Tactile sensors are the last frontier to robots that can handle everyday objects and interact with humans through contact. Robots are expected to recognize the properties of objects in order to handle them safely and efficiently in a variety of applications, such as health- and elder care, manufacturing, or high-risk environments. To be effective, such sensors have to sense the geometry of touched surfaces and objects, as well as any other relevant information for their tasks, such as forces, vibrations, and temperature, that allow them to safely and securely interact within an environment. Given the capability of humans to easily capture and interpret tactile data, one promising direction in order to produce enhanced robotic tactile sensors is to explore and imitate human tactile sensing capabilities. In this context, this thesis presents the design and hardware implementation issues related to the construction of a novel multimodal bio-inspired skin module for dynamic and static tactile surface characterization. Drawing inspiration from the type, functionality, and organization of cutaneous tactile elements in the human skin, the proposed solution determines the placement of two shallow sensors (a tactile array and a nine DOF magnetic, angular rate, and gravity system) and a deep pressure sensor within a flexible compliant structure, similar to the receptive field of the Pacinian mechanoreceptor. The benefit of using a compliant structure is tri-folded. First, the module has the capability of performing touch tasks on unknown surfaces, tackling the tactile inversion problem. The compliant structure guides deforming forces from its surface to the deep pressure sensor, while keeping track of the deformation of the structure using advantageously placed shallow sensors. Second, the module’s compliant structure and its embedded sensor placement provide useful data to overcome the problem of estimating non-normal forces, a significant challenge for the current generation of tactile sensing technologies. This capability allows accommodating sensing modalities essential for acquiring tactile images and classifying surfaces by vibrations and accelerations. Third, the compliant structure of the module also contributes to the relaxation of orientation constraints of end-effectors or other robotic parts carrying the module to contact surfaces of unknown objects. Issues related to the module calibration, its sensing capabilities and possible real-world applications are also presented.

The combination or integration of locomotion modes, is analyzed through the design, development, and verification of a miniature integrated jumping and gliding robot, the MultiMo-Bat, which is inspired by the locomotion strategies of vampire bats, locusts, and pelicans. This robot has a mass of between 100 and 162 grams and exhibits high jumping and gliding performance, reaching heights of over 4.5 meters, to overcome obstacles in the environment. Integration results in a smaller, lighter robot with high cooperation between the modes. This thesis presents a previously unstudied robot design concept and highlights the understudied evolutionary concept within organism mobility of integration of locomotion modes. High performance locomotion modes also require high energy density actuators. To this end, a design methodology is developed for tailoring magnetic springs to the characteristics of shape memory alloy-actuated mechanisms, which allow the MultiMo-Bat to reach jumping heights of 3.5 m with active wing deployment and full controller. Through a combinations of permanent magnets, a magnetic spring can be customized to desired characteristics; theoretically any welldefined function of force vs. displacement can be created. The methodology is not limited to SMA but can be adapted to any smart actuator, joint, or situation which requires a fixed complex force-displacement relationship with extension other interactions and magnetic field design. Robotic locomotion is also much more idealized than that of their biological counter parts. This thesis serves to highlight just how non-ideal, yet robust, biological locomotion can inspire concepts for enhancing the robustness of robot locomotion. We studied the desert locust (Schistocerca gregaria), which is adapted for jumping at the extreme limits of its surface friction, as evident by its morphological adaptations for not only jumping, but slipping. Analysis of both foot morphology and jumping behavior are used to understand how the feet interact with different surfaces, including hydrophobic glass, hydrophilic glass, wood, sandstone, and mesh. The results demonstrate a complex interplay of embodied mechanical intelligence, allowing the foot to interact and adapt passively to different surfaces without burdening the organism with additional tasks. The key morphological and dynamical features are extracted to create a concept for developing multi-Surface Locust Inspired Passively-adaptable (SLIP) feet. A simple interpretation of the concepts are then used to construct a SLIP foot for the MultiMo-Bat. These feet allow the MultiMo-Bat to reach jumping heights of well over 4 m, greater than any other electrically powered robot, and this is achieved on a 45 degree angled surface while slipping. The SLIP foot concept can be directly applied to a wide range of robot size scales, thus enhancing their dynamic terrestrial locomotion on variable surfaces.

Biological systems offer novel and efficient solutions to many engineering applications, including marine propulsion. It is of interest to determine how fish interact with the water around them, and how best to utilize the potential their methods offer. A stingray-like fin was chosen for analysis due to the maneuverability and versatility of stingrays. The stingray fin was modeled in 2D as a sinusoidal wave with an amplitude increasing from zero at the leading edge to a maximum at the trailing edge. Using this model, a parametric study was performed to examine the effects of the fin on surrounding water in computational fluid dynamics (CFD) simulations. The results were analyzed both qualitatively, in terms of the pressure contours on the fin and vorticity in the trailing wake, and quantitatively, in terms of the resultant forces and the mechanical power requirements to actuate the desired fin motion. The average thrust was shown to depend primarily on the relationship between the swimming speed and the frequency and wavelength (which both are directly proportional to the wavespeed of the fin), although amplitude can be used to augment thrust production as well. However, acceleration was shown to significantly correlate with a large variation in lift and moment, as well as with greater power losses. Using results from the parametric study, the potential for power regeneration was also examined. Relationships between frequency, velocity, drag, and power input were determined using nonlinear regression that explained more than 99.8% of the data. The actuator for a fin was modeled as a single DC motor-shaft system, allowing the combination of the energetic effects of the motor with the fin-fluid system. When combined, even a non-ideal fin model was able to regenerate more power at a given flow speed than was required to swim at the same speed. Even in a more realistic setting, this high proportion of regenerative power suggests that regeneration and energy harvesting could be both feasible and useful in a mission setting. / Master of Science / Animals interact with the world much differently than engineered systems, and can offer new and efficient ways to solve engineering problems, including underwater vehicles. To learn how to move an underwater vehicle in an environmentally conscious way, it is useful to study how a fish’s movements affect the manner in which it moves through the water. Through careful study, the principles involved can be implemented for an efficient, low-disturbance underwater vehicle. The particular fish chosen for in-depth study was the stingray, due to its maneuverability and ability to travel close to the seafloor without disturbing the sediment and creatures around it. In this work, computational analysis was performed on a model of a single stingray fin to determine how the motion of the fin affects the water around it, and how the water affects the fin in turn. The results were analyzed both in terms of the wake behind the fin and in terms of how much power was required to make the fin move in a particular way. The speed of the fin motion was found to have the strongest effect in controlling swimming speed, although the lateral motion of the fin also helped with accelerating faster. Additionally, the potential for a robotic stingray fin to harness power from the water around it was examined. Based on results from simulations of the fin, a mathematical model was formulated to relate energy harvesting with the flow speed past the fin. This model was used to determine how worthwhile it was to use energy harvesting. Analysis of the model showed that harvesting energy from the water was quite efficient, and would likely be a worthwhile investment for an exploration mission.

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Swarm robotics is the study of developing and controlling large groups of robots. Collectives of robots possess advantages over single robots such as being robust to mission failures due to single-robot errors. Experimental research in swarm robotics is currently limited by swarm robotic technology. Current swarm robotic systems are either small groups of sophisticated robots or large groups of simple robots due to manufacturing overhead, functionality-cost dependencies, and their need to avoid collisions, amongst others. It is therefore useful to develop a swarm robotic system that is easy to manufacture, that utilises its sensors beyond standard usage, and that allows for physical interactions. In this work, I introduce a new type of low-friction locomotion and show its first implementation in the HoverBot system. The HoverBot system consists of an air-levitation and magnet table, and a HoverBot agent. HoverBots are levitating circuit boards which are equipped with an array of planar coils and a Hall-effect sensor. HoverBot uses its coils to pull itself towards magnetic anchors that are embedded into a levitation table. These robots consist of a Printed Circuit Board (PCB), surface mount components, and a battery. HoverBots are easily manufacturable, robots can be ordered populated; the assembly consists of plugging in a battery to a robot. I demonstrate how HoverBot's low-cost hardware can be used beyond its standard functionality. HoverBot's magnetic field readouts from its Hall-effect sensor can be associated with successful movement, robot rotation and collision measurands. I build a time series classifier based on these magnetic field readouts, I modify and apply signal processing techniques to enable the online classification of the time-variant magnetic field measurements on HoverBot's low-cost microcontroller. This method allows HoverBot to detect rotations, successful movements, and collisions by utilising readouts from its single Hall-effect sensor. I discuss how this classification method could be applied to other sensors and demonstrate how HoverBots can utilise their classifier to create an occupancy grid map. HoverBots use their multi-functional sensing capabilities to determine whether they moved successfully or collided with a static object to map their environment. HoverBots execute an "explore-and-return-to-nest" strategy to deal with their sensor and locomotion noise. Each robot is assigned to a nest (landmark); robots leave their nests, move n steps, return and share their observations. Over time, a group of four HoverBots collectively builds a probabilistic belief over its environment. In summary, I build manufacturable swarm robots that detect collisions through a time series classifier and map their environment by colliding with their surroundings. My work on swarm robotic technology pushes swarm robotics research towards studies on collision-dependent behaviours, a research niche that has been barely studied. Collision events occur more often in dense areas and/or large groups, circumstances that swarm robots experience. Large groups of robots with collision-dependent behaviours could become a research tool to help invent and test novel distributed algorithms, to understand the dependencies between local to global (emergent) behaviours and more generally the science of complex systems. Such studies could become tremendously useful for the execution of large-scale swarm applications such as the search and rescue of survivors after a natural disaster.