Humanoid robots have captured the imagination of authors and researchers for years. Development of the bipedal walking necessary for humanoid robots began in earnest in the late 60's with research in Europe and Japan. The the unique challenges of a bipedal locomotion led to initial robots keeping power, computation, and perception systems off-board while developing the actuators and algorithms to enable locomotion. As technology has improved humanoid and exoskeleton systems have finally incorporated all the various subsytems to build a full independent system. Many of the groups building these platforms have developed them based on knowledge acquired through decades of prior development. For groups developing new humanoid systems little guidance on the pitfalls and challenges of humanoid design exist. Virginia Tech's robot ESCHER, developed for the DARPA Robotics Challenge (DRC), is the 4th generation full sized humanoid developed at the University. This paper attempts to quantify the design trades and techniques used to predict performance of ESCHER and how these trades specifically affected the design of the upper body. The development of ESCHER became necessary when it became obvious that the original design assumptions behind the previous robot THOR left it incapable of completing the DRC course and the necessary upgrades would require an almost complete redesign. Using the methods described in this paper ESCHER was designed manufactured and began initial testing within 10 months. One and a half months later ESCHER became the first humanoid to walk the 60 m course at the DRC. The methods described in this paper provide guidance on the decision making process behind the various subsystems on ESCHER. In addition the methodology of developing a dynamic simulation to predict performance before development of the platform helped provide design requirements that ensured the performance of the system. By setting design requirements ESCHER met or exceeded the goals of the team and remains a valuable development platform that can provide utility well beyond the DRC. / Master of Science / Long a product of science fiction, humanoid robots have been in development by researchers since the late 60’s but still haven’t reached their promised potential. The DARPA Robotics Challenge (DRC) was an inducement prize contest held in 2013 and 2015 to help accelerate the use of robotic systems for disaster response scenarios. Team VALOR Virginia Tech’s entry into the competition was required to build a completely new humanoid in 10 months, resulting in the Electric Series Compliant Humanoid for Emergency Response or ESCHER. The rapid development of ESCHER was made possible by system engineering and a analysis to ensure ESCHER could meet all the competition goals. At The DRC ESCHER became the first humanoid to walk the 60m course. Humanoid research labs have used intuition and knowledge gained through decades of experience to design their systems. This paper discusses techniques used to design the upper body of ESCHER as well as modeling and simulation to predict performance when designing a humanoid. By using trade analysis and modeling researcher’s new to the field can design to a predicted performance point with confidence in a chiving accurate results.

Intrusion systems have been the subject of considerable research during the past 33 years, since the original work of Anderson. Much has been published attempting to improve their performance using advanced data processing techniques including neural nets, statistical pattern recognition and genetic algorithms. Whilst some significant improvements have been achieved they are often the result of assumptions that are difficult to justify and comparing performance between different research groups is difficult. The thesis develops a new approach to defining performance focussed on comparing intrusion systems and technologies. A new taxonomy is proposed in which the type of output and the data scale over which an intrusion system operates is used for classification. The inconsistencies and inadequacies of existing definitions of detection are examined and five new intrusion levels are proposed from analogy with other detection-based technologies. These levels are known as detection, recognition, identification, confirmation and prosecution, each representing an increase in the information output from, and functionality of, the intrusion system. These levels are contrasted over four physical data scales, from application/host through to enterprise networks, introducing and developing the concept of a footprint as a pictorial representation of the scope of an intrusion system. An intrusion is now defined as “an activity that leads to the violation of the security policy of a computer system”. Five different intrusion technologies are illustrated using the footprint with current challenges also shown to stimulate further research. Integrity in the presence of mixed trust data streams at the highest intrusion level is identified as particularly challenging. Two metrics new to intrusion systems are defined to quantify performance and further aid comparison. Sensitivity is introduced to define basic detectability of an attack in terms of a single parameter, rather than the usual four currently in use. Selectivity is used to describe the ability of an intrusion system to discriminate between attack types. These metrics are quantified experimentally for network intrusion using the DARPA 1999 dataset and SNORT. Only nine of the 58 attack types present were detected with sensitivities in excess of 12dB indicating that detection performance of the attack types present in this dataset remains a challenge. The measured selectivity was also poor indicting that only three of the attack types could be confidently distinguished. The highest value of selectivity was 3.52, significantly lower than the theoretical limit of 5.83 for the evaluated system. Options for improving selectivity and sensitivity through additional measurements are examined.

Early human language technology systems were designed in a monolithic fashion. As these systems became more complex, this design became untenable. In its place, the concept of distributed processing evolved wherein the monolithic structure was decomposed into a number of functional components that could interact through a common protocol. This distributed framework was readily accepted by the research community and has been the cornerstone for the advancement in cutting edge human language technology prototype systems.The Defense Advanced Research Program Agency (DARPA) Communicator program has been highly successful in implementing this approach. The program has fueled the design and development of impressive human language technology applications. Its distributed framework has offered numerous benefits to the research community, including reduced prototype development time, sharing of components across sites, and provision of a standard evaluation platform. It has also enabled development of client-server applications with complex inter-process communication between modules. However, this latter feature, though beneficial, introduces complexities which reduce overall system robustness to failure. In addition, the ability to handle multiple users and multiple applications from a common interface is not innately supported. This thesis describes the enhancements to the original Communicator architecture that address robustness issues and provide a multiple multi-user application environment by enabling automated server startup, error detection and correction. Extensive experimentation and analysis were performed to measure improvements in robustness due to the enhancements to the DARPA architecture. A 7.2% improvement in robustness was achieved on the address querying task, which is the most complex task in the human language technology system.

This thesis describes the initial development of the Texas A&M Autonomous Ground Vehicle test platform and waypoint following software, including the associated controller design. The original goal of the team responsible for the development of the vehicle was to enter the DARPA Grand Challenge in October 2005. A 2004 Ford F150 4x4 pickup was chosen as the vehicle platform and was modified with a 6” suspension lift and 35” tires, as well as a commercial drive-by-wire system. The waypoint following software, the design of which is described in this thesis, is written in C and successfully drives the vehicle on a course defined by GPS waypoints at speeds up to 50 mph. It uses various heuristics to determine desired speeds and headings and uses control feedback to guide the vehicle towards these desired states. A vehicle dynamics simulator was also developed for software testing. Ultimately, this software will accept commands from advanced obstacle avoidance software so that the vehicle can navigate in true off-road terrain.

The malicious intrusions to computer systems result in the loss of money, time and hidden information which require deployment of intrusion detection systems. Existing intrusion detection methods analyze packet payload to search for certain strings and to match them with a rule database which takes a long time in large size packets. Because of buffer limits, packets may be dropped or the system may stop working due to high CPU load. In this thesis, we investigate signature based intrusion detection with signatures that only depend on the packet header information without payload inspection. To this end, we analyze the well-known DARPA 1998 dataset to manually extract such signatures and construct a new rule set to detect the intrusions. We implement our rule set in a popular intrusion detection software tool, Snort. Furthermore we enhance our rule set with the existing rules of Snort which do not depend on payload inspection. We test our rule set on DARPA data set as well as a new data set that we collect using attack generator tools. Our results show around 30% decrease in detection time with a tolerable decrease in the detection rate. We believe that our method can be used as a complementary component to speed up intrusion detection systems.

Previous methods for planning clothoid based continuous curvature paths aim at minimizing path length. However, minimal length paths are not always smooth, natural, and drivable. A method of generating clothoid-based trajectories is discussed using constructive polylines. The goal of the motion planner is to create a path for a large car-like vehicle in human driving environments. Thus, the trajectories generated by the motion planner must be smooth, drivable, and natural such that the vehicle can follow the planned path on human roadways. Several examples are shown of trajectories developed for a DARPA Urban Challenge vehicle and a method of testing the motion planner and the vehicle controller is described.

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Research in mobile robotics, unmanned systems, and autonomous man-portable vehicles has grown rapidly over the last decade. This push has taken the problems of robot cognition and behavioral control out of the lab and into the field. Two good examples of this are the DARPA Urban Challenge autonomous vehicle race and the RoboCup robot soccer competition. In these challenges, a mobile robot must be capable of completing complex, sophisticated tasks in a dynamic, partially observable and unpredictable environment. Such conditions necessitate a behavioral programming approach capable of performing high-level action selection in the presence of multiple goals of dynamically changing importance, and noisy, incomplete perception data. In this thesis, an approach to behavioral programming is presented that provides the designer with an intuitive method for building contextual intelligence while preserving the qualities of emergent behavior present in traditional behavior-based programming. This is done by using a modified hierarchical state machine for behavior arbitration in sequence with a command fusion mechanism for cooperative and competitive control. The presented approach is analyzed with respect to portability across platforms, missions, and functional requirements. Specifically, two landmark case-studies, the DARPA Urban Challenge and the International RoboCup Competition are examined. / Master of Science

Cognitive Radio technologies have been touted to be instrumental in solving resource-allocation problems in resource-constrained radio environments. The adaptive computational intelligence of these radios facilitates the dynamic allocation of network resources--particularly, the spectrum, a scarce physical asset. In addition to consumer-driven innovation that is governing the wireless communication ecosystem, its associated infrastructure is being increasingly viewed by governments around the world as critical national security interests--the US Military instituted the DARPA Spectrum Collaboration Challenge which requires competitors to design intelligent radios that leverage optimization, A.I., and game-theoretic strategies in order to efficiently access the RF spectrum in an environment wherein every other competitor is vying for the same limited resources. In this work, we detail the design of our radio, i.e., the design choices made in each layer of the network protocol stack, strategies rigorously derived from convex optimization, the collaboration API, and heuristics tailor-made to tackle the unique scenarios emulated in this DARPA Grand Challenge. We present performance evaluations of key components of our radio in a variety of military and disaster-relief deployment scenarios that mimic similar real-world situations. Furthermore, specifically focusing on channel access in the MAC, we formulate the spectrum sensing and access problem as a POMDP; derive an optimal policy using approximate value iteration methods; prove that our strategy outperforms the state-of-the-art, and facilitates means to control the trade-off between secondary network throughput and incumbent interference; and evaluate this policy on an ad-hoc distributed wireless platform constituting ESP32 radios, in order to study its implementation feasibility.