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

The free and open source Tele-Operation Platform of the MPI for Biological Cybernetics (TeleKyb) is an end-to-end software framework for the development of bilateral teleoperation systems between human interfaces (e.g., haptic force feedback devices or gamepads) and groups of quadrotor Unmanned Aerial Vehicles (UAVs). Among drivers for devices and robots from various hardware manufactures, TeleKyb provides a high-level closed-loop robotic controller for mobile robots that can be extended dynamically with modules for state estimation, trajectory planning, processing, and tracking. Since all internal communication is based on the Robot Operating System (ROS), TeleKyb can be easily extended to meet future needs. The capabilities of the overall framework are demonstrated in both an experimental validation of the controller for an individual quadrotor and a complex experimental setup involving bilateral human-robot interaction and shared formation control of multiple UAVs.

/pdf/the-telekyb-framework-for-a-modular-and-extendible-ros-based-2sch9a93w4.pdf

The TeleKyb framework for a modular and extendible ROS-based quadrotor control

In this paper, we investigate the effect of haptic cueing on a human operator's performance in the field of bilateral teleoperation of multiple mobile robots, particularly multiple unmanned aerial vehicles (UAVs). Two aspects of human performance are deemed important in this area, namely, the maneuverability of mobile robots and the perceptual sensitivity of the remote environment. We introduce metrics that allow us to address these aspects in two psychophysical studies, which are reported here. Three fundamental haptic cue types were evaluated. The Force cue conveys information on the proximity of the commanded trajectory to obstacles in the remote environment. The Velocity cue represents the mismatch between the commanded and actual velocities of the UAVs and can implicitly provide a rich amount of information regarding the actual behavior of the UAVs. Finally, the Velocity+Force cue is a linear combination of the two. Our experimental results show that, while maneuverability is best supported by the Force cue feedback, perceptual sensitivity is best served by the Velocity cue feedback. In addition, we show that large gains in the haptic feedbacks do not always guarantee an enhancement in the teleoperator's performance.

/pdf/human-centered-design-and-evaluation-of-haptic-cueing-for-19r2a56ewm.pdf

Human-Centered Design and Evaluation of Haptic Cueing for Teleoperation of Multiple Mobile Robots

In this letter, we investigate the online generation of optimal trajectories for target tracking with a quadrotor while satisfying a set of image-based and actuation constraints. We consider a quadrotor equipped with a camera (either down or front-looking) with limited field of view. The aim is to follow in a smooth but reactive way a moving target while avoiding obstacles in the environment and occlusions in the image space. We propose vision-based approaches based on multiobjective optimization, especially with the occlusion constraint formulation. We design an online replanning strategy inspired from model predictive control that successively solves a nonlinear optimization problem. The problem is formulated as a nonlinear program (NLP) using differential flatness and finite parametrization with B-Splines. This allows a resolution by sequential quadratic programming (SQP) at a rate of 30 Hz. The robustness and reactivity of the replanning algorithm are demonstrated through realistic simulation results. Experiments validating the performance with a real quadrotor are also presented.

/pdf/vision-based-reactive-planning-for-aggressive-target-1o5p09ku3s.pdf

Vision-Based Reactive Planning for Aggressive Target Tracking While Avoiding Collisions and Occlusions

For us humans, walking is our most natural way of moving through the world. One of the major challenges in present research on navigation in virtual reality is to enable users to physically walk through virtual environments. Although treadmills, in principle, allow users to walk for extended periods of time through large virtual environments, existing setups largely fail to produce a truly immersive sense of navigation. Partially, this is because of inadequate control of treadmill speed as a function of walking behavior. Here, we present a new control algorithm that allows users to walk naturally on a treadmill, including starting to walk from standstill, stopping, and varying walking speed. The treadmill speed control consists of a feedback loop based on the measured user position relative to a given reference position, plus a feed-forward term based on online estimation of the user's walking velocity. The purpose of this design is to make the treadmill compensate fully for any persistent walker motion, while keeping the accelerations exerted on the user as low as possible.We evaluated the performance of the algorithm by conducting a behavioral experiment in which we varied its most important parameters. Participants walked at normal walking speed and then, on an auditory cue, abruptly stopped. After being brought back to the center of the treadmill by the control algorithm, they rated how smoothly the treadmill had changed its velocity in response to the change in walking speed. Ratings, in general, were quite high, indicating good control performance. Moreover, ratings clearly depended on the control algorithm parameters that were varied. Ratings were especially affected by the way the treadmill reversed its direction of motion. In conclusion, controlling treadmill speed in such a way that changes in treadmill speed are unobtrusive and do not disturb VR immersiveness is feasible on a normal treadmill with a straightforward control algorithm.

/pdf/making-virtual-walking-real-perceptual-evaluation-of-a-new-2q2hg24jft.pdf

Making virtual walking real: Perceptual evaluation of a new treadmill control algorithm

In the image-based visual servoing framework, error signals are directly computed from image feature parameters, thus obtaining control schemes which do not need neither a 3-D model of the scene, nor a perfect knowledge of the camera calibration matrix. However, the current value of the depth Z for each considered feature must be known. We propose a method to estimate on-line the value of Z for point features while the camera is moving through the scene, by using tools from nonlinear observer theory. By interpreting Z as a continuous unknown state with known dynamics, we build an estimator which asymptotically recovers the actual depth value for the selected feature.

/pdf/on-line-estimation-of-feature-depth-for-image-based-visual-2j8mvupc2k.pdf

Paolo Robuffo Giordano

Papers

The TeleKyb framework for a modular and extendible ROS-based quadrotor control

Human-Centered Design and Evaluation of Haptic Cueing for Teleoperation of Multiple Mobile Robots

Vision-Based Reactive Planning for Aggressive Target Tracking While Avoiding Collisions and Occlusions

Making virtual walking real: Perceptual evaluation of a new treadmill control algorithm

On-Line Estimation of Feature Depth for Image-Based Visual Servoing Schemes