found to reduce the oscillations substantially.
Forest machines are used in many tasks and come in various designs. They can be used for cutting down trees, collecting logs and in different forest cleaning operations. Currently, in the commercial machines the level of automation is still relatively low, and they require a professional operator for good work efficiency. Nonlinear model predictive control (NMPC) is an optimal control strategy based on a dynamic model of the system. NMPC algorithms require quite a lot of computational power, but are becoming a more viable option as the performance of computers has increased. We demonstrate how an NMPC can be used for controlling a hydraulic forestry crane that has a freely hanging tool or processing head attached. The goal of the control is to follow a predefined path while simultaneously damping the undesired oscillations of the tool. Three different reference paths with velocities of 0.5m/s to 1.0m/s are tested. The average tracking error in these tests is between 0.02m and 0.11m. Anti-sway control can reduce the amplitude of sideways oscillations between 2% and 64% and longitudinal oscillations between 59% and 76%. The impact of anti-sway control on the tracking accuracy or the velocity is negligible.
This study highlights a combined center of gravity (CG) approach to model the comprehensive dynamics of the ground vehicles with articulated steering using solely six degrees of freedom (6-DOF). It ...is the case with an articulated vehicle that its CG shifts laterally towards the center of rotation during a turn. Thus, the idea is to compute the combined CG position of the multi-body articulated vehicle, which leads to the correction of the moment arms and body inertias about the updated CG position in the dynamic equations. Hence, the body forces and moments are computed with respect to the corrected CG position. In addition, the paper illustrates mathematical modeling of the center-articulated steering mechanism for the ground vehicle while restricting its operation to the primary handling regime. Overall, the draft presents the design of a nonlinear 6-DOF simulation for a load-haul-dump (LHD) type of articulated vehicle with a traveling CG. The simulation data is presented from one simulation run, where the critical results are analyzed. The obtained results signify the simplicity in using a combined CG to represent the vehicle dynamics.
•Coordinated control of a hydraulic forestry crane and a vehicle platform is proposed.•The vehicle follows a given path and the crane a time based trajectory.•Path and trajectory tracking is based on ...nonlinear model predictive control.•Average tracking error of the vehicle was 6.7–36.2cm and boom tip 3.7–13.6cm.
Forests are a challenging environment for autonomous operations. The automatic driving and control of hydraulic forestry cranes have been studied earlier as separate problems. However, this study focuses on control where the boom and the forest machine are employed in a coordinated manner. Such control could be beneficial in certain tasks, such as forest cleaning operations where small trees are removed. To accomplish the coordinated actions, nonlinear model predictive control (NMPC) is utilized. NMPC is a control strategy based on numerical optimization which minimizes a given objective function. The coordinated control was tested with real hardware consisting of a tractor and a forestry crane. In the tests, the tractor has a target path and the tip of the boom has a target trajectory. These tests correspond to a real world situation where the forest machine has its own driving lines and the tool is used to accomplish a given task. Two different trajectories for the boom tip were tested with the target velocity of the boom tip being 0.5m/s or 1.0m/s. At these velocities, the average tracking error of the tractor ranged from 6.7cm to 36.2cm while the average error of the boom tip varied between 3.7cm and 13.6cm. In a separate test where only the tractor was controlled, its tracking error was 15.4cm.
Crane posture estimation is the stepping stone to forest machine automation. Here, we introduce a robust minimal perception solution, that is, one that uses minimal constraints for maximal benefits. ...Specifically, we introduce a robust particle‐filter‐based method to estimate and track the posture of a flexible hydraulic crane by using only low‐cost equipment, namely, a two‐dimensional (2D) laser scanner, two short magnetically attached metal tubes as targets, and an angle sensor. An important feature of our method is that it incorporates control signals for hydraulic actuators. In contrast to the previous works employing laser scanners, we do not use the full shape of the crane to estimate the crane posture, but, instead, we use only two small targets in the field of view of the laser scanner. Thus, a large share of the range data is useful for other purposes, for example, to map the surrounding environment. We test the proposed method in a challenging forest environment and show that the particle filter is able to estimate the posture of the hydraulic crane efficiently and reliably in the presence of occlusions and obstructions. During our comprehensive testing, the tip position was measured with average errors smaller than 4.3 cm whereas the absolute maximum error was 15 cm.
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