Combining together all these new technologies follow- ing a formal optimization method is our challenge. This decision is a consequence of a several observa- tions. For example, VTOL vehicles control could be very costly in computing time because of the sys- tem’s dynamic instability. An adequate design modi- fication probably reduces that cost and allows a sta- bility improvement. The target applications mentioned before, especially indoor navigation, impose a strong design constraints. The robot should be compact, safe and not noisy. In most previous studies, efforts were focused either on design or on control of such sys- tems. This approach does not permits a global eval- uation of the problem, and in the case of systems difficult to control this separation could represent a handicap. Through the OS4 project, we advocate a different approach for simultaneously working on de- sign and control of microVTOL vehicles. This origi- nal approach makes it possible to simplify the control by design changes and vice versa as schematized in Fig. 2.
The high-level computation involves the global path planner which calculates the path, and a local path planner which generates the robot joint velocities required to traverse the local paths. These velocity values are sent to an easily accessible Arduino micro-controller via serial communication. The micro-controller converts these values to Pulse Width Modulation (PWM) signals, and then transmits these signals to the motor driver circuit which amplifies the signal in order to rotate the wheelchair motors at the desired speed. Now, the system is composed of varying electrical components like Raspberry Pi, Arduino micro-controller, motor driver, and wheelchair motors. All these components have different power voltages and current requirements. Thus, a separate power supply circuit has been designed to provide the appropriate power to the different components.
In this chapter, we focus on the initialization and failure recovery of estimators that are used for MAVs. In particular, we consider a monocular visual-inertial system (VINS) that consists of only an IMU and a camera. We are interested in this case because of two reasons. First, as we would like operate agile autonomous MAVs in confined envi- ronments, the platforms we use typically have very tight constrains on size, weight, and power (SWaP). Up to some point, a monocular VINS is the only viable setup due to its ultra light weight and small footprint. Second, this is a intellectually challenging prob- lem in which initialization is of great importance. For platforms with comprehensive set of sensors such as the one we used in Ch. 5, almost all states are directly observable by onboard sensors, and initialization can simply be done with the first sensor reading. How- ever, for monocular VINS, most of the critical navigation states such as initial velocity, attitude, as well as the metric scale are not directly observable, Providing a reasonable initial value of these states can be challenging without additional sensors or assumptions about the environments.
Only very recently it has been possible to equip MAVs with sufficient processing re- sources to perform stereo matching on-board. The MAV presented by Heng et al. (2011) features a forward-facing stereo camera and runs a dense block matching algorithm with a resolution of 320 × 240 pixels. This MAV was later extended by Meier et al. (2012) to use a larger image resolution of 640 × 480 pixels. In both cases, however, the stereo matching results are only used for obstacle avoidance, by creating a 3D occupancy map. For navigation, the MAV still depends on visual markers. This limitation was resolved by Fraundorfer et al. (2012), who equipping the MAV with the integrated optical flow cam- era and ultrasound altimeter developed by Honegger et al. (2013). This allows the MAV to perform autonomous exploration tasks in indoor and outdoor environments. However, according to the numbers given for the final revision of this MAV, stereo processing only runs at a relatively low frame rate of just 5 Hz.
lift required to remain airborne. This means that the the aircraft is usually un- able to hover and flying at slow speed raises the risk of aerodynamic stalling and impairs manoeuvrability. The exception being a small group of aircraft equipped with Vertical Take-Off and Landing (VTOL) functionality, which have the ability to vector the engine thrust. Examples include designs such as the BAe Harrier and the Bell/Boeing Osprey (see Figure3.2) which could be argued as being a rotary wing aircraft. The Osprey utilises a propeller driven propulsion system, which can be rotated through the lateral axis to either pull the aircraft forward or vertically to produce lift much like a helicopter.
For the microcontrollers, four Parallax Propeller boards and an Arduino boards are used. A Parallax MCU has eight cogs that can operate eight different tasks simultaneously without threading architecture on the processor. The stock frequency of the system clock is at 80 MHz, and it is overclocked to 100MHz to maximize the performance. The four onboard MCUs are used for sensor fusion, control, and communication with the ground control station and the FMC. The detailed is described in Chapter 5. An Arduino board is also incorporated to monitor battery status. Since monitoring battery level does not require high sampling rate, a 16MHz Arduino Micro board is chosen. The detailed battery voltage level monitoring circuit is discussed in chapter 2.1.7.
where, I is the initial obstacle distance from robot at 0 th frame, v is the robot’s linear velocity, f is the data acquisition frequency, and x is the frame number at which avoidance is initiated. Using the above relations, time and distances were computed and plotted on a graph of robot’s linear velocity illustrated in Figure 13, Avoidance maneuvers are initiated prior to at least five times the detected obstacle’s size providing sufficient distance and time for a successful avoidance maneuver which is extremely favorable in aerial robotics where underactuation is a major concern. Although our maneuvering and control command generation is per- formed on an entirely different principle when compared to the methodologies implemented by the afore mentioned literature, nevertheless, the required objectives are achieved successfully. Figure 13 and 8 represent the time before collision for higher forward velocities as 670 mm for real-time real-world tests, which evidently bolsters the briskness of our proposed model for indoormicro robot collision avoidance systems. Hence it can be claimed that using this system, steering commands may be generated well prior to the occurrence of a collision providing secure path at a very low computation load.
The exploration algorithm begins when the robot starts sensing the environment and building a map of the occupied space while concurrently initializing particles that represent the environment free space based on sensor observations (Sect. III-C). The particles are dispersed through the known and unknown space with dynamics defined by a stochastic differential equation (SDE) that considers collisions with the known occupied space defined by the current map (Sect.III- D). After the simulated application of particle dynamics, exploration frontiers are identified based on the particle dis- persion and sent to the autonomous navigation system in the form of navigation goals (Sect. III-E). The MAV navigates to these locations while incorporating sensor information into the map and defining new particles based on the sensor observations of the free space. After the final frontier is visited by the vehicle, the particle set is resampled based on the local density and current particle set (Sect. III-F), the SDE is re-evaluated and new frontiers are identified and sent to the autonomous navigation subsystem.
Received: 28 April 2020; Accepted: 23 May 2020; Published: 25 May 2020 Abstract: In this study, a semantic segmentation network is presented to develop an indoor navigation system for a mobile robot. Semantic segmentation can be applied by adopting different techniques, such as a convolutional neural network (CNN). However, in the present work, a residual neural network is implemented by engaging in ResNet-18 transfer learning to distinguish between the floor, which is the navigation free space, and the walls, which are the obstacles. After the learning process, the semantic segmentation floor mask is used to implement indoor navigation and motion calculations for the autonomous mobile robot. This motion calculations are based on how much the estimated path differs from the center vertical line. The highest point is used to move the motors toward that direction. In this way, the robot can move in a real scenario by avoiding different obstacles. Finally, the results are collected by analyzing the motor duty cycle and the neural network execution time to review the robot’s performance. Moreover, a different net comparison is made to determine other architectures’ reaction times and accuracy values.
Urban squatting was also seen, in this way, as the political other to ‘creative destruction’. The occupation and re-appropriation of empty build- ings and houses by squatters in various major cities in Europe from the late 1960s onwards offered a direct challenge to urban speculation, widespread housing shortages and commercial planning initiatives. As a form of ‘direct action’, squatting represented both an ‘attack on the unjust distribution of urban goods’ and an attempt to link alternative forms of collective living with non-institutional grassroots urban politics (Lo´pez, 2013: 871). For many squatters, this involved a basic attempt to carve out auton- omous spaces that not only responded to the hardships of creative destruction and accumula- tion by dispossession, but also served as eman- cipatory sites that would come to challenge the unyielding predetermination of lives and liveli- hoods (Bodenschatz et al., 1983; Pe´chu, 2010; SqEK, 2013; Vasudevan, 2011a). For others, this was predicated on queering the home as a site of domesticity and social reproduction and where the everyday micro-politics of making a ‘home’ countered not only traditional perfor- mances of housekeeping and kinship but also unsettled conventional distinctions between publicity and privacy and, in so doing, proffered radically new orientations for shared living (Brown, 2007; Cook, 2013; Amantine, 2011).
body by integration of the rigid-body angular velocity. However, due to problems associated with accuracy of estimator initial conditions, gyroscope bias, sensor gain and axis misalignments, it is well-known that this approach leads to errors, and po- tentially to the particularly disastrous eﬀect of gyroscope drift; a well known problem which causes the attitude estimates to continuously diverge over time. These prob- lems have led to the development of highly-accurate gyroscopes, which are usually very expensive and heavy devices, and are typically considered only for commercial applications. For other applications where size, weight and cost are important factors (such as in the area of small scale VTOL UAVs), control engineers prefer low-cost sen- sors, for example the Integrated Micro-Electro-Mechanical systems (IMEMs), which are cheap, small and lightweight (since they are contained within an integrated cir- cuit). In this case, rigorous design of robust attitude observers is required to deal with sensor inaccuracies.
chair and coming back up, bending and stay bent to tie shoelaces, frontal fall towards the radar, and crouching down to check below a piece of furniture and then standing back up. Sitting and standing (Figure 5a and 5b) appears to be different enough from the fall event (Figure 5e), but this is rather similar to the bending action (Figure 5d) and the initial part of the signatures of Figure 5c and 5f. This shows the importance of considering also the observation time to extract features in order to develop a classification scheme robust to false alarms, as well as highlighting the challenge posed by similar activities to fall, taking also into account the large variability of micro- movements for different subjects and for different environments.
Vrtuľník je lietadlo s rotujúcou nosnou plochou, z čoho vyplývajú aj jeho vlastnosti. Potrebný vztlak je dosiahnutý rotáciou listov rotoru okolo zvislej osi, pričom je profil listov v relatívnom pohybe voči okolitému vzduchu a vzniká na ňom vztlak rovnakým spôsobom ako na pevnej nosnej ploche. Rozdielom oproti pevnej nosnej ploche je, že vztlak je možné vyvodiť aj v prípade, keď sa vrtuľník nepohybuje. Navyše oproti vztlakovým motorom a vztlakovým vrtuliam je tento spôsob efektívnejší. V prípade vztlakových motorov je potrebné vyvodiť reakčnú silu pre kolmý vzlet zrýchlením dostatočného množstva vzduchu na vysokú rýchlosť a to je spojené s veľkou spotrebou prúdových motorov a prudkým výtokom horúcich plynov, ktoré môžu poškodiť vzletovú plochu. Oproti rotoru majú tieto vztlakové systémy ďalšiu nevýhodu a tou je problém ovládateľnosti a bezpečného pristátia v režime kolmého vzletu pri poruche motora. Vrtuľník má možnosť prechodu do autorotácie, v ktorej je ovládateľný a po dokĺzaní nepotrebuje rozľahlú plochu na pristátie. Pri VTOL lietadlách je tento problém možné kompenzovať zvýšením výkonu zostávajúcich pohonných jednotiek, pokiaľ to dovoľuje ich usporiadanie a technické riešenie.
2017 UAV is becoming versatile where fixed wing have high endurance and range and multicopter has hovering and various terrain operation, combination of both gives the idea of autonomous transition hence a conceptual design was made of VTOL capability which is the combination of fixed wing aircraft and multicopter for the mode of operation, which is suitable for hover as well as cruise mode operation and here the CG was fixed such a way to maintain hover, cruise along with transition, hence autopilot was developed for smooth transition.
Professional truck drivers are an essential part of transportation in keeping the global economy alive and commercial products moving. In order to increase productivity and improve safety, an increasing amount of automation is implemented in modern trucks. Transition to automated heavy good vehicles is intended to make trucks accident-free and, on the other hand, more com- fortable to drive. This motivates the automotive industry to bring more embedded ICT into their vehicles in the future. An avenue towardsautonomous vehicles requires robust environmental perception and driver monitoring technologies to be introduced. This is the main motivation be- hind the DESERVE project. This is the study of sensor technology trials in order to minimize blind spots around the truck and, on the other hand, keep the driver’s vigilance at a sufficiently high level. The outcomes are two innovative truck demonstrations: one R & D study for bringing equipment to production in the future and one implementation to the driver training vehicle. The earlier experiments include both driver monitoring technology which works at a 60% - 80% ac- curacy level and environment perception (stereo and thermal cameras) whose performance rates are 70% - 100%. The results are not sufficient for autonomous vehicles, but are a step forward, since they are in-line even if moved from the lab to real automotive implementations.
Security and Surveillance Mission Platform (MSSMP). The MSSMP operational scenario was based on a squad of three MPs deploying with a High Mobility Multi Wheeled Vehicle HMMWV towing a trailer holding three air mobility platforms. When the squad reached a central location in their area of responsibility they would launch one or all of the air mobility platforms to locations at which they desired to perform long term ground surveillance. The air mobility platform was a shrouded rotor, VTOL UAV with a sensor suite mounted on top of it. The platform would fly to target location where it would autonomously land and then conduct long term surveillance with its on board sensors. To reduce communication power and time of communication the sensor data was processed onboard the platform by automatic motion detection software. This allowed the system operator to monitor several systems at once since information was broadcast only when something of interest was occurring. At the end of the mission or when surveillance was required in another location the system would be commanded to restart, takeoff and go to the new location or return to its launch point.
Fully deﬁning a healthy indoor microbiome is likely to be a slow, iterative process. Each microbiome contains thousands of species, which each have diverse microbial functions. Many of these organisms are quite rare, and most may be irrelevant to health outcomes of interest. Additionally, exposures to microbes occur simultaneously with exposures to chemicals, allergens, and pollutants. The effect of these agents may also vary based on characteristics of the population, such as age and diet, and exposure route (ingestion, inhalation, or dermal) and exposure timing may contribute additional complexity to the process. A comprehensive deﬁnition of a healthy microbiome must also account for different building types and various building uses.
Many kinds of locating systems have been developed to estimate an autonomous mobile robot's absolute position in an indoor set-up . In one study , a laser range finder was used to estimate a mobile robot position which identified artificial landmarks positioned in the environment. In another study , data gathered from the ultrasonic sensors of the robot was matched with an environmental global map for calculating its position. Additionally, studies have been conducted using radio frequency (RF) operated systems for determining the location of mobile robots [29, 30]. In , movable objects estimate their location by employing the Time-of- Arrival technique (TOA). The Q-Track  system uses moving objects that send signals to fixed receivers. This information is sent to a central unit which calculates the object location. The effectiveness of RF Indoor Positioning Systems is limited due to multiple reflections of the sent signal.
Managing efficiently such scattered systems becomes increasingly complex and requires powerful management capabilities. Traditional solutions to manage and control them seem to have reached their limits. In recent years, integrated management systems and services, autonomic systems have raised much interest in distributed systems and software engineering . An autonomic system is capable to repair, configure, heal and protect itself . The emerging field of autonomic distributed computing addresses the challenge of how to design and build distributed computing systems that can manage, heal and optimise themselves. Distributed computing systems are moving towards increasingly autonomous operation and management, in which their interacting components can organise, regulate, repair and optimise themselves without human intervention. . These systems are intended to tackle administration complexity that is out of reach of human administrators, for instance handling a large number of alarms and notifications. Besides, automating management may reduce cost and improve efficiency. To automate management, we need at least three key elements: (a) representation, observation and monitoring capabilities, (b) decision rules and mechanisms and (c) control mechanisms.
(2016-18) is to create an environment for real-time testing of connected autonomous vehicles . It involves equipping over 40 miles of urban roads, dual-carriageways and motorways within Coventry and Warwickshire with V2X technologies. The i-MOTORS project (2016 - 2018) is devoted to developing a vehicular cloud computing platform that fuses data from vehicles with information from the road environment to create dynamic maps and real-time alerts of possible roadway hazards . Expected benefits from the i-MOTORS project are twofold, (i) reduction in fuel consumption and travel time by considering real time traﬃc data for active route planning, and (ii) improvement in road safety via car platooning. The G-ACTIVE project (2016 - 2019) targets a reduction of fuel consumption for passenger and light duty road vehicles for a range of drivetrain architectures (conventional, electric and hybrid electric) by leveraging oﬀ-board data in- cluding traﬃc condition and timing of traﬃc lights . This oﬀ-board information will be used to simultaneously optimize drivetrain energy and vehicle driving speed. The aim of the CARMA project (2016-2021) is to develop and test a cooperative automated driv- ing technology based on a distributed control system enabled by an ultra-low latency and highly reliable cloud-based infrastructure . Although there are several past and on-going research activities in the domain of CAV technology, the potentials and limita- tions of this technology in addressing the issues of current transportation system is not well investigated . Therefore, this paper is devoted to analysing achievable benefits of exploiting oﬀ-board data gathered via V2X communications, and inter-vehicular cooper- ation on autonomous vehicles. To investigate the potentials and limitations of CAVs, a set of five use-cases is chosen and analysed through the results presented in the current technical literature. The first four use cases (i.e. vehicle platooning, lane change, intersec- tion management and energy management) have been selected to show examples of how connectivity can support cooperative manoeuvring thereby improving road transporta- tion whereas the last use-case (i.e. cooperative road friction estimation) is dedicated to demonstrating how perception of the surrounding environment can be improved when vehicles cooperatively share their local perception knowledge. The analysis of coopera- tive localisation systems has been performed in a separate work by the authors and is reported in .