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.
The researchers build a drone with a grasping mechanism to wrap around branches to perch. The design process and methodology are discussed along with the software and hardware configuration. The researchers explain the influences on the design and the possibilities for what it could inspire.
The purpose of this project is to improve upon the passive ankle foot orthosis originally designed in the ASU’s Robotics and Intelligent Systems Laboratory (RISE Lab). This device utilizes springs positioned parallel to the user’s Achilles tendon which store energy to be released during the push off phase of the user’s gait cycle. Goals of the project are to improve the speed and reliability of the ratchet and pawl mechanism, design the device to fit a wider range of shoe sizes, and reduce the overall mass and size of the device. The resulting system is semi-passive and only utilizes a single solenoid to unlock the ratcheting mechanism when the spring’s potential force is required. The device created also utilizes constant force springs rather than traditional linear springs which allows for a more predictable level of force. A healthy user tested the device on a treadmill and surface electromyography (sEMG) sensors were placed on the user’s plantar flexor muscles to monitor potential reductions in muscular activity resulting from the assistance provided by the AFO device. The data demonstrates the robotic shoe was able to assist during the heel-off stage and reduced activation in the plantar flexor muscles was evident from the EMG data collected. As this is an ongoing research project, this thesis will also recommend possible design upgrades and changes to be made to the device in the future. These upgrades include utilizing a carbon fiber or lightweight plastic frame such as many of the traditional ankle foot-orthosis sold today and introducing a system to regulate the amount of spring force applied as a function of the force required at specific times of the heel off gait phase.
