Barrier-Lyapunov-Function-Based Backstepping Adaptive Hybrid Force/Position Control for Manipulator with Force and Position Constraints

In this paper, we present a backstepping adaptive hybrid force/position control based on Barrier Lyapunov Function for a robotic manipulator to prevent constraint violation of applied force and position simultaneously. First, the task space is partitioned according to the constrained and unconstrained directions, and a new representation of dynamics is introduced. Next, force/position control is applied using the strict-feedback backstepping technique, in which a time-varying Barrier Lyapunov Function is employed to ensure that the force and position do not violate their constraints. Finally, to deal with uncertainty, disturbance and non-linearity of the system, an adaptive radial basis function neural network (RBFNN) is also implemented in the control algorithm. Stability proof of the proposed control method is presented, and simulation studies on a 2-link manipulator show the effectiveness as well as the performance of the proposed controller in preventing constraint violation

Bayesian integration of audio and visual information for multi-target tracking using a CB-member filter

A new method is presented for integration of audio and visual information in multiple target tracking applications. The proposed approach uses a Bayesian filtering formulation and exploits multi-Bernoulli random finite set approximations. The work presented in this paper is the first principled Bayesian estimation approach to solve the sensor fusion problems that involve intermittent sensory data (e.g. audio data for a person who occasionally speaks.) We have examined our method with case studies from the SPEVI database. The results show nearly perfect tracking of people not only when they are silent but also when they are not visible to the camera (but speaking).Reza Hoseinnezhad, Ba-Ngu Vo, Ba-Tuong Vo, David Suterhttp://www.icassp2011.com/en/welcom

Robust pooling through the data mode: Robust Point cloud Classification and Segmentation Through Mode Pooling

The task of learning from point cloud data is always challenging due to the often occurrence of noise and outliers in the data. Such data inaccuracies can significantly influence the performance of state-of-the-art deep learning networks and their ability to classify or segment objects. While there are some robust deep-learning approaches, they are computationally too expensive for real-time applications. This paper proposes a deep learning solution that includes novel robust pooling layers which greatly enhance network robustness and perform significantly faster than state-of-the-art approaches. The proposed pooling layers replace conventional pooling layers in networks with global pooling operations such as PointNet and DGCNN. The proposed pooling layers look for data mode/cluster using two methods, RANSAC, and histogram, as clusters are indicative of models. We tested the proposed pooling layers on several tasks such as classification, part segmentation, and points normal vector estimation. The results show excellent robustness to high levels of data corruption with less computational requirements as compared to robust state-of-the-art methods. our code can be found at https://github.com/AymanMukh/ModePooling

Robust Object Classification Approach using Spherical Harmonics

Point clouds produced by either 3D scanners or multi-view images are often imperfect and contain noise or outliers. This paper presents an end-to-end robust spherical harmonics approach to classifying 3D objects. The proposed framework first uses the voxel grid of concentric spheres to learn features over the unit ball. We then limit the spherical harmonics order level to suppress the effect of noise and outliers. In addition, the entire classification operation is performed in the Fourier domain. As a result, our proposed model learned features that are less sensitive to data perturbations and corruptions. We tested our proposed model against several types of data perturbations and corruptions, such as noise and outliers. Our results show that the proposed model has fewer parameters, competes with state-of-art networks in terms of robustness to data inaccuracies, and is faster than other robust methods. Our implementation code is also publicly available1

BogieBot: A Climbing Robot in Cluttered Confined Space of Bogies with Ferrous Metal Surfaces

Proactive inspection is essential for prediction and prevention of rolling stock component failures. The conventional process for inspecting bogies under trains presents significant challenges for inspectors who need to visually check the tight and cluttered environment. We propose a miniature multi-link climbing robot, called BogieBot, that can be deployed inside the undercarriage areas of trains and other large vehicles for inspection and maintenance purposes. BogieBot can carry a visual sensor or manipulator on its main body. The novel compact design utilises six identical couple joints and two mechanically switchable magnetic grippers that together, empower multi-modal climbing and manipulation. The proposed mechanism is kinematically redundant, allowing the robot to perform self-motions in a tight space and manoeuvre around obstacles. The mechanism design and various analyses on the forward and inverse kinematic, work-space, and self-motions of BogieBot are presented. The robot is demonstrated to perform challenging navigation tasks in different scenarios involving simulated complex environments

Metoda za raspodjelu pogonske sile električnih vozila sa četiri kotača na kratkotrajno ili polovično skliskim cestama

In this paper, a four-wheel driving force distribution method based on driving force control is proposed. Driving force control is an anti-slip control method, previously proposed by the authors’ research group, which generates appropriate driving force based on the acceleration pedal. However, this control method cannot completely prevent reduction of driving force when a vehicle runs on an extremely slippery road. If the length of a slippery surface is shorter than the vehicle’s wheel base, the total driving force is retained by distributing the shortage of driving force to the wheels that still have traction. On the other hand, when either the left or right side runs on a slippery surface, yaw-moment is suppressed by setting total driving forces of left and right wheels to be the same. Therefore, four-wheel driving force distribution method is proposed for retaining driving force on instantaneous slippery roads, and suppressing yaw-moment on split ones. The effectiveness of the proposed distribution method is verified by simulations and experiments.U ovom radu predložena je metoda za raspodjelu pogonske sile električnih vozila sa četiri kotača temeljena na upravljanju pogonskom silom. Upravljanje pogonskom silom je metoda upravljanja koju je ranije predložila autorova istraživačka grupa, a koristi se za sprječavanje proklizavanja. Ova metoda generira prikladnu pogonsku silu temeljem pritiska na papučicu ubrzanja. Ipak, ova metoda upravljanja ne može u potpunosti spriječiti smanjenje pogonske sile kada vozilo nai.e na ekstremno sklisku cestu. Ako je dužina skliske površine kraća od me.uosovinskog razmaka vozila, ukupna pogonska sila se zadržava redistribucijom manjka pogonske sile na kotače koji i dalje imaju trakciju. S druge strane, kada lijeva ili desna strana vozila nai.e na sklisku površinu, moment zakretanja se potiskuje postavljanjem ukupne pogonske sile lijevih i desnih kotača na jednaki iznos. Dakle, metoda za raspodjelu pogonske sile predložena je za zadržavanje pogonske sile na kratkotrajno skliskim cestama, te za sprječavanje momenta zakretanja na polovično skliskim cestama. Učinkovitost predložene metode verificirana je simulacijski i eksperimentalno

Position sensing in brake-by-wire callipers using resolvers

Recent designs for brake-by-wire systems use "resolvers" to provide accurate and continuous measurements for the absolute position and speed of the rotor of the electric actuators in brake callipers (permanent magnet DC motors). Resolvers are absolute-angle transducers that are integrated with estimator modules called "angle tracking observer" and together they provide position and speed measurements. Current designs for angle-tracking observers are unstable in applications with high acceleration and/or speed. In this paper, we introduce a new angle-tracking observer in which a closed-loop linear time-invariant (LTI) observer is integrated with a quadrature encoder. Finite-gain stability of the proposed design and its robustness to three different kinds of parameter variations are proven based on theorems of input-output stability in nonlinear control theory. In our experiments, we examined the performance of our observer and two other methods (a well-known LTI observer and an extended Kalman filter) to estimate the position and speed of a brake-by-wire actuator. The results show that because of the very high speed and acceleration of the actuator in this application, the LTI observer and Kalman filter cannot track the rotor position and diverge. In contrast, with a properly designed open-loop transfer function and selecting a suitable switching threshold, our proposed angle-tracking observer is stable and highly accurate in a brake-by-wire applicatio

Efficient antilock braking by direct maximization of tire-road frictions

Antilock braking systems (ABSs) are usually designed based on controlling the wheel slip ratio so as to maintain each wheel in a presumed stable region. Since the optimal slip ratio (which results in maximum tire-road friction) varies with the type of road, current methods are not efficient in the sense of achieving the shortest possible stopping distance. This paper introduces an efficient ABS, in which the brake commands directly maximize the longitudinal component of tire-road friction at each wheel of the vehicle independently. The tire-road friction is estimated using a torque balance equation at each wheel, and within those equations, real-time estimates of the effective radius of tire are used. A step-by-step algorithm for computing the brake commands that maximize the tire-road friction is also presented. Three challenging braking scenarios were tested using the comprehensive CarSim simulation environment. The results show that, in comparison to conventional ABS, our method significantly reduces the stopping distance and improves the vehicle stabilit

A novel hybrid angle tracking observer for resolver to digital conversion

Resolvers are absolute angle transducers that are usually used for position and speed measurement in permanent magnet motors. An observer that uses the sinusoidal signals of the resolver for this measurement is called an Angle Tracking Observer (ATO). Current designs for such observers are not stable in high acceleration and high-speed applications. This paper introduces a novel hybrid scheme for ATO design, in which a closed-loop LTI observer is combined with a quadrature encoder. Finite gain stability of the proposed design is proven based on the circle theorem in input-output stability theory. Simulation results show that the proposed ATO design is stable in two cases where an LTI observer and an extended Kalman filter are unstable due to high speed and acceleration,. In addition, the tracking accuracy of our hybrid scheme is substantially higher than a single quadrature encoder