Most of the signal processing that we will study in this course involves local operations on a signal, namely transforming the signal by applying linear combinations of values in the neighborhood of each sample point. You are familiar with such operations from Calculus, namely, taking derivatives and you are also familiar with this from optics namely blurring a signal. We will be looking at sampled signals only. Let's start with a few basic examples. Local difference Suppose we have a 1D image and we take the local difference of intensities, DI(x) = 1 2 (I(x + 1) − I(x − 1)) which give a discrete approximation to a partial derivative. (We compute this for each x in the image.) What is the effect of such a transformation? One key idea is that such a derivative would be useful for marking positions where the intensity changes. Such a change is called an edge. It is important to detect edges in images because they often mark locations at which object properties change. These can include changes in illumination along a surface due to a shadow boundary, or a material (pigment) change, or a change in depth as when one object ends and another begins. The computational problem of finding intensity edges in images is called edge detection. We could look for positions at which DI(x) has a large negative or positive value. Large positive values indicate an edge that goes from low to high intensity, and large negative values indicate an edge that goes from high to low intensity. Example Suppose the image consists of a single (slightly sloped) edge:

http://ieeexplore.ieee.org/iel5/5/31307/01456462.pdf

Linear systems

Feel lonely? What about reading books? Book is one of the greatest friends to accompany while in your lonely time. When you have no friends and activities somewhere and sometimes, reading book can be a great choice. This is not only for spending the time, it will increase the knowledge. Of course the b=benefits to take will relate to what kind of book that you are reading. And now, we will concern you to try reading modelling and control of robot manipulators as one of the reading material to finish quickly.

Modelling and Control of Robot Manipulators

Unmanned aerial vehicles (UAVs) can be used to serve as aerial base stations to enhance both the coverage and performance of communication networks in various scenarios, such as emergency communications and network access for remote areas. Mobile UAVs can establish communication links for ground users to deliver packets. However, UAVs have limited communication ranges and energy resources. Particularly, for a large region, they cannot cover the entire area all the time or keep flying for a long time. It is thus challenging to control a group of UAVs to achieve certain communication coverage in a long run, while preserving their connectivity and minimizing their energy consumption. Toward this end, we propose to leverage emerging deep reinforcement learning (DRL) for UAV control and present a novel and highly energy-efficient DRL-based method, which we call DRL-based energy-efficient control for coverage and connectivity (DRL-EC3). The proposed method 1) maximizes a novel energy efficiency function with joint consideration for communications coverage, fairness, energy consumption and connectivity; 2) learns the environment and its dynamics; and 3) makes decisions under the guidance of two powerful deep neural networks. We conduct extensive simulations for performance evaluation. Simulation results have shown that DRL-EC3 significantly and consistently outperform two commonly used baseline methods in terms of coverage, fairness, and energy consumption.

Energy-Efficient UAV Control for Effective and Fair Communication Coverage: A Deep Reinforcement Learning Approach

A compilation of UAV applications for precision agriculture

Path planning techniques for unmanned aerial vehicles: A review, solutions, and challenges

This letter presents a wearable haptic device for the forearm and its application in robotic teleoperation. The device is able to provide skin stretch, pressure, and vibrotactile stimuli. Two servo motors, housed in a 3D printed lightweight platform, actuate an elastic fabric belt, wrapped around the arm. When the two servo motors rotate in opposite directions, the belt is tightened (or loosened), thereby compressing (or decompressing) the arm. On the other hand, when the two motors rotate in the same direction, the belt applies a shear force to the arm skin. Moreover, the belt houses four vibrotactile motors, positioned evenly around the arm at 90° from each other. The device weights 220 g for 115 × 122 × 50 mm of dimensions, making it wearable and unobtrusive. We carried out a perceptual characterization of the device as well as two human-subjects teleoperation experiments in a virtual environment, employing a total of 34 subjects. In the first experiment, participants were asked to control the motion of a robotic manipulator for grasping an object; in the second experiment, participants were asked to teleoperate the motion of a quadrotor fleet along a given path. In both scenarios, the wearable haptic device provided feedback information about the status of the slave robot(s) and of the given task. Results showed the effectiveness of the proposed device. Performance on completion time, length trajectory, and perceived effectiveness when using the wearable device improved of 19.8%, 25.1%, and 149.1% than when wearing no device, respectively. Finally, all subjects but three preferred the conditions including wearable haptics.

/pdf/design-and-evaluation-of-a-wearable-haptic-device-for-skin-5b92hiniun.pdf

Design and Evaluation of a Wearable Haptic Device for Skin Stretch, Pressure, and Vibrotactile Stimuli

This paper considers the problem of controlling a formation of quadrotor UAVs equipped with onboard cameras able to measure relative bearings in their local body frames w.r.t. neighboring UAVs. The control goal is twofold: (i) steering the agent group towards a formation defined in terms of desired bearings, and (ii) actuating the group motions in the ‘null-space’ of the current bearing formation. The proposed control strategy relies on an extension of the rigidity theory to the case of directed bearing frameworks in ℝ3×S1. This extension allows to devise a decentralized bearing controller which, unlike most of the present literature, does not need presence of a common reference frame or of reciprocal bearing measurements for the agents. Simulation and experimental results are then presented for illustrating and validating the approach.

/pdf/a-rigidity-based-decentralized-bearing-formation-controller-1m67dswsh2.pdf

A rigidity-based decentralized bearing formation controller for groups of quadrotor UAVs

We consider the task-oriented modeling of the differential kinematics of nonholonomic mobile manipulators (NMMs). A suitable NMM Jacobian is defined that relates the available input commands to the time derivative of the task variables, and can be used to formulate and solve kinematic control problems. When the NMM is redundant with respect to the given task, we provide an extension of two well-known redundancy resolution methods for fixed-base manipulators (Projected Gradient and Task Priority) and introduce a novel technique (Task Sequencing) aimed at improving performance, e.g., avoiding singularities. The proposed methods are applied then to the specific case of image-based visual servoing, where the NMM image Jacobian combines the interaction matrix and the kinematic model of the mobile manipulator. Comparative numerical results are presented for two case studies.

Image-based visual servoing schemes for nonholonomic mobile manipulators

Robotic vision has become an important field of research for micro aerial vehicles in the recent years. While many approaches for autonomous visual control of such vehicles rely on powerful ground stations, the increasing availability of small and light hardware allows for the design of more independent systems. In this context, we present a robust algorithm able to recover the UAV ego-motion using a monocular camera and on-board hardware. Our method exploits the continuous homography constraint so as to discriminate among the observed feature points in order to classify those belonging to the dominant plane in the scene. Extensive experiments on a real quadrotor UAV demonstrate that the estimation of the scaled linear velocity in a cluttered environment improved by a factor of 25% compared to previous approaches.

https://www.irisa.fr/lagadic/pdf/2012_iros_grabe.pdf

Robust optical-flow based self-motion estimation for a quadrotor UAV

A new framework for semi-autonomous path plan- ning for mobile robots that extends the classical paradigm of bilateral shared control is presented. The path is represented as a B-spline and the human operator can modify its shape by controlling the motion of a finite number of control points. An autonomous algorithm corrects in real time the human directives in order to facilitate path tracking for the mobile robot and ensures i) collision avoidance, ii) path regularity, and iii) attraction to nearby points of interest. A haptic feedback algorithm processes both human's and autonomous control terms, and their integrals, to provide an information of the mismatch between the path specified by the operator and the one corrected by the autonomous algorithm. The framework is validated with extensive experiments using a quadrotor UAV and a human in the loop with two haptic interfaces.

/pdf/semi-autonomous-trajectory-generation-for-mobile-robots-with-5b8ejvo1w9.pdf

Paolo Robuffo Giordano

Papers

Design and Evaluation of a Wearable Haptic Device for Skin Stretch, Pressure, and Vibrotactile Stimuli

A rigidity-based decentralized bearing formation controller for groups of quadrotor UAVs

Image-based visual servoing schemes for nonholonomic mobile manipulators

Robust optical-flow based self-motion estimation for a quadrotor UAV

Semi-autonomous Trajectory Generation for Mobile Robots with Integral Haptic Shared Control