To design a pedestrian crossing area reasonably can be a demanding task for traffic planners. There are several challenges, including determining the appropriate dimensions, and ensuring that pedestrians are exposed to the least risks. Pedestrian safety is especially obscure to analyze, given that many people in Stockholm cross the street illegally by running against the red light. To cope with these challenges, computational approaches of trajectory data visual analytics can be used to support the analytical reasoning process. However, it remains an unexplored field regarding how to visualize and communicate the street-crossing spatio-temporal data effectively. Moreover, the rendering also needs to deal with a growing data size for a more massive number of people. This thesis proposes a web-based interactive visual analytics tool for pedestrians' street-crossing behavior under various flow rates. The visualization methodology is also presented, which is then evaluated to have achieved satisfying communication and rendering effectiveness for maximal 180 agents over 100 seconds. In terms of the visualization scenario, pedestrians either wait for the red light or cross the street illegally; all people can choose to stop by a buffer island before they finish crossing. The visualization enables the analysis under multiple flow rates for 1) pedestrian movement, 2) space utilization, 3) crossing frequency in time-series, and 4) illegal frequency. Additionally, to acquire the initial trajectory data, Optimal Reciprocal Collision Avoidance (ORCA) algorithm is engaged in the crowd simulation. Then different visualization techniques are utilized to comply with user demands, including map animation, data aggregation, and time-series graph. / Att konstruera ett gångvägsområde kan rimligen vara en krävande uppgift för trafikplanerare. Det finns flera utmaningar, bland annat att bestämma lämpliga dimensioner och se till att fotgängare utsätts för minst risker. Fotgängarnas säkerhet är särskilt obskyrlig att analysera, eftersom många människor i Stockholm korsar gatan olagligt genom att springa mot det röda ljuset. För att klara av dessa utmaningar kan beräkningsmetoder för bana data visuell analys användas för att stödja den analytiska resonemangsprocessen. Det är emellertid ett oexplorerat fält om hur man visualiserar och kommunicerar gataövergången spatio-temporal data effektivt. Dessutom måste rendering också hantera en växande datastorlek för ett mer massivt antal människor. Denna avhandling föreslår ett webbaserat interaktivt visuellt analysverktyg för fotgängares gatöverföring under olika flödeshastigheter. Visualiseringsmetoden presenteras också, som sedan utvärderas för att ha uppnått tillfredsställande kommunikation och effektivitet för maximal 180 agenter över 100 sekunder. Vad beträffar visualiseringsscenariot, väntar fotgängare antingen på det röda ljuset eller tvärs över gatan; alla människor kan välja att stanna vid en buffertö innan de slutar korsa. Visualiseringen möjliggör analysen under flera flödeshastigheter för 1) fotgängarrörelse, 2) rymdutnyttjande, 3) korsfrekvens i tidsserier och 4) olaglig frekvens. För att förvärva den ursprungliga bana-data är Optimal Reciprocal Collision Avoidance (ORCA) algoritmen förknippad med folkmassimuleringen. Därefter utnyttjas olika visualiseringstekniker för att uppfylla användarnas krav, inklusive kartanimering, dataaggregering och tidsserier.

Testing of eye tracking devices is done by humans looking at well defined stimuli. This way of testing eye trackers is not accurate enough because of human errors. The goal of this thesis is to design and construct reliable robotic eyes that can mimic the behaviour of human eyes. After a pre-study where human eyes, eye tracking and previous robotic eyes were studied, system requirements and specifications were formulated. Based on the re-quirements important design decisions were taken such as the use of RC servo motors, push rods, microcontrollers and a Raspberry Pi. Later the inverse kinematics of the movements and a saccade’s path planing were modelled. Additional mechanical de-sign features are rotation of the head and adjustment of the interpupillary distance. The robot is controlled using two types of application programming interfaces (APIs.) The first API is used to control the motors and the second API builds on top of the first API but is used to design paths of different eye movements between fixation points. All eye movement calculations are computed on the Raspberry Pi before the movements are communicated in real time to the microcontroller which directly performs the control signal. The robot was tested using the integrated lasers in the eyes and a video cam-era with slow motion capabilities to capture the projected laser dot on a wall. The properties tested are saccade, smooth pursuit, head rotation and eye tracking device compatibility. The results show high precision but not enough accuracy. The robot needs a few mechanical improvements such as removing the backlash in the rotat-ing joints on the eyes, decreasing the flexibility of some of the 3D printed parts and assuring symmetry in the design. The robot is a powerful testing platform capa-ble of performing all eye movement types with high-resolution control of both eyes independently through an API. / Eyetracking utrustning testas av människor som tittar på väldefinierade stimuli. Att testa eyetracking på det här sättet är inte tillräckligt noggrant på grund av mänskligt fel. Malet med detta examensarbete är att designa och bygga en pålitlig ögonrobot som kan härma beteendet hos mänskliga ögon. Efter en förstudie om mänskliga ögon, eyetracking och existerade robotögon formulerades system-krav och -specikationer. Baserat på dessa krav togs en del betydande designbeslut som att använda RC servomotorer, tryckstånger, mikrokontrollers och en Raspberry Pi. Senare modellerades den inverterade kinematiken av rörelserna och saccaders banor. Ytterligare mekaniska funktioner är rotation av huvudet och justering av avståndet mellan pupillerna. Roboten styrs med hjälp av två applikationsprogrammeringsgränssnitt (API). Det första API:et används för att styra motorerna och det andra API:et bygger på det första men används för att bygga rörelsevanor av olika ögonrörelser mellan fixationspunkter. Alla ögonrörelseberåkningar görs på Raspberry Pin innan rörelsen kommuniceras i realtid till mikrokontrollen som på direkten exekverar styrsignalen. Roboten testades med integrerade lasrar i ögonen och en kamera med slow motion funktionalitet för att fånga laser prickens projektion på en vägg. Funktioner som testades är saccader, smooth pursuit, huvudrotation och eyetracking kompatibilitet. Resultat visade en hög precision men inte tillräckligt hög noggrannhet. Roboten behöver några få mekaniska förbättringar som att få bort glappet i de roterande lederna på ögat, minska flexibiliteten i några av de 3D-utskrivna delarna och garantera symmetri i designen. Roboten är en kraftfull testplatform kapabel till att utföra alla typer av ögonrörelser med högupplöst kontroll av båda ögonen var för sig genom ett API.

The Atomic Force Microscope (AFM) is a powerful tool for studying structure and dynamics at the nanometer scale. Despite its wide application in many applications, the slow imaging rate of AFM remains a severe limitation. Non-raster methods seek to overcome this limitation by appealing to alternative scan patterns, either designed to be easier for the actuators to follow or to reduce the amount of sampling needed. One particular example in this latter category is the local circular scan (LCS). LCS reduces the imaging time by scanning less sample area rather than scanning faster. It drives the tip of the AFM along a circular trajectory, using feedback to center that circle on a sample edge, and moving the circle along the feature, thus concentrating the samples to the region of interest. While this approach can have a significant impact on improving the imaging rate of any AFM, its impact is further enhanced when it is combined with high speed scanners. Due to its unique scanning pattern, a high-speed, Dual-Stage Actuator (DSA) system is a natural fit. DSAs consist of the serial combination of a (relatively) low-speed, long-range piezoelectric actuator (LRA) and a high-speed, short-range piezoelectric actuator (SRA). The SRA can be dedicated to implementing the local circular motion and the LRA to tracking the underlying sample. However, the control of a DSA scanner is challenging for at least three reasons: it is a multi-input, single-output system, it is a highly resonant system due to the underlying piezoelectric actuators, and it is a high-speed system. In this thesis, we address these challenges. First, we establish the controllability and observability of a general N-stage system whose outputs are summed to produce a single signal. This property allows us to develop individual controllers for the LRA and SRA of a DSA system so that we can focus our design on the specific requirements of each component and its desired action. While we apply both a Model Predictive Control (MPC) and simple state feedback approach to the LRA, our primary focus is on the SRA element as its high speed character makes it the more challenging component. Here we turn to receding horizon Linear Quadratic Tracking (LQT) control and develop methods to implement this approach at high speed using a Field Programmable Gate Array (FPGA). We develop three variants of LQT that differ in the required sample rates, memory resources, and computing power. Implementing and testing all three in both simulation and on a DSA scanning stage in our lab, we compare their performance and address the practical implementation considerations under the limitations imposed by the hardware. Finally, we combine the control of the LRA and SRA in two axes to demonstrate the LCS scanning approach. Overall, this thesis achieves a practical implementation of a model-based receding LQT design on a dual-stage, high speed, highly resonant actuator system. Through both simulation and experimental results, we demonstrate that this approach is robust to modeling error and disturbances and suitable for high-speed implementation of the LCS approach to non-raster AFM. / 2023-08-29T00:00:00Z

Nonlinear constrained optimal trajectory control is an important and fundamental area of research that continues to advance in numerous fields. Many attempts have been made to present new methods that can solve for optimal trajectories more efficiently or to improve the overall performance of existing techniques. This research presents a recently developed bio-inspired method called the Virtual Motion Camouflage (VMC) method that offers a means of quickly finding, within a defined but varying search space, the optimal trajectory that is equal or close to the optimal solution. The research starts with the polynomial-based VMC method, which works within a search space that is defined by a selected and fixed polynomial type virtual prey motion. Next will be presented a means of improving the solution’s optimality by using a sequential based form of VMC, where the search space is adjusted by adjusting the polynomial prey trajectory after a solution is obtained. After the search space is adjusted, an optimization is performed in the new search space to find a solution closer to the global space optimal solution, and further adjustments are made as desired. Finally, a B-spline augmented VMC method is presented, in which a B-spline curve represents the prey motion and will allow the search space to be optimized together with the solution trajectory. It is shown that (1) the polynomial based VMC method will significantly reduce the overall problem dimension, which in practice will significantly reduce the computational cost associated with solving nonlinear constrained optimal trajectory problems; (2) the sequential VMC method will improve the solution optimality by sequentially refining certain parameters, such as the prey motion; and (3) the B-spline augmented VMC method will improve the solution iv optimality without sacrificing the CPU time much as compared with the polynomial based approach. Several simulation scenarios, including the Breakwell problem, the phantom track problem, the minimum-time mobile robot obstacle avoidance problem, and the Snell’s river problem are simulated to demonstrate the capabilities of the various forms of the VMC algorithm. The capabilities of the B-spline augmented VMC method are also shown in a hardware demonstration using a mobile robot obstacle avoidance testbed.

With the recent trend for systems to be more and more autonomous, there is a growing need for cooperative trajectory planning. Applications that can be considered as cooperative systems such as surveying, formation flight, and traffic control need a method that can rapidly produce trajectories while considering all of the constraints on the system. Currently most of the existing methods to handle cooperative control are based around either simple dynamics and/or on the assumption that all vehicles have homogeneous properties. In reality, typical autonomous systems will have heterogeneous, nonlinear dynamics while also being subject to extreme constraints on certain state and control variables. In this thesis, a new approach to the cooperative control problem is presented based on the bio-inspired motion strategy known as local pursuit. In this framework, decision making about the group trajectory and formation are handled at a cooperative level while individual trajectory planning is considered in a local sense. An example is presented for a case of an autonomous farming system (e.g. scouting) utilizing nonlinear vehicles to cooperatively accomplish various farming task with minimal energy consumption or minimum time. The decision making and trajectory generation is handled very quickly while being able to consider changing environments laden with obstacles

In this dissertation, a novel and generic solution of trajectory generation is developed and evaluated for ground and aerial vehicles in a dynamic environment. By explicitly considering a kinematic model of the ground vehicles, the family of feasible trajectories and their corresponding steering controls are derived in a closed form and are expressed in terms of one adjustable parameter for the purpose of collision avoidance. A collision-avoidance condition is developed for the dynamically changing environment, which consists of a time criterion and a geometrical criterion. By imposing this condition, one can determine a family of collision-free paths in a closed form. Then, optimization problems with respect to different performance indices are setup to obtain optimal solutions from the feasible trajectories. Among these solutions, one with respect to the near-shortest distance and another with respect to the near-minimal control energy are analytical and simple. These properties make them good choices for real-time trajectory planning. Such optimal paths meet all boundary conditions, are twice differentiable, and can be updated in real time once a change in the environment is detected. Then this novel method is extended to 3D space to find a real-time optimal path for aerial vehicles. After that, to reflect the real applications, obstacles are classified to two types: "hard" obstacles that must be avoided, and "soft" obstacles that can be run over/through. Moreover, without losing generality, avoidance criteria are extended to obstacles with any geometric shapes. This dissertation also points out that the emphases of the future work are to consider other constraints such as the bounded velocity and so on. The proposed method is illustrated by computer simulations.

Many real-world electrical and mechanical systems have velocity-dependent constraints in their dynamic models. For example, car-like robots, unmanned aerial vehicles, autonomous underwater vehicles and hopping robots, etc. Most of these systems can be transformed into a chained form, which is considered as a canonical form of these nonholonomic systems. Hence, study of chained systems ensure their wide applicability. This thesis studied the problem of continuous feed-back control of the chained systems while pursuing inverse optimality and exponential convergence rates, as well as the feed-back stabilization problem under input saturation constraints. These studies are based on global singularity-free state transformations and controls are synthesized from resulting linear systems. Then, the application of optimal motion planning and dynamic tracking control of nonholonomic autonomous underwater vehicles is considered. The obtained trajectories satisfy the boundary conditions and the vehicles' kinematic model, hence it is smooth and feasible. A collision avoidance criteria is set up to handle the dynamic environments. The resulting controls are in closed forms and suitable for real-time implementations. Further, dynamic tracking controls are developed through the Lyapunov second method and back-stepping technique based on a NPS AUV II model. In what follows, the application of cooperative surveillance and formation control of a group of nonholonomic robots is investigated. A designing scheme is proposed to achieves a rigid formation along a circular trajectory or any arbitrary trajectories. The controllers are decentralized and are able to avoid internal and external collisions. Computer simulations are provided to verify the effectiveness of these designs.

State estimation is an essential part of any robotic autonomy solution. Continuous-time trajectory estimation is an attractive method because continuous trajectories can be queried at any time, allowing for fusion of multiple asynchronous, high-frequency measurement sources. This dissertation investigates various continuous-time estimation algorithms and their application to a handful of mobile robot autonomy and sensor calibration problems. In particular, we begin by analyzing and comparing two prominent continuous-time trajectory representations from the literature: Gaussian processes and splines, both on vector spaces and Lie groups. Our comparisons show that the two methods give comparable results so long as the same measurements and motion model are used. We then apply spline-based estimation to the problem of calibrating the extrinsic parameters between a camera and a GNSS receiver by fusing measurements from these two sensors and an IMU in continuous-time. Next, we introduce a novel estimation technique that uses the differential flatness property of dynamic systems to model the continuous-time trajectory of a robot on its flat output space, and show that estimating in the flat output space can provide superior accuracy and computation time than estimating on the configuration manifold. We use this new flatness-based estimation technique to perform pose estimation for velocity-constrained vehicles using only GNSS and IMU and show that modeling on the flat output space renders the global heading of the system observable, even when the motion of the system is insufficient to observe attitude from the measurements alone. We then show how flatness-based estimation can be used to calibrate the transformation between the dynamics coordinate frame and the coordinate frame of a sensor, along with other sensor-to-dynamics parameters, and use this calibration to improve the performance of flatness-based estimation when six-degree-of-freedom measurements are involved. Our final contribution involves nonlinear control of a quadrotor aerial vehicle. We use Lie theoretic concepts to develop a geometric attitude controller that utilizes logarithmic rotation error and prove that this controller is globally-asymptotically stable. We then demonstrate the ability of this controller to track highly-aggressive quadrotor trajectories.

The present study focuses on developing an efficient and stable unmanned aerial system traffic management (UTM) system that utilizes a data-driven target tracking method and a distributed path planning algorithm for multiple Unmanned Aerial Vehicle (UAV) operations with local dynamic networks, which can provide flexible scalability, enabling autonomous operation of a large number of UAVs in dynamically changing environment. Traditional dynamic motion-based target tracking methods often encounter limitations due to their reliance on a finite number of dynamic motion models. To address this, data-driven target tracking methods were developed based on the statistical model of the Gaussian mixture model (GMM) and deep neural networks of long-short term memory (LSTM) model, to estimate instant and future states of UAV for local path planning problems. The estimation accuracy of the data-driven target tracking methods were analyzed and compared with dynamic model-based target tracking methods. A hybrid dynamic path planning algorithm was proposed, which selectively employs grid-free and -based path search methods depending on the spatio-temporal characteristics of the environments. In static environment, the artificial potential field (APF) method was utilized, while the $A^*$ algorithm was applied in the dynamic state environment. Furthermore, the data-driven target tracking method was integrated with the hybrid path planning algorithm to enhance deconfliction. To ensure smooth trajectories, a minimum snap trajectory method was applied to the planned paths, enabling controller tracking that remains dynamically feasible throughout the entire operation of UAVs. The methods were validated in the Software-in-the-loop (SITL) demonstration with the simple PID controller of the UAVs implemented in the software program. / Ph.D. / This dissertation focuses on developing data-driven models for tracking and path planning of Unmanned Aerial Vehicle (UAV) in dynamic environments with multiple operations. The goal is to improve the accuracy and efficiency of Unmanned Aircraft System traffic management (UTM) under such conditions. The data-driven models are based on Gaussian mixture model (GMM) and long-short term memory (LSTM) and are used to estimate the instant and consecutive future states of UAV for local planning problems. These models are compared to traditional target tracking models, which use dynamic motion models like constant velocity or acceleration. A hybrid dynamic path planning approach is also proposed to solve dynamic path planning problems for multiple UAV operations at an efficient computation cost. The algorithm selectively employs a path planning method between grid-free and grid-based methods depending on the characteristics of the environment. In static state conditions, the system uses the artificial potential field method (APF). When the environment is time-variant, local path planning problems are solved by activating the $A^*$ algorithm. Also, the planned paths are refined by minimum snap trajectory to ensure that the path is dynamically feasible throughout a full operation of the UAV along with controller tracking. The methods were validated in the Software-in-the-loop (SITL) demonstration with the simple PID controller of the UAVs implemented in the software program.

The Mechatronics group in the Mechanical Engineering department of Cal Poly is interested in creating a demonstration of a ball-and-plate trajectory tracking controller on hardware. The display piece will serve to inspire engineering students to pursue Mechatronics and control theory as an area of study. The ball-and-plate system is open-loop unstable, underactuated, and has complicated, nonlinear equations of motion. These features present substantial challenges for control - especially if the objective is trajectory tracking. Because the system is underactuated, common nonlinear trajectory tracking control techniques are ineffective. This thesis lays out a theoretical foundation for controlling the hardware. Several important concepts related to ball-and-plate trajectory tracking control are presented. Models of the system, with various assumptions, are given and used in deriving control law candidates. To limit project scope, reasonable control criteria are introduced and used to evaluate designs from the thesis. Several control architectures are explored, these being Full-State Feedback with Integral Action, Single-Input-Single-Output Sliding Mode, and Full-State Feedback with Feed Forward. The mathematical reasoning behind each is detailed, simulation results are shown to validate their practicality and demonstrate features of the architectures, and trajectory similarity measure studies are produced to evaluate controller performance for a wide range of setpoint functions. The Full-State Feedback with Feed Forward controller is recommended based on its theoretical advantages and compliance with the control criteria over the competing designs. The control architecture has a proof of asymptotic tracking in the linear model, has excellent performance in simulations that use a nonlinear plant model, and produces the most pleasing visual experience when viewed in animation.