Juho Vihonen

Geometry-aided angular acceleration sensing of rigid multi-body manipulator using MEMS rate gyros and linear accelerometers

We consider full motion state sensing of a rigid open-chain multi-body linkage assembly using rate gyros and linear accelerometers. The research is built upon micro-electromechanical systems (MEMS) components for low-cost “strap-down” implementation. Our emphasis is on direct lag-free joint angular acceleration sensing, for which a novel multi-MEMS configuration is motivated by motion control requirements. By using the multi-MEMS configuration, the bandwidth of the angular acceleration sensed is mostly proportional to the physical distances of linear accelerometers. The related joint position sensing, which is robust against linear and angular motion, is founded on the complementary and Kalman filtering principles for exclusive low delay. Experiments on a robotic vertically mounted three-link planar arm demonstrate the advantage of our key theoretical finding.

KNN classification of metallic targets using the magnetic polarizability tensor

Walk-through metal detectors are used at check points for preventing personnel and passengers from carrying threatening metallic objects, such as knives and guns, into a secure area. These systems are capable of detecting small metallic items, such as handcuff keys and blades, but are unable to distinguish accurately between threatening objects and innocuous items. This paper studies the extent to which a K-nearest-neighbour classifier can distinguish various kinds of metallic objects, such as knives, shoe shanks, belts and containers. The classifier uses features extracted from the magnetic polarizability tensor, which represents the electromagnetic properties of the object. The tests include distinguishing threatening objects from innocuous ones, classifying a set of objects into 13 classes, and distinguishing between several similar objects within an object class. A walk-through metal detection system is used as source for the test data, which consist of 835 scans and 67 objects. The results presented show a typical success rate of over 95% for recognizing threats, and over 85% for correct classification. In addition, we have shown that the system is capable of distinguishing between similar objects reliably. Overall, the method shows promise for the field of security screening and suggests the need for further research.

MEMS-based state feedback control of multi-body hydraulic manipulator

This paper presents closed-loop state feedback motion control of a heavy-duty hydraulic manipulator using solely micro-electro-mechanical systems (MEMS) rate gyroscopes and linear accelerometers for joint angular position, velocity and acceleration feedback. For benchmarking, incremental encoders with 2 million counts per revolution are also used to supply the joint motion state feedback. The two motion state estimation methods are compared using Cartesian path trajectory closed-loop control experiments with both position feedback-based proportional control and motion state-based feedback control. The experiments show that the proposed MEMS-based state feedback control yields comparable tracking results compared with the high accuracy encoder. Furthermore, the MEMS-based angular acceleration estimation in particular is free from typical differentiation induced noise amplification and post-filtering phase-lag.

Geometry-aided MEMS motion state estimation for multi-body manipulators

We consider full motion state sensing of a rigid multi-body linkage assembly using rate gyros and linear accelerometers. The research is built upon micro-electromechanical systems (MEMS) components for low-cost “strap-down” implementation. An open-chain geometrical assembly motion model is proposed and validated experimentally using a minimum MEMS-configuration per link. The related inclination sensing, which is robust against linear and angular motion effects, proceeds in a novel cascaded manner and is founded on the complementary and Kalman filtering principles for exclusive low delay. This is demonstrated by a suite of experiments on a robotic vertically mounted three-link planar arm rig.

Linear Accelerometers and Rate Gyros for Rotary Joint Angle Estimation of Heavy-Duty Mobile Manipulators Using Forward Kinematic Modeling

A gravity-referenced joint angle estimation approach is proposed for multiple-degree-of-freedom hydraulic manipulators. The approach is built solely upon easy-to-install linear accelerometers and angular rate gyroscopes to avoid physical contact to rotary joint mechanisms and the use of in-axis sensors. As a significant novelty, a comprehensive kinematics model for linear accelerations acting on the accelerometers during motion is associated with the well-known principles of complementary sensor fusion for the first time, which provides a practical solution for using the force of gravity as an angular reference while in fast motion. In experiments with a serial-link manipulator of a multiton off-road forestry vehicle, gyro-aided sensor fusion employing the kinematics model achieved a joint angle sensing error of less than ±1°, which translated to a centimeter end-effector positioning accuracy. This can be considered a significant result in view of the vibrations oscillating through the manipulator structure, coupled linear accelerations of linkage motion, and nonstatic interaction between the vehicle base and the terrain.

New aspects to knowledge-aided clutter analysis

Digital signal processing allows improvements in site-specific clutter prediction. With digital terrain maps and a flight obstacle register, land clutter origin can be solved. An efficient, knowledge-aided approach to extracting homogeneous clutter from radar signal is presented. Once homogeneous clutters statistic has been recognized, also mixture models can be constructed. The suggested aspects are illustrated through an air surveillance radar simulation. The enhancement attained in clutter analysis and thus in clutter models is the novelty of the presented aspects.

Determination of material and geometric properties of metallic objects using the magnetic polarisability tensor

A walk-through metal detector system has been used for measuring the magnetic polarisability tensor for a variety of metallic objects. We propose a method for classifying objects by their metallic composition using features of the tensor. Furthermore, we investigate the potential of using the tensor representation as an indication geometric properties of the object. The method used is shown to be accurate for classification of material composition. Furthermore, the results suggest that it is possible to use the tensor to distinguish between similar objects of different sizes in limited scenarios. These findings demonstrate the potential for this method, but also suggest the need for further studies.

Hydraulic manipulator virtual decomposition control with performance analysis using low-cost MEMS sensors

This paper presents closed-loop motion control of a heavy-duty hydraulic manipulator using non-linear model-based Virtual Decomposition Control (VDC), where the motion feedback is estimated solely with low-cost micro-electromechanical systems (MEMS) inertial sensors. By virtually decomposing the strongly non-linear and dynamically cross-coupled manipulator system into individually controlled subsystems, a significant improvement in overall control performance is achieved. The controller performance is analysed using planar Cartesian end-effector motion. The experiments show that the stability-guaranteed VDC approach based on low-cost MEMS sensor feedback yields a high-performance control solution: with a 0.85 m/s maximum velocity, the end-effector has a peak tracking error of 13 mm, which is a notable improvement by a factor of 3.6 compared to our previous work based on linear state feedback control.

MEMS Sensor Network Based Anti-Sway Control System for Articulated Hydraulic Crane

Hydraulic articulated multi-joint crane systems are widely used for the transportation of heavy loads. High productivity requires a short cargo transportation time which can lead to undesirable oscillations during crane load acceleration and deceleration. Typically it is the task of a crane operator to suppress the load swing, but with ever-increasing demand for faster operation the need for supporting control systems is evident. For overhead gantry cranes such assisting control systems can be considered as state of the art. However, for more complex articulated multi-link cranes only a few applicable control concepts have been proposed. Load swing angle and angular velocity measurement, or corresponding state observer based estimation, has been seen as a main problem in the realization of such assisting control systems. To tackle the problem, we present a novel suspended load anti-sway control system for heavy-duty articulated hydraulic cranes using solely low-cost linear MEMS accelerometers and angular rate gyroscopes embedded into easy-to-install sensor units. The proposed closed-loop anti-sway controller uses a network of embedded MEMS sensors for the crane motion state, suspended load inclination angle and angular velocity estimation. The control concept uses a semi-active approach where the desired load velocity is set by the crane operator via e.g. joystick input and the underlying load oscillation damping control system creates the desired crane tip velocity. Comparative results of anti-sway control are obtained using high resolution incremental encoder feedback for the articulated crane and suspended load motion states. Our experimental results verify effectiveness of the proposed anti-sway control system for articulated hydraulic cranes as well as applicability of the proposed MEMS sensor network for real-time closed-loop control of multi-body manipulators.Copyright

Geometry-aided low-noise angular velocity sensing of rigid-body manipulator using MEMS rate gyros and linear accelerometers

We consider low-noise angular velocity estimation for serial link manipulators using inertial readings from rate gyros and linear accelerometers. The research is founded on microelectromechanical systems (MEMS) components, which offer an attractive alternative to many traditional angular sensors due to their low cost, low power requirements, small size, and straightforward “strap-down” installation. By using a multi-MEMS configuration, an algebraic estimate of angular acceleration, where low- and high frequency perturbations are mostly proportional to the physical distances of linear accelerometers, is fused with rate gyro readings with the well-known principles of complementary and Kalman filtering. Experiments on a robotic three-link planar arm rig and a hydraulic heavy-duty manipulator demonstrate the feasibility of our practically lag-free novel approach.