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

Nowadays, one of the largest environmental challenges that European countries must face consists in dealing with the past half century of nuclear waste. In order to optimize maintenance costs, nuclear waste must be sorted, segregated and stored according to its radiation level. Towards this end, in [1] we have recently proposed a visual-based shared control architecture meant to facilitate a human operator in controlling two remote robotic arms (one equipped with a gripper and another with a camera) during remote manipulation tasks of nuclear waste via a master device. The operator could then receive force cues informative of the feasibility of her/his motion commands during the task execution. The strategy presented in [1], albeit effective, suffers however from a locality issue since the operator can only provide instantaneous velocity commands (in a suitable task space), and receive instantaneous force feedback cues. On the other hand, the ability to ‘steer’ a whole future trajectory in task space, and to receive a corresponding integral force feedback along the whole planned trajectory (because of any constraint of the considered system), could significantly enhance the operator's performance, especially when dealing with complex manipulation tasks. The aim of this work is to then extend [1] towards a planning-based shared control architecture able to take into account the mentioned requirements. A human/hardware-in-the-loop experiment with simulated slave robots and a real master device is reported for demonstrating the feasibility and effectiveness of the proposed approach.

/pdf/visual-based-shared-control-for-remote-telemanipulation-with-1xi9ta2l18.pdf

Visual-based shared control for remote telemanipulation with integral haptic feedback

Robot-assisted cutting is considered an important task in several fields, such as robotic surgery, nuclear decommissioning, waste management, and manufacturing. Despite the complex dexterity requirements of cutting tasks, very simple mechanically-linked master-slave manipulators still dominate many of the above fields (e.g., nuclear robotics). Moreover, even when more dexterous manipulators are available (e.g., in robot-assisted surgery), the employed systems show little or no autonomy, delegating all control to the experience of the human operator. To ameliorate this situation, we present two haptic shared-control approaches for robotic cutting. They are designed to assist the human operator by enforcing different nonholonomic-like constraints representative of the cutting kinematics. To validate our approach, we carried out a human-subject experiment in a real cutting scenario. We compared our shared-control techniques with each other and with a standard haptic teleoperation scheme. Results show the usefulness of assisted control schemes in complex applications such as cutting. However, they also show a discrepancy between objective and subjective metrics.

/pdf/haptic-shared-control-methods-for-robotic-cutting-under-4ntfqraia4.pdf

Haptic Shared-Control Methods for Robotic Cutting under Nonholonomic Constraints

The selection of a suitable set of visual features for an optimal performance of closed-loop visual control or Structure from Motion (SfM) schemes is still an open problem in the visual servoing community For instance, when considering integral region-based features such as image moments, only heuristic, partial, or local results are currently available for guiding the selection of an appropriate moment set The goal of this paper is to propose a novel learning strategy able to automatically optimize online the shape of a given class of image moments as a function of the observed scene for improving the SfM performance in estimating the scene structure As case study, the problem of recovering the (unknown) 3D parameters of a planar scene from measured moments and known camera motion is considered The reported simulation results fully confirm the soundness of the approach and its superior performance over more consolidated solutions in increasing the information gain during the estimation task

/pdf/learning-the-shape-of-image-moments-for-optimal-3d-structure-22y0qsv5sz.pdf

Learning the shape of image moments for optimal 3D structure estimation

One way to deal with occlusions or loss of tracking of the visual features used for visual servoing tasks is to predict the feature behavior in the image plane when the measurements are missing. Different prediction and correction methods have already been proposed in the literature. The purpose of this paper is to compare and experimentally validate some of these methods for eye-in-hand and eye-to-hand configurations. In particular, we show that a correction based both on the image and the camera/target pose provides the best results.

/pdf/visual-servoing-when-visual-information-is-missing-s2nergcszw.pdf

Visual servoing when visual information is missing: Experimental comparison of visual feature prediction schemes

This paper shows a preliminary experimental evaluation of a novel haptic aiding for Remotely Piloted Vehicles. The aerodynamically-inspired haptic feedback law was named Conventional Aircraft Artificial Feel, and was implemented as a variable stiffness spring. The experimental set-up comprises a fully nonlinear mathematical model of the aircraft, a visual display and a haptic device (a 3 DoF Omega Device). The tests, performed using a set of 18 naive subjects, show the validity of the proposed approach.

/pdf/preliminary-evaluation-of-a-haptic-aiding-concept-for-26q5lmn7cf.pdf

Paolo Robuffo Giordano

Papers

Visual-based shared control for remote telemanipulation with integral haptic feedback

Haptic Shared-Control Methods for Robotic Cutting under Nonholonomic Constraints

Learning the shape of image moments for optimal 3D structure estimation

Visual servoing when visual information is missing: Experimental comparison of visual feature prediction schemes

Preliminary evaluation of a haptic aiding concept for remotely piloted vehicles