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

Research on formation control and cooperative localiza-tion for multi-robot systems has been an active field over the last years. A powerful theoretical framework for addressing formation control and localization, especially when exploiting onboard sensing, is that of formation rigidity (mainly studied for the cases of distance and bearing measurements). Rigidity of a formation depends on the topology of the sens-ing/communication graph but also on the spatial arrangement of the robots, since special configurations ('singulari-ties' of the rigidity matrix), which are hard to detect in general , can cause a rigidity loss and prevent convergence of formation control/localization algorithms based on formation rigidity.The aim of this paper is to gain additional insights into the internal structure of bearing rigid formations by considering an alternative characterization of formation rigidity using tools borrowed from the mechanical engineering community. In particular, we show that bearing rigid graphs can be given a physical interpretation related to virtual mechanisms , whose mobility and singularities can be analyzed and detected in an analytical way by using tools from the mechanical engineering community (Screw theory, Grassmann geometry, Grassmann-Cayley algebra). These tools offer a powerful alternative to the evaluation of the mobility and sin-gularities typically obtained by numerically determining the spectral properties of the bearing rigidity matrix (which typically prevents drawing general conclusions). We apply the proposed machinery to several case formations with different degrees of actuation, and discuss known (and unknown) singularity cases for representative formations. The impact on the localization problem is also discussed.

/pdf/physical-interpretation-of-rigidity-for-bearing-formations-2cu8cdalq8.pdf

Physical Interpretation of Rigidity for Bearing Formations: Application to Mobility and Singularity Analyses

This paper considers a multi-robot team tasked with monitoring an environmental field of interest over long time horizons. The approach is based on a control-theoretic measure of the information collected by the robots, namely a norm of the constructability Gramian. This measure is leveraged in order to learn a distributed multi-robot control policy using the reinforcement learning paradigm. The learned policy is then combined with energy constraints using the constraint-driven control framework in order to achieve persistent environmental monitoring. The proposed approach is tested in a simulated multi-robot persistent environmental monitoring scenario where a team of robots with limited availability of energy is to be controlled in a coordinated fashion in order to estimate the concentration of a gas diffusing in the environment.

/pdf/multi-robot-persistent-environmental-monitoring-based-on-db1ypt2o.pdf

Multi-Robot Persistent Environmental Monitoring Based on Constraint-Driven Execution of Learned Robot Tasks

In this work we present our results on dynamic visual servoing for the case of moving targets while also exploring the possibility of using such a controller for interaction with the environment. We illustrate the derivation of a feature space impedance controller for tracking a moving object as well as an Extended Kalman Filter based on the visual servoing kinematics for increasing the rate of the visual information and estimating the target velocity for both the cases of PBVS and IBVS with image point features. Simulations are carried out to validate the estimator performance during a Peg-in-Hole insertion task with a moving part. Experiments are also conducted on a real redundant manipulator with a low-cost wrist-mounted camera. Details on several implementation issues encountered during implementation are also discussed.

/pdf/towards-dynamic-visual-servoing-for-interaction-control-and-ktdsrngi.pdf

Towards Dynamic Visual Servoing for Interaction Control and Moving Targets

We propose in this paper a new active perception scheme based on Model Predictive Control under constraints for generating a sequence of visual servoing tasks. The proposed control scheme is used to compute the motion of a camera whose task is to successively observe a set of robots for measuring their position and improving the accuracy of their localization. This method is based on the prediction of an uncertainty model (due to actuation and measurement noise) for determining which robot has to be observed by the camera. Simulation results are presented for validating the approach.

/pdf/visual-servoing-using-model-predictive-control-to-assist-9jevz901ek.pdf

Visual servoing using model predictive control to assist multiple trajectory tracking

This letter presents a control-theoretic approach for trajectory optimization of mobile robots suitable for environmental monitoring. The method is based on the optimization of the Constructability Gramian in order to maximize the information collected while traversing a state trajectory. The maximization of the collected information is combined with energy constraints to define an optimization-based controller that achieves persistent environmental monitoring. The results of its application to the estimation of the concentration of a diffusing gas using a mobile robot are shown in simulation.

https://hal.inria.fr/hal-03335162/document

Paolo Robuffo Giordano

Papers

Physical Interpretation of Rigidity for Bearing Formations: Application to Mobility and Singularity Analyses

Multi-Robot Persistent Environmental Monitoring Based on Constraint-Driven Execution of Learned Robot Tasks

Towards Dynamic Visual Servoing for Interaction Control and Moving Targets

Visual servoing using model predictive control to assist multiple trajectory tracking

Online Robot Trajectory Optimization for Persistent Environmental Monitoring