Browsing by Author "Shield, Stacey"

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    A contact-implicit direct trajectory optimization scheme for the study of legged maneuverability
    (2022) Shield, Stacey; Patel, Amir
    For legged robots to move safely in unpredictable environments, they need to be manoeuvrable, but transient motions such as acceleration, deceleration and turning have been the subject of little research compared to constant-speed gait. They are difficult to study for two reasons: firstly, the way they are executed is highly sensitive to factors such as morphology and traction, and secondly, they can potentially be dangerous, especially when executed rapidly, or from high speeds. These challenges make it an ideal topic for study by simulation, as this allows all variables to be precisely controlled, and puts no human, animal or robotic subjects at risk. Trajectory optimization is a promising method for simulating these manoeuvres, because it allows complete motion trajectories to be generated when neither the input actuation nor the output motion is known. Furthermore, it produces solutions that optimize a given objective, such as minimizing the distance required to stop, or the effort exerted by the actuators throughout the motion. It has consequently become a popular technique for high-level motion planning in robotics, and for studying locomotion in biomechanics. In this dissertation, we present a novel approach to studying motion with trajectory optimization, by viewing it more as “trajectory generation” – a means of generating large quantities of synthetic data that can illuminate the differences between successful and unsuccessful motion strategies when studied in aggregate. One distinctive feature of this approach is the focus on whole-body models, which capture the specific morphology of the subject, rather than the highly-simplified “template” models that are typically used. Another is the use of “contact-implicit” methods, which allow an appropriate footfall sequence to be discovered, rather than requiring that it be defined upfront. Although contact-implicit methods are not novel, they are not widely-used, as they are computationally demanding, and unnecessary when studying comparatively-predictable constant speed locomotion. The second section of this dissertation describes innovations in the formulation of these trajectory optimization problems as nonlinear programming problems (NLPs). This “direct” approach allows these problems to be solved by general-purpose, open-source algorithms, making it accessible to scientists without the specialized applied mathematics knowledge required to solve NLPs. The design of the NLP has a significant impact on the accuracy of the result, the quality of the solution (with respect to the final value of the objective function), and the time required to solve the problem
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    Optimizing dynamic locomotion in Baleka II: from simulation to real-world running
    (2025) Martin, Zubair; Shield, Stacey; Patel, Amir
    In the field of legged locomotion, agility is a critical area of research in robotics due to its potential to enable versatile movement for various applications, including search and rescue missions. However, bipedal robots face significant challenges in achieving rapid movements, such as maintaining stability and agility. This dissertation presents the development of Baleka II, a bipedal robot designed to overcome these challenges by achieving rapid legged locomotion through open-loop control. Building upon its predecessor, this research seeks to evaluate the robot's capacity to perform agile tasks by incorporating trajectory optimization algorithms and conducting real-world experiments. The study is structured around four primary objectives: improving the embedded system configuration, generating control trajectories using trajectory optimization, validating these solutions through simulations, and implementing them on the physical robot. The key locomotive tasks investigated include acceleration, deceleration (gait termination), and steady-state walking/running. The control system was implemented using the Speedgoat Real-Time Target Machine, integrating Simulink Real-Time and Simscape Multibody for real-time execution. Trajectory optimization was accomplished using Pyomo and Interior Point Optimizer (IPOPT), producing solutions for walking (0.5 m/s), walk-to-run transitions (1.5 m/s), and maximum forward speeds (4.0 m/s). Simulations were used to verify these solutions, taking into account the robot's physical constraints. Despite the use of open-loop control, stability was maintained through proportional-derivative (PD) controllers for each motor. The key findings of this research indicate that as the robot's speed increased, so did the actuation effort, peak torque, and GRFs, leading to velocity discrepancies and high deceleration upon ground contact. Nevertheless, Baleka II was able to accelerate into 3.2 m/s steady-state gait and decelerate in a stable manner, demonstrating competitive acceleration and deceleration rates relative to other bipedal robots. These results offer valuable insights into the use of open-loop optimal control for achieving rapid transitions in bipedal robots, with potential applications in search and rescue, industrial assistance, and entertainment. Future work will focus on enhancing the robot's deceleration capabilities, integrating additional sensors, exploring advanced control techniques, and testing the robot on uneven terrain. These efforts will further expand the potential of Baleka II for real-world applications.