Autonomous ships are one way to increase safety at sea and to decrease environmental impact of marine traveling and shipping. For this application, a good representation of the environment and a physical model of the ship are vital components. By optimizing the trajectory of the ship, a good trade-off between the time duration and energy consumption can be found. In this thesis, a three degree of freedom model that describes the dynamics of a small-scale surface ship is estimated. By using optimal control theory and a grey-box model, the parameters are estimated by defining an optimal control problem (OCP). The optimal solution is found by transcribing the problem into a nonlinear program and solving it using an interior point algorithm. The identification method is tested and validated using simulated data as well as using data from real world experiments. The performance of the estimated models is validated using cross validation. In a second track of this thesis, a trajectory is created in two steps. The first is path planning to find a shortest geometric path between two points. In the second step, the path is converted to a trajectory and is optimized to become dynamically feasible. For this purpose, a roadmap is generated from a modified version of the generalized Voronoi diagram. To find an initial path in the roadmap, the A-star algorithm is utilized and to connect start and goal position to the map a few different methods are examined. An initial trajectory is created by mapping a straight-line trajectory to the initial path, thus connecting time, position and velocity. The final trajectory is found by solving a discrete OCP initialized with the initial trajectory. The OCP contains spatial constraints that ensures that the vessel does not collide with static obstacles. The suggested estimation method resulted in models that could be used for trajectory planning to generate a dynamically feasible trajectory for both simulated and real data. The trajectory generated by the trajectory planner resulted in a collision-free trajectory, satisfying the dynamics of the estimated model, such that the trade-off between time duration and energy consumption is well balanced. Future work consists of implementation of a controller to see if the planned trajectory can be followed by the small-scale ship.

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Previous methods for planning clothoid based continuous curvature paths aim at minimizing path length. However, minimal length paths are not always smooth, natural, and drivable. A method of generating clothoid-based trajectories is discussed using constructive polylines. The goal of the motion planner is to create a path for a large car-like vehicle in human driving environments. Thus, the trajectories generated by the motion planner must be smooth, drivable, and natural such that the vehicle can follow the planned path on human roadways. Several examples are shown of trajectories developed for a DARPA Urban Challenge vehicle and a method of testing the motion planner and the vehicle controller is described.

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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.

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

Trajectory planning is important in many applications involving unmanned aerial vehicles, underwater vehicles, spacecraft, and industrial manipulators. It is still a challenging task to rapidly find an optimal trajectory while taking into account dynamic and environmental constraints. In this dissertation, a unified, varying manifold based optimal trajectory planning method inspired by several predator-prey relationships is investigated to tackle this challenging problem. Biological species, such as hoverflies, ants, and bats, have developed many efficient hunting strategies. It is hypothesized that these types of predators only move along paths in a carefully selected manifold based on the prey’s motion in some of their hunting activities. Inspired by these studies, the predator-prey relationships are organized into a unified form and incorporated into the trajectory optimization formulation, which can reduce the computational cost in solving nonlinear constrained optimal trajectory planning problems. Specifically, three motion strategies are studied in this dissertation: motion camouflage, constant absolute target direction, and local pursuit. Necessary conditions based on the speed and obstacle avoidance constraints are derived. Strategies to tune initial guesses are proposed based on these necessary conditions to enhance the convergence rate and reduce the computational cost of the motion camouflage inspired strategy. The following simulations have been conducted to show the advantages of the proposed methods: a supersonic aircraft minimum-time-to-climb problem, a ground robot obstacle avoidance problem, and a micro air vehicle minimum time trajectory problem. The results show that the proposed methods can find the optimal solution with higher success rate and faster iv convergent speed as compared with some other popular methods. Among these three motion strategies, the method based on the local pursuit strategy has a relatively higher success rate when compared to the other two. In addition, the optimal trajectory planning method is embedded into a receding horizon framework with unknown parameters updated in each planning horizon using an Extended Kalman Filter

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