Trajectory solutions for a game-playing robot using nonprehensile manipulation methods and machine vision

The need for autonomous systems designed to play games, both strategy-based and physical, comes from the quest to model human behaviour under tough and competitive environments that require human skill at its best. In the last two decades, and especially after the 1996 defeat of the world chess champion by a chess-playing computer, physical games have been receiving greater attention. Robocup TM, i.e. robotic football, is a well-known example, with the participation of thousands of researchers all over the world. The robots created to play snooker/pool/billiards are placed in this context. Snooker, as well as being a game of strategy, also requires accurate physical manipulation skills from the player, and these two aspects qualify snooker as a potential game for autonomous system development research. Although research into playing strategy in snooker has made considerable progress using various artificial intelligence methods, the physical manipulation part of the game is not fully addressed by the robots created so far. This thesis looks at the different ball manipulation options snooker players use, like the shots that impart spin to the ball in order to accurately position the balls on the table, by trying to predict the ball trajectories under the action of various dynamic phenomena, such as impacts. A 3-degree of freedom robot, which can manipulate the snooker cue on a par with humans, at high velocities, using a servomotor, and position the snooker cue on the ball accurately with the help of a stepper drive, is designed and fabricated. [Continues.

Navigation under Obstacle Motion Uncertainty using Markov Decision Processes

In terms of navigation, a central problem in the field of autonomous robotics is obstacle avoidance. This research explores how to navigate as well as avoid obstacles by leveraging what is known of the environment to determine decisions with new incoming information during execution. The algorithm presented in this work is divided into two procedures: an offline process that uses prior knowledge to navigate toward the goal; and an online execution strategy that leverages results obtained offline to drive safely towards the target when new information is encountered (e.g., obstacles). To take advantage of what is known offline, the navigation problem was formulated as a Markov Decision Process (MDP) where the environment is characterized as an occupancy grid. Baseline dynamic programming techniques were used to solve this, producing general behaviors that drive the robot (or agent) toward the goal and a value function which encodes the value of being in particular states. Then during online execution, the agent uses these offline results and surrounding local information of the environment to operate (e.g., data from a LIDAR sensor). This locally acquired information, which may contain new data not seen prior, is represented as a small occupancy grid and leverages the offline obtained value function to define local goals allowing the agent to make short term plans. When the agent encounters an obstacle locally, the problem becomes a Partially Observable Markov Decision Process (POMDP) since it is uncertain where these obstacles will be in the next state. This is solved by utilizing an approximate planner (QMDP) that uses uncertainty of the obstacle motion and considers all possible obstacle state combinations in the next time step to determine the best action. The approximate planner can quickly solve the POMDP, due to the small size of the local occupancy grid and by using the behaviors produced offline to help speed up convergence, which opens the possibility for this procedure to be executed in real time, on a physical robot. Two simulated environments were created, varying in complexity and dynamic obstacles. Simulation results under complex conditions with narrow operable spaces and many dynamic obstacles show the proposed algorithm has approximately an 85% success rate, in test cases with cluttered environments and multiple dynamic obstacles, and is shown to produce safer trajectories than the baseline approach, which had roughly a 37% success rate, under the assumptions that dynamic obstacles can only move a short distance by the next time step

Estimating Acceleration from a Single Uniform Linear Motion-Blurred Image using Homomorphic Mapping and Machine Learning

Context:  Vision-based measurement (VBM) systems are becoming popular as an affordable and suitable alternative for scientific and engineering applications. When cameras are used as instruments, motion blur usually emerges as a recurrent and undesirable image degradation, which in fact contains kinematic information that is usually dismissed. Method: This paper introduces an alternative approach to measure relative acceleration from a real invariant uniformly accelerated linear motion-blurred image. This is done by using homomorphic mapping to extract the characteristic Point Spread Function (PSF) of the blurred image, as well as machine learning regression. A total of 125 uniformly accelerated motion-blurred pictures were taken in a light- and distance-controlled environment, at five different accelerations ranging between 0,64 and 2,4 m/s2. This study evaluated 19 variants such as tree ensembles, Gaussian processes (GPR), and linear, support vector machine (SVM), and tree regression. Results: The best RMSE result corresponds to GPR (Matern 5/2), with 0,2547 m/s2 and a prediction speed of 530 observations per second (obs/s). Additionally, some novel deep learning methods were used to obtain the best RMSE value (0,4639 m/s2 for Inception ResNet v2, with a prediction speed of 11 obs/s. Conclusions: The proposed method (homomorphic mapping and machine learning) is a valid alternative for calculating acceleration from invariant motion blur in real-time applications when additive noise is not dominant, even surpassing the deep learning techniques evaluated

Factors affecting brightness and colour vision under water

Both theoretical and practical importance can be attached to attempts to model human threshold and supra-threshold visual performance under water. Previously, emphasis has been given to the integration of visual data from experiments conducted in air with data of the physical specification of the underwater light field. However, too few underwater studies have been undertaken for the validity of this approach to be assessed. The present research therefore was concerned with the acquisition of such data. Four experiments were carried out: (a) to compare the predicted and obtained detection thresholds of achromatic targets, (b) to measure the relative recognition thresholds of coloured targets, (c) to compare the predicted and obtained supra-threshold appearance of coloured targets at various viewing distances and under different experimental instructions, (d) to compare the predicted and obtained detection thresholds for achromatic targets under realistic search conditions. Within each experiment, observers were tested on visual tasks in the field and in laboratory simulations. Physical specifications of targets and backgrounds were determined by photometry and spectroradiometry. The data confirmed that: (a) erroneous predictions of the detection threshold could occur when the contributions of absorption and scattering to the attenuation of light were not differentiated, (b) the successful replication of previous findings for the relative recognition thresholds of colours depended on the brightness of the targets, (c) the perceived change in target colour with increasing viewing distance was less than that measured physically, implying the presence of a colour constancy mechanism other than chromatic adaptation and simultaneous colour contrast; the degree of colour constancy also varied with the type of target and experimental instructions, (d) the successful prediction of the effects of target-observer motion and target location uncertainty required more than simple numerical corrections to the basic detection threshold model. It was concluded that further progress in underwater visibility modelling is possible provided that the tendency to oversimplify human visual performance is suppressed

ROObockey: Remote Controlled, Aim-Assisted Street Hockey Robot

The goal of the ROObockey project is to design and construct a floor hockey robot that can competitively shoot a puck. The robot design quickly locates a specific beacon through the use of image processing and uses a pneumatic shooting mechanism to send a puck to a specified target. The beacons act as possible player or goal positions in a hockey game. The robot also utilizes a wireless controller device to allow a user to maneuver the robot across a hockey field

ROOBockey Autonomous Hockey Robot

The goal of the ROOBockey project is to design and construct a floor hockey robot that can competitively shoot a puck. The robot design quickly locates a specific beacon through the use of image processing and uses a pneumatic shooting mechanism to send a puck to a specified target. The beacons act as possible player or goal positions in a hockey game. The robot also utilizes a wireless controller device to allow a user to maneuver the robot across a hockey field

Parametric impact characterisation of a solid sports ball, WITH a view to developing a standard core for the GAA Sliotar

The main aim of this research was to characterise the dynamic impact behaviour of the sliotar core. Viscoelastic characterisation of the balls was conducted for a range of impact speeds. Modern polymer balls exhibited strain and strain-rate sensitivity while traditional multi-compositional balls exhibited strain dependency. The non-linear viscoelastic response was defined by two values of stiffness, initial and bulk stiffness. Traditional balls were up to 2.5 times stiffer than the modern types, with this magnitude being rate-dependent. The greater rate of increase of traditional ball stiffness produced a more non-linear COR velocity-dependence compared to modern balls. The dynamic stiffness results demonstrated limited applicability of quasi-static testing and springtheory equations. Analysis of ball deformation behaviour demonstrated that centre-of mass displacement and diameter compression values were not consistently equivalent for all ball types. The contribution of manufacturing conditions to ball performance was investigated by conducting extensive prototyping experiments. Manufacturing parameters of temperature, pressure and material composition were varied to produce a range of balls. Polymer hardness affected stiffness but not energy dissipation, with increased hardness increasing ball stiffness. The nucleating additive influenced ball COR, with increased additive tending to reduce ball COR, but this effect was sensitive to polymer grade. The impact response of the ball was simulated using three mathematical models. The first model was shown to replicate ball behaviour to only a limited degree, despite being used previously with reported success for other ball types. The second model exhibited a reasonable representation of ball impact response that was universally applicable to all tested ball types; however, the accuracy in terms of predicting force-displacement response was not as high as required for broad range implementation. The third model exhibited significantly better accuracy in simulating ball response. The force values generated from this model exhibited up to 95% agreement with experimental data

Real Time Trajectory Optimization for Vision Based Navigation with Aerobatic Fixed-Wing Vehicles

Fixed-wing unmanned aerial vehicles (UAVS) pose advantages in energy efficiency, endurance, and speed, but also pose disadvantages in maneuverability. These maneuverability challenges can be addressed by exploiting high angle of attack maneuvers. However, navigation with fixed-wing UAVs in constrained spaces is still extremely difficult when the system state and environment are unknown. This essay investigates the use of vision sensors in autonomous navigation of aerobatic fixed-wing UAVs. Perception aware NMPC is explored through the integration of a visibility metric into the trajectory optimization problem. Additionally, a novel frontier-based NMPC method, which improves obstacle avoidance capabilities while mapping, is proposed. These methods are evaluated in a realistic real-time simulation

Vision testing and visual training in sport

This thesis examines vision testing and visual training in sport. Through four related studies, the predictive ability of visual and perceptual tests was examined in a range of activities including driving and one-handed ball catching. The potential benefits of visual training methods were investigated (with particular emphasis on stroboscopic training), as well as the mechanisms that may underpin any changes. A key theme throughout the thesis was that of task representativeness; a concept by which it is believed the more a study design reflects the environment it is meant to predict, the more valid and reliable the results obtained are. Chapter one is a review of the literature highlighting the key areas which the thesis as a whole addresses. Chapter’s two to five include the studies undertaken in this thesis and follow the same format each time; an introduction to the relevant research, a methods section detailing the experimental procedure, a results section which statistically analysed the measures employed, and a discussion of the findings with reference to the existing literature. Finally, in chapter six the strengths and limitations of the thesis are considered, before suggestions are made for future studies, and concluding remarks made