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Perturbations to the upper limb in aiming tasks act to force individuals to modify their movements using online control processes. Individuals are able to successfully counteract these mechanical and perceptual perturbations to accurately acquire a specific target goal. What is less well understood is how individuals self-initiate a change to their trajectory during obstacle avoidance. A series of two studies were conducted to better understand the effects of a visual perturbation when performing two-dimensional sliding aiming movements during a manual obstacle avoidance task when a second set of obstacles appeared unexpectedly along the preferred, optimal trajectory. On each trial, a planned obstacle appeared at 25%, 50% or 75% of the movement amplitude. On some trials, a second set of obstacles appeared early or late in the movement to force participants to make online corrections or adapt their preferred trajectory to successfully reach the specified target. Results revealed that the mere possibility of the unexpected second obstacles influenced the overall trajectory and movement kinematics (i.e., whether that second obstacles appeared or not). Despite performing the movement in the same amount of time, participants executed a more lateral avoidance trajectory and reached higher accelerations later and further into the movement. We suggest that this pattern of behaviour is indicative of an optimal movement strategy such that the potential for an online correction resulted in individuals planning for the worst-case scenario. The presentation of a case-study for an individual with autism spectrum disorder (ASD) provides insight into potential differences in obstacle avoidance tasks when compared to a matched control. Despite relative differences in execution behaviour, the individual with an ASD successfully completed the task. This provides potential support for the sparing of motor execution processes within this population. Taken together, we suggest that optimal movement strategies may be better defined on a more individual basis. That is, what is optimal for one population might not be optimal for another. / Thesis / Master of Science (MSc) / It is well known that individuals are able to successfully aim to targets in environments that are both predictable and unpredictable. Additionally, these trajectories are successfully modified in the presence of an expected obstacle resulting in a change to the optimal movement to incorporate the location of the obstacle. What is less understood, however, is how individuals respond to the sudden onset of an obstacle along the optimal trajectory. This thesis characterizes these behaviours using a manual obstacle avoidance task wherein obstacles unexpectedly appear to obstruct the preferred movement pathway. The behavioural response to this perturbation is indicative of the performance of more cautious movements, adapted for the worst-case scenario. On average, individuals compromise between the biomechanical and computational demands of the task to execute wide trajectories that do not need to be updated during movement execution, a response that is potentially identified in an autism spectrum population.

Operation of Autonomous Mobile Robots (AMRs) of all forms that include wheeled ground vehicles, quadrupeds and humanoids in dynamically changing GPS denied environments without a-priori maps, exclusively using onboard sensors, is an unsolved problem that has potential to transform the economy, and vastly improve humanity's capabilities with improvements to agriculture, manufacturing, disaster response, military and space exploration. Conventional AMR automation approaches are modularized into perception, motion planning and control which is computationally inefficient, and requires explicit feature extraction and engineering, that inhibits generalization, and deployment at scale. Few works have focused on real-world end-to-end approaches that directly map sensor inputs to control outputs due to the large amount of well curated training data required for supervised Deep Learning (DL) which is time consuming and labor intensive to collect and label, and sample inefficiency and challenges to bridging the simulation to reality gap using Deep Reinforcement Learning (DRL). This dissertation presents a novel method to efficiently train DRL with significantly fewer samples in a constrained racetrack environment at physical limits in simulation, transferred zero-shot to the real-world for robust end-to-end AMR navigation. The representation learned in a compact parameter space with 2 fully connected layers with 64 nodes each is demonstrated to exhibit emergent behavior for Out-of-Distribution (OOD) generalization to navigation in new environments that include unstructured terrain without maps, dynamic obstacle avoidance, and navigation to objects of interest with vision input that encompass low light scenarios with the addition of a night vision camera. The learned policy outperforms conventional navigation algorithms while consuming a fraction of the computation resources, enabling execution on a range of AMR forms with varying embedded computer payloads. / Doctor of Philosophy / Robots with wheels or legs to move around environments improve humanity's capabilities in many applications such as agriculture, manufacturing, and space exploration. Reliable, robust mobile robots have the potential to significantly improve the economy. A key component of mobility is navigation to either explore the surrounding environment, or travel to a goal position or object of interest by avoiding stationary, and dynamic obstacles. This is a complex problem that has no reliable solution, which is one of the main reasons robots are not present everywhere, assisting people in various tasks. Past and current approaches involve first mapping an environment, then planning a collision-free path, and finally executing motor signals to traverse along the path. This has several limitations due to the lack of detailed pre-made maps, and inability to operate in previously unseen, dynamic environments. Furthermore, these modular methods require high computation resources due to the large number of calculations required for each step that prevents high real-time speed, and functionality in small robots with limited weight capacity for onboard computers, that are beneficial for reconnaissance, and exploration tasks. This dissertation presents a novel Artificial Intelligence (AI) method for robot navigation that is more computationally efficient than current approaches, with better performance. The AI model is trained to race in simulation at multiple times real-time speed for cost-effective, accelerated training, and transferred to a physical mobile robot where it retains its training experience, and generalizes to navigation in new environments without maps, with exploratory behavior, and dynamic obstacle avoidance capabilities.

With many robots now being developed for indoor settings, an autonomous mobile robot should be capable of reaching multiple targets within a dense, complex environment while maintaining the optimal path taken and avoiding all obstacles. In this thesis, we propose a global path planning algorithm that uses data created from a Generalized Voronoi Diagram (GVD) to traverse complex environments. The global route is made from the skeleton of the diagram that ensures the avoidance of static obstacles. Once this route is determined, dynamic programming is used to determine the optimal route to reach each target location while safely navigating obstacles in the map. A Dynamic Window Approach (DWA) local path planner is integrated into the algorithm to provide collision-free navigation in case of unexpected or dynamic obstacles that may be encountered during traversal. Our comprehensive simulations and comparative analyses highlight the proposed model’s robustness, demonstrating its ability to efficiently navigate to multiple targets through the shortest routes while adeptly circumventing obstacles. These findings validate the model’s effectiveness, confirming its superior performance in complex multi-target navigation scenarios and its capability to dynamically adapt to unforeseen obstacles, thereby illustrating a significant advancement in the field of autonomous indoor navigation.

Pursuit-evasion problems comprise a set of pursuers that strive to catch oneor several evaders, often in a constrained environment. This thesis proposesand compares heuristic algorithms for pursuit-evasion problems wherein several double integrator agents pursue a single evader in a bounded subset of theEuclidean plane. Different methods for assigning surrounding target points tothe pursuers are tested numerically. In addition, a method which finds the timeoptimal strategy for pursuing a static target in an unconstrained setting is presented, and is then used to pursue the assigned, dynamic, target. Numericalresults show that the time optimal strategy for pursuing a static target translateswell to the dynamic problem.

The thesis focuses on the design of a control strategy for safety-critical remote teleoperation. The main goal is to make the controlled system track the desired velocity specified by a human operator while avoiding obstacles despite communication delays. Different methods adopting Control Barrier Functions (CBFs) and Model Predictive Control (MPC) have been explored and tested. In this combination, CBFs are used to define the safety constraints the system has to respect to avoid obstacles, while MPC provides the framework for filtering the desired input by solving an optimization problem. The resulting input is sent to the remote system, where appropriate low-level velocity controllers translate it into system-specific commands. The main novelty of the thesis is a method to make the CBFs robust against the uncertainties affecting the system’s state due to network delays. Other techniques are investigated to improve the quality of the system information starting from the delayed one and to formulate the optimization problem without knowing the specific dynamic of the controlled system. The results show how the proposed method successfully solves the safetycritical teleoperation problem, making the controlled systems avoid obstacles with different types of network delay. The controller has also been tested in simulation and on a real manipulator, demonstrating its general applicability when reliable low-level velocity controllers are available. / Avhandlingen fokuserar på utformningen av en kontrollstrategi för säkerhetskritisk fjärrstyrd teleoperation. Huvudmålet är att få det kontrollerade systemet att följa den önskade hastigheten som specificeras av en mänsklig operatör samtidigt som hinder undviks trots kommunikationsfördröjningar. Olika metoder som använder Control Barrier Functions (CBFs) och Model Predictive Control har undersökts och testats. I denna kombination används CBFs för att definiera de säkerhetsbegränsningar som systemet måste respektera för att undvika hinder, medan MPC utgör ramverket för filtrering av den önskade indata genom att lösa ett optimeringsproblem. Den resulterande indata skickas till fjärrsystemet, där lämpliga hastighetsregulatorer på låg nivå översätter den till systemspecifika kommandon. Den viktigaste nyheten i avhandlingen är en metod för att göra CBFs robust mot de osäkerheter som påverkar systemets tillstånd på grund av nätverksfördröjningar. Andra tekniker undersöks för att förbättra kvaliteten på systeminformationen med utgångspunkt från den fördröjda informationen och för att formulera optimeringsproblemet utan att känna till det kontrollerade systemets specifika dynamik. Resultaten visar hur den föreslagna metoden framgångsrikt löser det säkerhetskritiska teleoperationsproblemet, vilket gör att de kontrollerade systemen undviker hinder med olika typer av nätverksfördröjningar. Styrningen har också testats i simulering och på en verklig manipulator, vilket visar dess allmänna tillämpbarhet när tillförlitliga lågnivåhastighetsregulatorer finns tillgängliga.

Autonomous coordination among multiple aerial vehicles to ensure a collision free airspace is a critical aspect of today’s airspace. With the rise of Unmanned Aerial Vehicles (UAVs) in the military and commercial sectors, obstacle avoidance in a densely populated airspace is necessary. This thesis investigates finding optimal or near-optimal trajectories in real-time for aircraft in complex airspaces containing a large number of obstacles. The solution for the trajectories is described as a linear program subject to mixed integer constraints, known as a Mixed Integer Linear Program (MILP). The resulting MILP problem is solved in real time using a well-known, public domain MILP solver. In addition, an Exhaustive, Breadth-First Search algorithm was implemented and is used for comparison in terms of execution time and flight path optimality. The Exhaustive Search algorithm is comprised of a multi-branch tree structure that iterates through all possible flight paths from source to target. The MILP solution was implemented in both PC based and embedded system environments. The embedded system environment was implemented on an onboard processor to develop trajectories for each individual aircraft in real time.

The world is slowly moving into increased human-robot interaction where both humans and robots can co-exist in the same domain. For the robot to be able to operate effectively in a man’s designed environment, it becomes necessary to model the robot with human capabilities as humans are seen as more capable. Replicating human becomes a huge challenge due to numerous degrees-of-freedom (DOFs) that human possess resulting into too many variables and nonlinear equations. Other challenges do occur like singularities.   In this thesis, the singularity challenge of a redundant humanoid arm is explored while maintaining a simple 7 DOF serial chain structure. As opposed to the 30 DOF human arm, a simpler 7 DOF humanoid arm is adopted and studied to eliminate the singularity challenges. The singularity problem mainly comes from the elbow and the spherical joints at the shoulder and wrist. A step-by-step review of available inverse kinematics techniques is made with more focus on the iterative Jacobian-based methods. A step-by-step approach is adopted so as to identify the source of singularities while using the iterative Jacobian-based techniques that are able to handle the nonlinearities of the equations.   The Singular Value Filtering (SVF) technique coupled with Selectively Damped Least Squares (SDLS) is employed. Without any restrictions to the stretch of the arm or end-effector pose, the method demonstrates, in conjunction with Euler angle singularity avoidance method, the elimination of singularity problems. This is achieved with no adjustment to kinematic model of the manipulator.

Este trabalho apresenta um novo método de detecção de obstáculos usados para navegação de um barco autônomo. O método desenvolvido é baseado em visão computacional e permite medir a distância e direção do obstáculo à câmera de video. A distância do possível obstáculo à câmera de vídeo, e o vetor de contorno predominante da imagem são os parâmetros usados para identificar os obstáculos. Imagens estereoscópicas adquiridas nas margens da lago do clube Náutico de Araraquara, usando bóias de navegação como obstáculos, foram usadas para extrair as características significantes das experiências. Para validar a experiência, foram utilizadas imagens do Reservatório do Broa (Itirapina, SP). A proposta desenvolvida mostrou ser mais eficiente que o método tradicional usando a teoria de Campos Potenciais. As imagens foram propositadamente tomadas contra o sol, onde o brilho das ondas são erroneamente indicadas como obstáculos pelo método de campos potenciais. Esta proposta filtra as ondas de forma a diminuir sua interferência no diagnóstico. / This work presents the results of new obstacle detection methods used for an autonomous boat navigation. The developed method is based on computer vision and allows to measure the distance and direction of the obstacle to the boat. The distance of the possible obstacle to the camera, and the obstacle outline predominant vector are the parameters used to identify the obstacles. Stereo images acquired from the margins of the Nautical Araraquara lake, using navigation buoys as obstacles, were used to extract the meaningful characteristics of the experiments. To validate the experiment, images from the Broa Reservoir (Itirapina, SP) where used. The developed proposal showed to be more efficient than the traditional method using the potential fields theory. The images were taken willfully against the sun, where the brightness of the waves are erroneously identified as obstacles by the method of potential fields. This method filters the waves so as to reduce its interference in the diagnosis.