In a pursuit-evasion setup where one group of agents tracks down another adversarial group, vision-based algorithms have been known to make use of techniques such as Linear Dynamic Estimation to determine the probable future location of an evader in a given environment. This helps a pursuer attain an edge over the evader that has conventionally benefited from the uncertainty of the pursuit. The pursuer can utilize this knowledge to enable a faster capture of the evader, as opposed to a pursuer that only knows the evader's current location. Inspired by the function of dorsal anterior cingulate cortex (dACC) neurons in natural predators, the use of a predictive model that is built using an encoder-decoder Long Short-Term Memory (LSTM) Network and can produce a more accurate estimate of the evader's future location is proposed. This enables an even quicker capture of a target when compared to previously used filtering-based methods. The effectiveness of the approach is evaluated by setting up these agents in an environment based in the Modular Open Robots Simulation Engine (MORSE). Cross-domain adaptability of the method, without the explicit need to retrain the prediction model is demonstrated by evaluating it in another domain.

Touch plays a vital role in maintaining human relationships through social andemotional communications. This research proposes a multi-modal haptic display capable of generating vibrotactile and thermal haptic signals individually and simultaneously. The main objective for creating this device is to explore the importance of touch in social communication, which is absent in traditional communication modes like a phone call or a video call. By studying how humans interpret haptically generated messages, this research aims to create a new communication channel for humans. This novel device will be worn on the user's forearm and has a broad scope of applications such as navigation, social interactions, notifications, health care, and education. The research methods include testing patterns in the vibro-thermal modality while noting its realizability and accuracy. Different patterns can be controlled and generated through an Android application connected to the proposed device via Bluetooth. Experimental results indicate that the patterns SINGLE TAP and HOLD/SQUEEZE were easily identifiable and more relatable to social interactions. In contrast, other patterns like UP-DOWN, DOWN-UP, LEFTRIGHT, LEFT-RIGHT, LEFT-DIAGONAL, and RIGHT-DIAGONAL were less identifiable and less relatable to social interactions. Finally, design modifications are required if complex social patterns are needed to be displayed on the forearm.

The majority of drones are extremely simple, their functions include flight and sometimes recording video and audio. While drone technology has continued to improve these functions, particularly flight, additional functions have not been added to mainstream drones. Although these basic functions serve as a good framework for drone designs, it is now time to extend off from this framework. With this Honors Thesis project, we introduce a new function intended to eventually become common to drones. This feature is a grasping mechanism that is capable of perching on branches and carrying loads within the weight limit. This concept stems from the natural behavior of many kinds of insects. It paves the way for drones to further imitate the natural design of flying creatures. Additionally, it serves to advocate for dynamic drone frames, or morphing drone frames, to become more common practice in drone designs.

The research presented in this Honors Thesis provides development in machine learning models which predict future states of a system with unknown dynamics, based on observations of the system. Two case studies are presented for (1) a non-conservative pendulum and (2) a differential game dictating a two-car uncontrolled intersection scenario. In the paper we investigate how learning architectures can be manipulated for problem specific geometry. The result of this research provides that these problem specific models are valuable for accurate learning and predicting the dynamics of physics systems.<br/><br/>In order to properly model the physics of a real pendulum, modifications were made to a prior architecture which was sufficient in modeling an ideal pendulum. The necessary modifications to the previous network [13] were problem specific and not transferrable to all other non-conservative physics scenarios. The modified architecture successfully models real pendulum dynamics. This case study provides a basis for future research in augmenting the symplectic gradient of a Hamiltonian energy function to provide a generalized, non-conservative physics model.<br/><br/>A problem specific architecture was also utilized to create an accurate model for the two-car intersection case. The Costate Network proved to be an improvement from the previously used Value Network [17]. Note that this comparison is applied lightly due to slight implementation differences. The development of the Costate Network provides a basis for using characteristics to decompose functions and create a simplified learning problem.<br/><br/>This paper is successful in creating new opportunities to develop physics models, in which the sample cases should be used as a guide for modeling other real and pseudo physics. Although the focused models in this paper are not generalizable, it is important to note that these cases provide direction for future research.

Bicycle stabilization has become a popular topic because of its complex dynamic behavior and the large body of bicycle modeling research. Riding a bicycle requires accurately performing several tasks, such as balancing and navigation which may be difficult for disabled people. Their problems could be partially reduced by providing steering assistance. For stabilization of these highly maneuverable and efficient machines, many control techniques have been applied – achieving interesting results, but with some limitations which includes strict environmental requirements. This thesis expands on the work of Randlov and Alstrom, using reinforcement learning for bicycle self-stabilization with robotic steering. This thesis applies the deep deterministic policy gradient algorithm, which can handle continuous action spaces which is not possible for Q-learning technique. The research involved algorithm training on virtual environments followed by simulations to assess its results. Furthermore, hardware testing was also conducted on Arizona State University’s RISE lab Smart bicycle platform for testing its self-balancing performance. Detailed analysis of the bicycle trial runs are presented. Validation of testing was done by plotting the real-time states and actions collected during the outdoor testing which included the roll angle of bicycle. Further improvements in regard to model training and hardware testing are also presented.

Daily collaborative tasks like pushing a table or a couch require haptic communication between the people doing the task. To design collaborative motion planning algorithms for such applications, it is important to understand human behavior. Collaborative tasks involve continuous adaptations and intent recognition between the people involved in the task. This thesis explores the coordination between the human-partners through a virtual setup involving continuous visual feedback. The interaction and coordination are modeled as a two-step process: 1) Collecting data for a collaborative couch-pushing task, where both the people doing the task have complete information about the goal but are unaware of each other's cost functions or intentions and 2) processing the emergent behavior from complete information and fitting a model for this behavior to validate a mathematical model of agent-behavior in multi-agent collaborative tasks. The baseline model is updated using different approaches to resemble the trajectories generated by these models to human trajectories. All these models are compared to each other. The action profiles of both the agents and the position and velocity of the manipulated object during a goal-oriented task is recorded and used as expert-demonstrations to fit models resembling human behaviors. Analysis through hypothesis teasing is also performed to identify the difference in behaviors when there are complete information and information asymmetry among agents regarding the goal position.

The term Poly-Limb stems from the rare birth defect syndrome, called Polymelia. Although Poly-Limbs in nature have often been nonfunctional, humans have had the fascination of functional Poly-Limbs. Science fiction has led us to believe that having Poly-Limbs leads to augmented manipulation abilities and higher work efficiency. To bring this to life however, requires a synergistic combination between robot manipulation and wearable robotics. Where traditional robots feature precision and speed in constrained environments, the emerging field of soft robotics feature robots that are inherently compliant, lightweight, and cost effective. These features highlight the applicability of soft robotic systems to design personal, collaborative, and wearable systems such as the Soft Poly-Limb.

This dissertation presents the design and development of three actuator classes, made from various soft materials, such as elastomers and fabrics. These materials are initially studied and characterized, leading to actuators capable of various motion capabilities, like bending, twisting, extending, and contracting. These actuators are modeled and optimized, using computational models, in order to achieve the desired articulation and payload capabilities. Using these soft actuators, modular integrated designs are created for functional tasks that require larger degrees of freedom. This work focuses on the development, modeling, and evaluation of these soft robot prototypes.

In the first steps to understand whether humans have the capability of collaborating with a wearable Soft Poly-Limb, multiple versions of the Soft Poly-Limb are developed for assisting daily living tasks. The system is evaluated not only for performance, but also for safety, customizability, and modularity. Efforts were also made to monitor the position and orientation of the Soft Poly-Limbs components through embedded soft sensors and first steps were taken in developing self-powered compo-nents to bring the system out into the world. This work has pushed the boundaries of developing high powered-to-weight soft manipulators that can interact side-by-side with a human user and builds the foundation upon which researchers can investigate whether the brain can support additional limbs and whether these systems can truly allow users to augment their manipulation capabilities to improve their daily lives.

Bicycles and motorcycles offer maneuverability, energy efficiency and acceleration that four wheeled vehicles cannot offer given similar budget for. Two wheeled vehicles have drastically different dynamics from four wheeled vehicles due to their instability and gyroscopic effect from their wheels.

This thesis focuses on self-stabilization of a motorcycle using an active control momentum gyroscope (CMG) and validation of this multi-degree-of-freedom system’s mathematical model. Physical platform was created to mimic the simulation as accurately as possible and all components used were justified. This process involves derivation of a 3 Degree-of-Freedom (DOF) system’s forward kinematics and its Jacobian matrix, simulation analysis of different controller algorithms, setting the system and subsystem specifications, and real system experimentation and data analysis.

A Jacobian matrix was used to calculate accurately decomposed resultant angular velocities which are used to create the dynamics model of the system torque using the Euler-Lagrange method. This produces a nonlinear second order differential equation that is modeled using MATLAB/Simulink. PID, and cascaded feedback loop are tested in this Simulink model. Cascaded feedback loop shows most promises in the simulation analysis. Therefore, system specifications are calculated according to the data produced by this controller method. The model validation is executed using the Vicon motion capture system which captured the roll angle of the motorcycle. This work contributes to creating a set of procedures for creating a validated dynamic model for a CMG stabilized motorcycle which can be used to create variants of other self-stabilizing motorcycle system.