Decentralized Triangular Guidance Algorithms for Formations of UAVs

This paper deals with the design of a guidance control system for a swarm of unmanned aerial systems flying at a given altitude, addressing flight formation requirements that can be formulated constraining the swarm to be on the nodes of a triangular mesh. Three decentralized guidance algorithms are presented. A classical fixed leader–follower scheme is compared with two alternative schemes: the former is based on the self-identification of one or more time-varying leaders; the latter is an algorithm without leaders. Several operational scenarios have been simulated involving swarms with obstacles and an increasing number of aircraft in order to prove the effectiveness of the proposed guidance schem

Intelligent gripper design and application for automated part recognition and gripping

Intelligent gripping may be achieved through gripper design, automated part recognition, intelligent algorithm for control of the gripper, and on-line decision-making based on sensory data. A generic framework which integrates sensory data, part recognition, decision-making and gripper control to achieve intelligent gripping based on ABB industrial robot is constructed. The three-fingered gripper actuated by a linear servo actuator designed and developed in this project for precise speed and position control is capable of handling a large variety of objects. Generic algorithms for intelligent part recognition are developed. Edge vector representation is discussed. Object geometric features are extracted. Fuzzy logic is successfully utilized to enhance the intelligence of the system. The generic fuzzy logic algorithm, which may also find application in other fields, is presented. Model-based gripping planning algorithm which is capable of extracting object grasp features from its geometric features and reasoning out grasp model for objects with different geometry is proposed. Manipulator trajectory planning solves the problem of generating robot programs automatically. Object-oriented programming technique based on Visual C++ MFC is used to constitute the system software so as to ensure the compatibility, expandability and modular programming design. Hierarchical architecture for intelligent gripping is discussed, which partitions the robot’s functionalities into high-level (modeling, recognizing, planning and perception) layers, and low-level (sensing, interfacing and execute) layers. Individual system modules are integrated seamlessly to constitute the intelligent gripping system

Advances in Robot Navigation

Robot navigation includes different interrelated activities such as perception - obtaining and interpreting sensory information; exploration - the strategy that guides the robot to select the next direction to go; mapping - the construction of a spatial representation by using the sensory information perceived; localization - the strategy to estimate the robot position within the spatial map; path planning - the strategy to find a path towards a goal location being optimal or not; and path execution, where motor actions are determined and adapted to environmental changes. This book integrates results from the research work of authors all over the world, addressing the abovementioned activities and analyzing the critical implications of dealing with dynamic environments. Different solutions providing adaptive navigation are taken from nature inspiration, and diverse applications are described in the context of an important field of study: social robotics

Attitude and Tension Control of a Tethered Formation of Aerial Vehicles

In this thesis we deal with the problem of formation control exploiting external constraints. In particular, we want to tether two quadrotors to each other and to a fixed point by ropes. Then, we want to control the quadrotors in order to drive the orientation of the formation, keeping the cables tautopenEmbargo per motivi di segretezza e/o di proprietà dei risultati e/o informazioni sensibil

Sensing Highly Non-Rigid Objects with RGBD Sensors for Robotic Systems

The goal of this research is to enable a robotic system to manipulate clothing and other highly non-rigid objects using an RGBD sensor. The focus of this thesis is to define and test various algorithms / models that are used to solve parts of the laundry process (i.e. handling, classifying, sorting, unfolding, and folding). First, a system is presented for automatically extracting and classifying items in a pile of laundry. Using only visual sensors, the robot identifies and extracts items sequentially from the pile. When an item is removed and isolated, a model is captured of the shape and appearance of the object, which is then compared against a dataset of known items. The contributions of this part of the laundry process are a novel method for extracting articles of clothing from a pile of laundry, a novel method of classifying clothing using interactive perception, and a multi-layer approach termed L-M-H, more specifically L-C-S-H for clothing classification. This thesis describes two different approaches to classify clothing into categories. The first approach relies upon silhouettes, edges, and other low-level image measurements of the articles of clothing. Experiments from the first approach demonstrate the ability of the system to efficiently classify and label into one of six categories (pants, shorts, short-sleeve shirt, long-sleeve shirt, socks, or underwear). These results show that, on average, classification rates using robot interaction are 59% higher than those that do not use interaction. The second approach relies upon color, texture, shape, and edge information from 2D and 3D data within a local and global perspective. The multi-layer approach compartmentalizes the problem into a high (H) layer, multiple mid-level (characteristics(C), selection masks(S)) layers, and a low (L) layer. This approach produces \u27local\u27 solutions to solve the global classification problem. Experiments demonstrate the ability of the system to efficiently classify each article of clothing into one of seven categories (pants, shorts, shirts, socks, dresses, cloths, or jackets). The results presented in this paper show that, on average, the classification rates improve by +27.47% for three categories, +17.90% for four categories, and +10.35% for seven categories over the baseline system, using support vector machines. Second, an algorithm is presented for automatically unfolding a piece of clothing. A piece of cloth is pulled in different directions at various points of the cloth in order to flatten the cloth. The features of the cloth are extracted and calculated to determine a valid location and orientation in which to interact with it. The features include the peak region, corner locations, and continuity / discontinuity of the cloth. In this thesis, a two-stage algorithm is presented, introducing a novel solution to the unfolding / flattening problem using interactive perception. Simulations using 3D simulation software, and experiments with robot hardware demonstrate the ability of the algorithm to flatten pieces of laundry using different starting configurations. These results show that, at most, the algorithm flattens out a piece of cloth from 11.1% to 95.6% of the canonical configuration. Third, an energy minimization algorithm is presented that is designed to estimate the configuration of a deformable object. This approach utilizes an RGBD image to calculate feature correspondence (using SURF features), depth values, and boundary locations. Input from a Kinect sensor is used to segment the deformable surface from the background using an alpha-beta swap algorithm. Using this segmentation, the system creates an initial mesh model without prior information of the surface geometry, and it reinitializes the configuration of the mesh model after a loss of input data. This approach is able to handle in-plane rotation, out-of-plane rotation, and varying changes in translation and scale. Results display the proposed algorithm over a dataset consisting of seven shirts, two pairs of shorts, two posters, and a pair of pants. The current approach is compared using a simulated shirt model in order to calculate the mean square error of the distance from the vertices on the mesh model to the ground truth, provided by the simulation model

Enabling New Functionally Embedded Mechanical Systems Via Cutting, Folding, and 3D Printing

Traditional design tools and fabrication methods implicitly prevent mechanical engineers from encapsulating full functionalities such as mobility, transformation, sensing and actuation in the early design concept prototyping stage. Therefore, designers are forced to design, fabricate and assemble individual parts similar to conventional manufacturing, and iteratively create additional functionalities. This results in relatively high design iteration times and complex assembly strategies

Recent Advances in Multi Robot Systems

To design a team of robots which is able to perform given tasks is a great concern of many members of robotics community. There are many problems left to be solved in order to have the fully functional robot team. Robotics community is trying hard to solve such problems (navigation, task allocation, communication, adaptation, control, ...). This book represents the contributions of the top researchers in this field and will serve as a valuable tool for professionals in this interdisciplinary field. It is focused on the challenging issues of team architectures, vehicle learning and adaptation, heterogeneous group control and cooperation, task selection, dynamic autonomy, mixed initiative, and human and robot team interaction. The book consists of 16 chapters introducing both basic research and advanced developments. Topics covered include kinematics, dynamic analysis, accuracy, optimization design, modelling, simulation and control of multi robot systems

Decentralized Autonomous Navigation Strategies for Multi-Robot Search and Rescue

In this report, we try to improve the performance of existing approaches for search operations in multi-robot context. We propose three novel algorithms that are using a triangular grid pattern, i.e., robots certainly go through the vertices of a triangular grid during the search procedure. The main advantage of using a triangular grid pattern is that it is asymptotically optimal in terms of the minimum number of robots required for the complete coverage of an arbitrary bounded area. We use a new topological map which is made and shared by robots during the search operation. We consider an area that is unknown to the robots a priori with an arbitrary shape, containing some obstacles. Unlike many current heuristic algorithms, we give mathematically proofs of convergence of the algorithms. The computer simulation results for the proposed algorithms are presented using a simulator of real robots and environment. We evaluate the performance of the algorithms via experiments with real robots. We compare the performance of our own algorithms with three existing algorithms from other researchers. The results demonstrate the merits of our proposed solution. A further study on formation building with obstacle avoidance for a team of mobile robots is presented in this report. We propose a decentralized formation building with obstacle avoidance algorithm for a group of mobile robots to move in a defined geometric configuration. Furthermore, we consider a more complicated formation problem with a group of anonymous robots; these robots are not aware of their position in the final configuration and need to reach a consensus during the formation process. We propose a randomized algorithm for the anonymous robots that achieves the convergence to a desired configuration with probability 1. We also propose a novel obstacle avoidance rule, used in the formation building algorithm.Comment: arXiv admin note: substantial text overlap with arXiv:1402.5188 by other author

Self-Triggered Formation Control of Nonholonomic Robots

In this paper, we report the design of an aperiodic remote formation controller applied to nonholonomic robots tracking nonlinear, trajectories using an external positioning sensor network. Our main objective is to reduce wireless communication with external sensors and robots while guaranteeing formation stability. Unlike most previous work in the field of aperiodic control, we design a self-triggered controller that only updates the control signal according to the variation of a Lyapunov function, without taking the measurement error into account. The controller is responsible for scheduling measurement requests to the sensor network and for computing and sending control signals to the robots. We design two triggering mechanisms: centralized, taking into account the formation state and decentralized, considering the individual state of each unit. We present a statistical analysis of simulation results, showing that our control solution significantly reduces the need for communication in comparison with periodic implementations, while preserving the desired tracking performance. To validate the proposal, we also perform experimental tests with robots remotely controlled by a mini PC through an IEEE 802.11g wireless network, in which robots pose is detected by a set of camera sensors connected to the same wireless network