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

Robotic systems are increasingly used to work in dynamic and/or unstructured environments and to operate with a high degree of safety and autonomy. Consequently, they are often equipped with external sensors capable of perceiving the environment ( e.g. , cameras) and the contacts that may arise (e.g. force/torque sensors). This letter proposes a general framework for combining force and visual information in the visual feature space. By leveraging recent results on the derivation of visual servo dynamics, we generalize the treatment regardless of the visual features chosen. Vision and force sensing are coupled in the feature space, avoiding both the convergence to a local minimum and the arising of inconsistencies at the actuation level. Any task space direction is simultaneously controlled by both vision and force. Compliance against interaction forces is achieved in feature space along the features defining the visual task. Experiments on a real platform are carried out to evaluate the effectiveness of the proposed framework.

/pdf/a-general-visual-impedance-framework-for-effectively-4lvdxzhm2u.pdf

A General Visual-Impedance Framework for Effectively Combining Vision and Force Sensing in Feature Space

This paper presents a novel distributed control strategy that enables multi-target exploration while ensuring a time-varying connected topology in both 2D and 3D cluttered environments. Flexible continuous connectivity is guaranteed by gradient descent on a monotonic potential function applied on the algebraic connectivity (or Fiedler eigenvalue) of a generalized interaction graph. Limited range, line-of-sight visibility, and collision avoidance are taken into account simultaneously by weighting of the graph Laplacian. Completeness of the multitarget visiting algorithm is guaranteed by using a decentralized adaptive leader selection strategy and a suitable scaling of the exploration force based on the direction alignment between exploration and connectivity force and the traveling efficiency of the current leader. Extensive MonteCarlo simulations with a group of several quadrotor UAVs show the practicability, scalability and effectiveness of the proposed method.

Decentralized Multi-target Exploration and Connectivity Maintenance with a Multi-robot System

In this paper we propose a novel general method to let a dynamical system fulfil at best a control task when the nominal parameters are not perfectly known. The approach is based on the introduction of the novel concept of closed-loop sensitivity, a quantity that relates parameter variations to deviations of the closed-loop trajectory of the system/controller pair. This new definition takes into account the dependency of the control inputs from the system states and nominal parameters as well as from the controller dynamics. The reference trajectory to be tracked is taken as optimization variable, and the dynamics of both the sensitivity and of its gradient are computed analytically along the system trajectories. We then show how this computation can be effectively exploited for solving trajectory optimization problems aimed at generating a reference trajectory that minimizes a norm of the closed-loop sensitivity. The theoretical results are validated via an extensive campaign of Monte Carlo simulations for two relevant robotic systems: a unicycle and a quadrotor UAV.

/pdf/trajectory-generation-for-minimum-closed-loop-state-3czu3959hb.pdf

Trajectory Generation for Minimum Closed-Loop State Sensitivity

This paper presents a complete framework for image-based navigation from an image memory that exploits mutual information and does not need any feature extraction, matching or any 3D information. The navigation path is represented by a set of automatically selected key images obtained during a prior learning phase. The shared information (entropy) between the current acquired image and nearby key images is exploited to switch key images during navigation. Based on the key images and the current image, the control law proposed by [1] is used to compute the rotational velocity of a mobile robot during its qualitative visual navigation. Using our approach, real-time navigation has been performed inside a corridor and inside a room with a Pioneer 3-DX equipped with an on-board perspective camera without the need of accurate mapping and localization.

/pdf/appearance-based-indoor-navigation-by-ibvs-using-mutual-566661z2bh.pdf

Appearance-based indoor navigation by IBVS using mutual information

This article presents the design of a decentralized connectivity-maintenance algorithm for the teleoperation of a team of multiple UAVs, together with an extensive human subject evaluation in virtual and real environments. The proposed connectivity-maintenance algorithm enhances earlier works by improving their applicability, safety, effectiveness, and ease of use, by including: 1) an airflow-avoidance behavior that avoids stack downwash phenomena in rotor-based aerial robots; 2) a consensus-based action for enabling fast displacements with minimal topology changes by having all follower robots moving at the leader’s velocity; 3) an automatic decrease of the minimum degree of connectivity, enabling an intuitive and dynamic expansion/compression of the formation; and 4) an automatic detection and resolution of deadlock configurations, i.e., when the robot leader cannot move due to counterbalancing connectivity- and external-related inputs. We also devised and evaluated different interfaces for teleoperating the team as well as different ways of receiving information about the connectivity force acting on the leader. The results of two human subject experiments show that the proposed algorithm is effective in various situations. Moreover, using haptic feedback to provide information about the team connectivity outperforms providing both no feedback at all and sensory substitution via visual feedback. Note to Practitioners —The control of one drone is usually performed with a remote controller (similar to a joypad) that uses radio-wave signals. When controlling more than one drone, even a simple task, such as moving the whole team around, becomes very challenging. Developing an easy, yet efficient way to impart commands to a formation of drones is necessary to achieve any complex task. This article proposes a framework to control a fleet of drones (quadrotors) in an intuitive way while receiving meaningful and effective information on the state of the formation. The proposed technique does not rely on any absolute positioning system (e.g., GPS) or centralized command center. Instead, it only uses the relative position of the drones with respect to each other, and all computations are designed in a decentralized fashion. These features make the proposed framework ready for deployment in different high-impact applications, such as in surveillance, search-and-rescue, and disaster response scenarios.

/pdf/connectivity-maintenance-teleoperation-of-a-uav-fleet-with-3zkpap57bf.pdf

Paolo Robuffo Giordano

Papers

A General Visual-Impedance Framework for Effectively Combining Vision and Force Sensing in Feature Space

Decentralized Multi-target Exploration and Connectivity Maintenance with a Multi-robot System

Trajectory Generation for Minimum Closed-Loop State Sensitivity

Appearance-based indoor navigation by IBVS using mutual information

Connectivity-Maintenance Teleoperation of a UAV Fleet With Wearable Haptic Feedback