An individual`s eyes provide a wealth of information during social interactions. The present research investigates the social adjustment implications of one gaze behaviour, specifically, shared attention, which is the tendency to follow an interlocutor`s directed gaze to attend to the same object or location. Recent clinical research suggested that gaze control reflects the capacity to differentiate self from other at the attentional level, since patient populations with poor gaze control abilities (i.e., schizophrenic patients) were also found to exhibit difficulty in differentiating between the self and another agent. Four studies were conducted to examine whether flexible gaze following behavior, specifically the ability to inhibit gaze-following, when the situation warrants, would be positively linked with two markers of adaptive social functioning: sociocognitive abilities and self-close other(s) differentiation. Based on previous research that gaze cues linked to upright (but not inverted) faces trigger reflexive gaze following mechanisms, an upright face condition was used to assess social cueing mechanisms and an inverted face condition, as a control for non-social cueing mechanisms in a gaze control task with realistic (Study 2) and schematic faces (Studies 1, 3, and 4). Studies 1-4 showed that more flexible gaze following behavior predicted superior sociocognitive abilities, as indexed by higher capacity to infer the mental states of others in both young and older adults (Studies 1-3), as well as in clinical populations (i.e., Parkinson’s Disease [PD] patients, Study 4). Studies 2-4 further revealed that poorer gaze control predicted decreased self-close other differentiation in both younger and older adults. In Study 2, poorer gaze control performance characterized young adults from enmeshed family systems, which allow limited private space and emotional autonomy. In Studies 3 and 4, poorer gaze control predicted decreased cognitive-affective differentiation from one’s spouse and lower marital quality in healthy elderly couples (Study 3) and elderly couples, where one spouse had PD (Study 4). The present findings argue for the existence of a unified sociocognitive network, perpetually shaped by one’s interpersonal history, and which encompasses perceptual mechanisms, specialized for face and gaze processing and higher-order cognitive mechanisms, specialized for processing the meaning (s) of social environments.

An individual`s eyes provide a wealth of information during social interactions. The present research investigates the social adjustment implications of one gaze behaviour, specifically, shared attention, which is the tendency to follow an interlocutor`s directed gaze to attend to the same object or location. Recent clinical research suggested that gaze control reflects the capacity to differentiate self from other at the attentional level, since patient populations with poor gaze control abilities (i.e., schizophrenic patients) were also found to exhibit difficulty in differentiating between the self and another agent. Four studies were conducted to examine whether flexible gaze following behavior, specifically the ability to inhibit gaze-following, when the situation warrants, would be positively linked with two markers of adaptive social functioning: sociocognitive abilities and self-close other(s) differentiation. Based on previous research that gaze cues linked to upright (but not inverted) faces trigger reflexive gaze following mechanisms, an upright face condition was used to assess social cueing mechanisms and an inverted face condition, as a control for non-social cueing mechanisms in a gaze control task with realistic (Study 2) and schematic faces (Studies 1, 3, and 4). Studies 1-4 showed that more flexible gaze following behavior predicted superior sociocognitive abilities, as indexed by higher capacity to infer the mental states of others in both young and older adults (Studies 1-3), as well as in clinical populations (i.e., Parkinson’s Disease [PD] patients, Study 4). Studies 2-4 further revealed that poorer gaze control predicted decreased self-close other differentiation in both younger and older adults. In Study 2, poorer gaze control performance characterized young adults from enmeshed family systems, which allow limited private space and emotional autonomy. In Studies 3 and 4, poorer gaze control predicted decreased cognitive-affective differentiation from one’s spouse and lower marital quality in healthy elderly couples (Study 3) and elderly couples, where one spouse had PD (Study 4). The present findings argue for the existence of a unified sociocognitive network, perpetually shaped by one’s interpersonal history, and which encompasses perceptual mechanisms, specialized for face and gaze processing and higher-order cognitive mechanisms, specialized for processing the meaning (s) of social environments.

Autonomous vehicles are an emerging trend in robotics, seen in a vast range of applications and environments. Consequently, Volvo Construction Equipment endeavour to apply the concept of autonomous vehicles onto one of their main products. In the company’s Autonomous Machine project an autonomous wheel loader is being developed. As an ob jective given by the company; a demonstration proving the possibility of conducting a fully autonomous load and haul cycle should be performed. Conducting such cycle requires the vehicle to be able to localise itself in its task space and navigate accordingly. In this Master’s Thesis, methods of solving those requirements are proposed and evaluated on a real wheel loader. The approach taken regarding localisation, is to apply sensor fusion, by extended Kalman filtering, to the available sensors mounted on the vehicle, including; odometric sensors, a Global Positioning System receiver and an Inertial Measurement Unit. Navigational control is provided through an interface developed, allowing high level software to command the vehicle by specifying drive paths. A path following controller is implemented and evaluated. The main objective was successfully accomplished by integrating the developed localisation and navigational system with the existing system prior this thesis. A discussion of how to continue the development concludes the report; the addition of a continuous vision feedback is proposed as the next logical advancement.

Autonomous vehicles have many application possibilities within many different fields like rescue missions, exploring foreign environments or unmanned vehicles etc. For such system to navigate in a safe manner, high requirements of reliability and security must be fulfilled. This master's thesis explores the possibility to use the machine learning algorithm convolutional network on a robotic platform for autonomous path following. The only input to predict the steering signal is a monochromatic image taken by a camera mounted on the robotic car pointing in the steering direction. The convolutional network will learn from demonstrations in a supervised manner. In this thesis three different preprocessing options are evaluated. The evaluation is based on the quadratic error and the number of correctly predicted classes. The results show that the convolutional network has no problem of learning a correct behaviour and scores good result when evaluated on similar data that it has been trained on. The results also show that the preprocessing options are not enough to ensure that the system is environment dependent.

The utilization of micro-scale technologies is limited by the speed of their manufacture. Micro-milling is one particular technology used to manufacture micro-scale parts which could benefit extensively from an increase in throughput. Micro-milling involves a rotating cutter slightly thicker than a human hair removing material while spinning at speeds often over one hundred thousand revolutions per minute. An obvious solution to the throughput bottleneck is to move current micro-mills faster using existing technology; however, simply increasing the operational speed of existing micro-mills will lead to vibration and trajectory following problems. If a micro-mill cannot be positioned precisely, then part tolerances cannot be maintained. Thus any increase in throughput would be counterproductive in terms of overall performance. This dissertation presents techniques to improve the performance of micro-mills, as well as other flexible machines. Theses improvements are possible through the utilization of the vibration suppression scheme of input shaping. By thoughtfully altering the commands sent to flexible systems, their vibration can be significantly reduced. Input shaping was effectively applied to an existing micro-mill, which improved part tolerances and increased operational speed. However, at extremely high speeds, traditional input shaping is not effective at following complicated trajectories. Therefore, new input shaping techniques were developed specifically for trajectory tracking of extremely fast motions on micro-mills and other flexible systems. Often machines cannot achieve these high speeds while maintaining their accuracy because of the mechanical design of the machines themselves. If the mechanical design of micro-mills and other machines consider flexible and lightweight design alternatives that utilize input shaping for vibration suppression instead of stiff and heavy designs, then faster machine motion will be possible. By considering input shaped flexible systems as part of traditional mechanical design processes, these flexible solutions allow vast performance improvement. Specifically, embodiment design can be improved through consideration of input shaping performance requirements. Through these advancements, this dissertation improves the design, control, and performance of micro-mills and other flexible machines.

Nowadays, the location technology in the field of the Intelligent Transformation System (ITS) is used generally. Most of location devices on the cars are low-cost GPS, however, it¡¦s not enough if we want to combine with the safe algorithm. Hence, we present a suit of vehicle collision-avoidance system which combined location technology with Intersection-agent in this thesis. The system uses vehicle sensors and GPS information to calculate in Extend Kalman Filter, in order to get the optimal location information. Furthermore, Map-Matching algorithm is used to match the vehicle location on the right road. As to the driver¡¦s safety, laser range scanner¡¦s data are used in fuzzy algorithm and calculate the safe distance between cars. In the intersection area where accident happened most, we also combine with Intersection-agent system to enhance safety. When moving objects cross through the intersection area, Intersection-agent system would use laser range scanner to find the moving objects¡¦ position and velocity, judging whether they can pass the intersection safely or not. Once it¡¦s not safe, system would send out warning signal to the drivers to brake cars, also, passing the position information to car location system by wireless RS-232 transceiver, to decrease location error and let vehicle¡¦s location precision more accurate. In brief, this thesis combines with vehicle location, wireless transmission, car following warning system and Intersection-agent. And make sure this system we developed can fit in with traffic requirement in many experiments.

In daily life, there are a lot of inconveniences for the vision disabled people, even thought there are few equipments for them to use. However, there has no equipment to guide the vision disabled people on the pedestrian crossing, it will cause them being a dangerous situation while through the pedestrian crossing. To design an intelligent vehicle to help vision disabled people through the pedestrian crossing safely is an important topic. This thesis presents an autonomous transportation robot in intersections to provide older and vision disabled people to through the pedestrian crossing safely. This robot is based on a commercial wheelchair which equipped with cameras, inertial measurement unit, encoder, GPS module, hearts rate sensor, etc. In this study, by using the camera settled in the top of the robot to capture the picture, it could detect the region of pedestrian crossing and find the obstacles and pedestrian on the pedestrian crossing with image processing techniques. Then using the fuzzy controller to do the obstacle avoidance or people following. This robot can make the automatic parking after through the pedestrian crossing and transmit the position of the robot and the user¡¦s heart rate to the remote monitor system.

This thesis aims to implement a linear wireless transmitter based on the envelope-following architecture. A class-E PA is utilized to replace the linear PA used in the traditional envelope-following transmitter for enhancing the average efficiency. The transmitter relies on a digital processor realized by FPGA to generate the baseband IQ signal and corresponding envelope signal. This way can not only achieve more accurate modulation accuracy and wider modulation bandwidth, but also use less analog components for the future convenience of realizing single-chip integration when compared to the traditional envelope-following transmitter. Furthermore, this thesis implements a predistorter in the digital processor to compensate the Vdd/AM distortion of class-E amplifier. Therefore, this transmitter can simultaneously achieve high efficiency and high linearity over a wide input power range. From the results measured in transmitting a QPSK-modulated CDMA2000 1x signal at a chip rate of 1.2288 Mcps, the transmitter incorporating an InGaAs pHEMT class-E PA can achieve 30~44 % in average efficiency (23~38 % in average PAE) with above 44 dBc in ACPR and below 4 % in EVM in the average modulated output power range from 7 to 21 dBm, while the transmitter incorporating a GaAs HBT can achieve 20~40 % in average efficiency (16~35 % in average PAE) with above 43 dBc in ACPR and below 5 % in EVM in the average modulated output power range from 4 to 18.5 dBm.

Terrestrial robots must be capable of negotiating rough terrain if they are to become autonomous outside of the lab. Although the control mechanism offered by wheels is attractive in its simplicity, any wheeled system is confined to relatively flat terrain. Wheels will also only ever be useful for rolling, while limbs observed in nature are highly multimodal. The robust locomotive utility of legs is evidenced by the many animals that walk, run, jump, swim, and climb in a world full of challenging terrain. On the other hand, legs with multiple degrees of freedom (DoF) require much more complex control and precise sensing than wheels. Legged robotic systems are easily hampered by sensor noise and bulky control loops that prohibit the high-speed adaptation to external perturbations necessary for dynamic stability in real time. Low sensor bandwidth can limit the system’s reaction time to external perturbations. It is also often necessary to filter sensor data, which introduces significant delays in the control loop. In addition, state estimation is often relied upon in order to compute active stabilizing responses. State estimation requires accurate sensor data, often involving filtering, and can involve additional nontrivial computation such as the pseudo-inversion of fullbody Jacobians. This perception portion of the control burden is all incurred before a response can be planned and executed. These delays can prevent a system from executing a corrective response before instability leads to failure. The present work presents an approach to legged system design and control that reduces both the perception and planning aspects of the online control burden. A commonly accepted design goal in robotics is to accomplish a task with the fewest possible DoF in order to tighten the control loop and avoid the curse of dimensionality. However, animals control many DoF in a manner that adapts to external perturbations faster than can be explained by efferent neural control. The passive mechanics of segmented animal limbs are capable of rejecting unexpected disturbances without the supervision of an active controller. By simulating biomimetic limbs, we can learn more about this preflexive response, how the properties of segmented biological limbs foster self-stable passive mechanics, and how the control burden can be mitigated in robotic legged systems. The contribution of this body of work is to reduce the control burden of legged locomotion for robots by drawing on self-stabilizing mechanical design and control principles observed in animal locomotion. To that end, minimal templates such as Sensory-Coupled Action Switching Modules (SCASM), Central Pattern Generators (CPGs), and the Spring-Loaded Inverted Pendulum (SLIP) model are used to learn more about the essential components of legged locomotion. The motivation behind this work lies largely in the study of how internal, predictive models and the intrinsic mechanical properties of biological limbs help animals self-stabilize in real time. Robotic systems have already begun to demonstrate the benefits of these biological design primitives in an engineering context, such as reduced cost of transportation and an immediate mechanical response that does not need to wait for sensor feedback or planning. The original research presented here explores the extent to which these principles can be utilized in order to encourage stable legged locomotion over uneven terrain with as little sensory information as possible. A method for generating feedforward, terrain-adaptive control primitives based on a compliant limb architecture is developed. Offline analysis of system dynamics is used to develop clock-driven patterns of leg stiffness and attack angle control during late swing with which passive stance phase dynamics will produce the desired apex height and stride period to within 0.1 mm and 50 μs, respectively. A feedforward method of energy modulation is incorporated that regulates velocity to within 10−5 m/s. Preservation of a constant stride period eliminates the need for detection of the apex event. Precise predictive controls based on thorough offline dynamic modeling reduce the system’s reliance on state and environmental data, even in rough terrain. These offline models of system dynamics are used to generate a controller that predicts the dynamics of running over uneven terrain using an internal clock signal. Real-time state estimation is a non-trivial bottleneck in the control of mobile systems, legged and wheeled alike. The present work significantly reduces this burden by generating predictive models that eliminate the need for state estimation within the control loop, even in the presence of damping. The resulting system achieves not only self-stable legged running, but direct control of height, speed, and stride period without inertial sensing or force feedback. Through this work, the controller dependency on accurate and rapid sensing of the body height and velocity, apex event, and ground variation was eliminated. This was done by harnessing physics-based models of leg dynamics, used to generate predictive controls that exploit the passive mechanics of the compliant limb to their full potential. While no real world system is entirely deterministic, such a predictive model may serve as the base layer for a lightweight control architecture capable of stable robotic limb control, as in animal locomotion.

This research is mainly about autonomously navigation of an agricultural wheeled mobile robot in an unstructured outdoor setting. This project has four distinct phases defined as: (i) Navigation and control of a wheeled mobile robot for a point-to-point motion. (ii) Navigation and control of a wheeled mobile robot in following a given path (path following problem). (iii) Navigation and control of a mobile robot, keeping a constant proximity distance with the given paths or plant rows (proximity-following). (iv) Navigation of the mobile robot in rut following in farm fields. A rut is a long deep track formed by the repeated passage of wheeled vehicles in soft terrains such as mud, sand, and snow. To develop reliable navigation approaches to fulfill each part of this project, three main steps are accomplished: literature review, modeling and computer simulation of wheeled mobile robots, and actual experimental tests in outdoor settings. First, point-to-point motion planning of a mobile robot is studied; a fuzzy-logic based (FLB) approach is proposed for real-time autonomous path planning of the robot in unstructured environment. Simulation and experimental evaluations shows that FLB approach is able to cope with different dynamic and unforeseen situations by tuning a safety margin. Comparison of FLB results with vector field histogram (VFH) and preference-based fuzzy (PBF) approaches, reveals that FLB approach produces shorter and smoother paths toward the goal in almost all of the test cases examined. Then, a novel human-inspired method (HIM) is introduced. HIM is inspired by human behavior in navigation from one point to a specified goal point. A human-like reasoning ability about the situations to reach a predefined goal point while avoiding any static, moving and unforeseen obstacles are given to the robot by HIM. Comparison of HIM results with FLB suggests that HIM is more efficient and effective than FLB. Afterward, navigation strategies are built up for path following, rut following, and proximity-following control of a wheeled mobile robot in outdoor (farm) settings and off-road terrains. The proposed system is composed of different modules which are: sensor data analysis, obstacle detection, obstacle avoidance, goal seeking, and path tracking. The capabilities of the proposed navigation strategies are evaluated in variety of field experiments; the results show that the proposed approach is able to detect and follow rows of bushes robustly. This action is used for spraying plant rows in farm field. Finally, obstacle detection and obstacle avoidance modules are developed in navigation system. These modules enables the robot to detect holes or ground depressions (negative obstacles), that are inherent parts of farm settings, and also over ground level obstacles (positive obstacles) in real-time at a safe distance from the robot. Experimental tests are carried out on two mobile robots (PowerBot and Grizzly) in outdoor and real farm fields. Grizzly utilizes a 3D-laser range-finder to detect objects and perceive the environment, and a RTK-DGPS unit for localization. PowerBot uses sonar sensors and a laser range-finder for obstacle detection. The experiments demonstrate the capability of the proposed technique in successfully detecting and avoiding different types of obstacles both positive and negative in variety of scenarios.