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

State estimation is a fundamental and challenging problem in many applications involving planning and control, in particular when dealing with systems exhibiting nonlinear dynamics. While the design of nonlinear observers is an active research field, the issue of optimizing over time the transient response of the estimation error has not received, to the best of our knowledge, a comparable attention. In this paper, an active strategy for tuning the transient response of a particular class of nonlinear observers is discussed. This is achieved by suitably acting on the estimation gains and on the inputs applied to the system under observation. The theory is validated by simulation results applied to two visual estimation tasks (Structure from Motion - SfM).

/pdf/a-framework-for-active-estimation-application-to-structure-4fy871858g.pdf

A framework for active estimation: Application to Structure from Motion

In this paper, we address the problem of using a camera with limited field of view for controlling the motion of a quadrotor in aggressive flight regimes. We present a minimum time trajectory planning method that guarantees visibility of the image features while allowing the robot to undertake aggressive motions for which the usual near-hovering assumption is violated. We exploit differential flatness and B-Splines to parametrize the system trajectories in terms of a finite number of control points, which can then be optimized by Sequential Quadratic Programming (SQP). The control strategy is similar to a Receding Horizon Control (RHC) approach for modifying online the reference trajectory in order to account for noise, disturbances and any non-modeled effect. The algorithm is validated in a physically realistic simulation environment.

https://hal.inria.fr/hal-01572362/document

Vision-based minimum-time trajectory generation for a quadrotor UAV

For the purpose of autonomous UAV flight control, cameras are ubiquitously exploited as a cheap and effective onboard sensor for obtaining non-metric position or velocity measurements. Since the metric scale cannot be directly recovered from visual input only, several methods have been proposed in the recent literature to overcome this limitation by exploiting independent `metric' information from additional onboard sensors. The flexibility of most approaches is, however, often limited by the need of constantly tracking over time a certain set of features in the environment, thus potentially suffering from possible occlusions or loss of tracking during flight. In this respect, in this paper we address the problem of estimating the scale of the observed linear velocity in the UAV body frame from direct measurement of the instantaneous (and non-metric) optical flow, and the integration of an onboard Inertial Measurement Unit (IMU) for providing (metric) acceleration readings. To this end, two different estimation techniques are developed and critically compared: a standard Extended Kalman Filter (EKF) and a novel nonlinear observer stemming from the adaptive control literature. Results based on simulated and real data recorded during a quadrotor UAV flight demonstrate the effectiveness of the approach.

/pdf/a-comparison-of-scale-estimation-schemes-for-a-quadrotor-uav-2fi0atmpty.pdf

A comparison of scale estimation schemes for a quadrotor UAV based on optical flow and IMU measurements

This paper considers the problem of controlling a formation of quadrotor UAVs equipped with onboard cameras with the goal of maintaining bearing rigidity during motion despite the presence of several sensing constraints, that is, minimum/maximum range, limited camera field of view, and possible occlusions caused by the agents of the formation. To this end, a decentralized gradient-based control action is developed, based on a suitable ‘degree of infinitesimal rigidity’ linked to the spectral properties of the bearing rigidity matrix. The approach is then experimentally validated with five quadrotor UAVs.

https://hal.inria.fr/hal-01482422/document

Bearing rigidity maintenance for formations of quadrotor UAVs

This work extends the framework of bilateral shared control of mobile robots with the aim of increasing the robot autonomy and decreasing the operator commitment. We consider persistent autonomous behaviors where a cyclic motion must be executed by the robot. The human operator is in charge of modifying online some geometric properties of the desired path. This is then autonomously processed by the robot in order to produce an actual path guaranteeing: i) tracking feasibility, ii) collision avoidance with obstacles, iii) closeness to the desired path set by the human operator, and iv) proximity to some points of interest. A force feedback is implemented to inform the human operator of the global deformation of the path rather than using the classical mismatch between desired and executed motion commands. Physically-based simulations, with human/hardware-in-the-loop and a quadrotor UAV as robotic platform, demonstrate the feasibility of the method.

/pdf/interactive-planning-of-persistent-trajectories-for-human-3p4j7fiq7h.pdf

Paolo Robuffo Giordano

Papers

A framework for active estimation: Application to Structure from Motion

Vision-based minimum-time trajectory generation for a quadrotor UAV

A comparison of scale estimation schemes for a quadrotor UAV based on optical flow and IMU measurements

Bearing rigidity maintenance for formations of quadrotor UAVs

Interactive planning of persistent trajectories for human-assisted navigation of mobile robots