Scaling down robots is a challenge that requires innovative design approaches. Current locomotion robots are often dependent on complex actuation schemes or multiple actuators, which limits their miniaturization. Integrating control of a mechanism into the body instead of using complex electrical control architectures can contribute greatly to the scalability of locomotion robots. This research proposes a compliant mechanism that converts a rectilinear input into a closed-loop End Effector (EE) trajectory. Six of these mechanisms are connected through a compliant transmission to form a walking hexapod with a tripod gait. The result is a robot where a single linear actuator creates a walking gait with zero internal friction. This research hereby paves the way towards scalable locomotion robots with a simple control architecture.

Automated driving systems are expected to be revolutionary technologies that will reconstruct mobility by improving safety and efficiency. Among the components of automated driving systems, motion planning plays a critical role as it determines how the vehicle reaches its target location from a given origin. The current challenges of motion planning lie in real-time performance and passenger comfort, which is also the main objective of motion planning algorithms. These challenges often occur simultaneously but are treated separately. Neither of these challenges could be solved at a global level of motion planning, which is mainly concerned with route selection and travel time minimization, and could be computed in advance. Consequently, this thesis primarily focuses on local motion planning related to the maneuvering of a vehicle, ensuring real-time performance and passenger comfort.
To address these two challenges, an extended tentacle-based motion planning algorithm is developed. This is an interpolation curve-based algorithm that could work in real-time because there are no optimizers or learning processes that cost a lot of computational resources in the algorithm. The most important factor that affects passenger comfort in a ride is jerk, which is also known as the time derivative of acceleration. The proposed algorithm manages to control the jerk in both lateral and longitudinal directions, thus ensuring ride comfort. Begin with the current state of the vehicle, including velocity, attitude, and steering angle, a series of geometry curves called tentacles is generated and evaluated. The maximum lateral jerk is limited during tentacle generation to avoid excessive impact on passengers during maneuvering.To address these two challenges, an extended tentacle-based motion planning algorithm is developed. This is an interpolation curve-based algorithm that could work in real-time because there are no optimizers or learning processes that cost a lot of computational resources in the algorithm. The most important factor that affects passenger comfort in a ride is jerk, which is also known as the time derivative of acceleration. The proposed algorithm manages to control the jerk in both lateral and longitudinal directions, thus ensuring ride comfort. Begin with the current state of the vehicle, including velocity, attitude, and steering angle, a series of geometry curves called tentacles is generated and evaluated. The maximum lateral jerk is limited during tentacle generation to avoid excessive impact on passengers during maneuvering.
In most motion planning algorithms, geometry path planning and speed planning are treated separately. However, in the proposed planning algorithm, the speed profile is generated based on the selected best tentacle, thus making the speed profile more rational and adaptable to current maneuvers. In addition, the target speed of the speed profile is decided based on road curvature and traffic conditions, ensuring safety and avoiding wasting time on some low-speed traffic participants. More importantly, the speed profile limits the maximum longitudinal jerk, thus making jerk limited in all directions and ensuring passenger comfort.
To evaluate the performance of the proposed motion planning algorithm, simulations using a high-fidelity vehicle model through IPG CarMaker and MATLAB/Simulink are implemented under various conditions. Because this report does not focus on the design of the path-following controller, the vehicle directly uses the output of the planner as control input during simulations. Even so, the algorithm still manages to complete static and dynamic obstacle avoidance as well as adaptive following and overtaking maneuvers at various speed ranges and road conditions. A virtual map of part of the campus of TU Delft is also constructed using Carmaker and used for validation of the proposed algorithm under urban traffic conditions. The proposed algorithm works effectively in complex environments and can operate in real time at a frequency of $20$ $Hz$ while constraining the total jerk.
This research illustrated the capability of the developed tentacle-based motion planning algorithm to ensure passenger comfort and safety under various traffic conditions. Although primarily serving as a concept emphasizing feasibility through simulations rather than immediate on-road verification, the proposed algorithm establishes a foundation for future real-vehicle implementation, thus contributing towards resolving normal driving conditions essential for achieving fully automated driving.

This thesis presents the development of a novel Variable Stiffness Gripper using bistable metamaterials (B-VSG) to address limitations in current actuator technologies. The primary objective was to design a lightweight VSG that can switch between different stiffness states without requiring energy to maintain them.

The design process involved optimizing metamaterials for torsional stiffness and bistability. Finite Element Analysis (FEA) and physical experiments were conducted to predict and validate the mechanical behaviour, dynamic behaviour, and stability of the gripper. The results demonstrated a significant increase in torsional stiffness between bistable states, achieving a stiffness increase of 40.2% with a change of 0.173 Nm/rad. The B-VSG showed reduced complexity and lightweight characteristics compared to existing technologies, though further optimization is needed, particularly in the actuation mechanisms and unit cell design.

In conclusion, this research successfully developed a novel, lightweight VSG using bistable metamaterials, proving the feasibility of integrating these materials into robotic actuators. Future work should focus on further improving the metamaterial, enhanced control for stiffness adjustment, and long-term performance tests in real-world conditions.

In recent years, the deployment of ground-based mobile robots has gained more and more interest in various domains. In contrast to other types of mobile robots, legged robots can traverse irregular terrains, climb stairs, and step over obstacles. However, these unique properties intensify the energy demand and require highly advanced perception methods, actuator designs, and motion control algorithms. The most significant challenges in legged robotics lie in robustness, energy efficiency, and agility.

In recent years, the integration of an articulated torso or active spine, inspired by the body motion of high-performance mammals like the cheetah, has shown promising results. Various studies observed higher maximum velocities and lower energy consumption compared to a rigid torso. However, in these studies, the compliant systems were typically controlled using basic control strategies. In recent years, the development of highly dynamic model-based motion optimization strategies has greatly enhanced the overall performance of various legged robots. Therefore, a model-based motion optimization scheme is developed specifically for articulated quadruped robots. This scheme fully exploits the additional degrees of freedom of the torso to enhance the dynamic performance of the legged robot further.