<p>In this work new artificial learning and innate control mechanisms are proposed for application</p><p>in autonomous behavioral systems for mobile robots. An autonomous system (for mobile robots)</p><p>existent in the literature is enhanced with respect to its capacity of exploring the environment and</p><p>avoiding risky configurations (that lead to collisions with obstacles even after learning). The</p><p>particular autonomous system is based on modular hierarchical neural networks. Initially,the</p><p>autonomous system does not have any knowledge suitable for exploring the environment (and</p><p>capture targets œ foraging). After a period of learning,the system generates efficientobstacle</p><p>avoid ance and target seeking behaviors. Two particular deficiencies of the forme rautonomous</p><p>system (tendency to generate unsuitable cyclic trajectories and ineffectiveness in risky</p><p>configurations) are discussed and the new learning and controltechniques (applied to the</p><p>autonomous system) are verified through simulations. It is shown the effectiveness of the</p><p>proposals: theautonomous system is able to detect unsuitable behaviors (cyclic trajectories) and</p><p>decrease their probability of appearance in the future and the number of collisions in risky</p><p>situations is significantly decreased. Experiments also consider maze environments (with targets</p><p>distant from each other) and dynamic environments (with moving objects).</p>

In this work new artificial learning and innate control mechanisms are proposed for application in autonomous behavioral systems for mobile robots. An autonomous system (for mobile robots) existent in the literature is enhanced with respect to its capacity of exploring the environment and avoiding risky configurations (that lead to collisions with obstacles even after learning). The particular autonomous system is based on modular hierarchical neural networks. Initially,the autonomous system does not have any knowledge suitable for exploring the environment (and capture targets œ foraging). After a period of learning,the system generates efficientobstacle avoid ance and target seeking behaviors. Two particular deficiencies of the forme rautonomous system (tendency to generate unsuitable cyclic trajectories and ineffectiveness in risky configurations) are discussed and the new learning and controltechniques (applied to the autonomous system) are verified through simulations. It is shown the effectiveness of the proposals: theautonomous system is able to detect unsuitable behaviors (cyclic trajectories) and decrease their probability of appearance in the future and the number of collisions in risky situations is significantly decreased. Experiments also consider maze environments (with targets distant from each other) and dynamic environments (with moving objects).

An autonomous system has been widely applied for various civil/military research because of its versatile capability of understanding high-level intent and direction of a surrounding environment and targets of interest. However, as autonomous systems can be out of control to cause serious loss, injury, or death in the worst case, the verification of their functionalities has got increasing attention. For that reason, this study is focused on the verification of a heterogeneous multi-agent autonomous system. The thesis first presents an overview of formal methods, especially focuses on model checking for autonomous systems verification. Then, six case studies are presented to verify the decision making behaviours of multi-agent system using two basic scenarios: surveillance and convoy. The initial system considered in the surveillance mission consists of a ground control system and a micro aerial vehicle. Their decision-making behaviours are represented by means of Kripke model and computational tree logic is used to specify the properties of this system. For automatic verification, MCMAS (Model Checker for Multi-Agent Systems) is adopted due to its novel capability to accommodate the multi-agent system. After that, the initial system is extended to include a substitute micro aerial vehicle. These initial case studies are then further extended based on SEAS DTC exemplar 2 dealing with behaviours of convoy protection. This case study includes now a ground control system, an unmanned aerial vehicle, and an unmanned ground vehicle. The MCMAS successfully verifies the targeting behaviours of the team-level unmanned systems. Reversely, these verification results help retrospectively improve the design of decision-making algorithms by considering additional agents and behaviours during four steps of scenario modification. Consequently, the last scenario deals with the system composed of a ground control system, two unmanned aerial vehicles, and four unmanned ground vehicles with fault-tolerant and communications relay capabilities. In conclusion, this study demonstrates the feasibility of model checking algorithms as a verification tool of a multi-agent system in an initial design stage. Moreover, this research can be an important first step of the certification of multi-agent autonomous systems for the domains of robotics, aerospace and aeronautics.

Telemanipulators allow a user to interact with a potentially dangerous environment remotely. Deploying a robot arm from a UAV would allow an operator to reach farther and quicker than he or she would with a ground robotics system alone. This thesis will discuss the design and fabrication of a compact, light, 3 degree of freedom robot arm using common off the shelf products and machined components that in combination can pick up half pound samples and has a reach of 260 mm. Also addressed is making the telemanipulator interface easier to use. One of the challenges in using a robot arm with a single camera in a beyond line-of-sight scenario is the difficulty of interacting with the environment because of a loss of depth information. This lack of information can be remedied with additional sensors. Once depth to an object of interest is known, the sampler can automatically pick up objects of interest. The manipulator arm will be used in conjunction with systems developed by the Unmanned Systems Laboratory at Virginia Tech. This group is developing a unmanned ground vehicle to be carried in the payload pod of a unmanned aerial vehicle. The robot's ultimate objective is to collect shrapnel and bomb material from potentially dangerous environments. / Master of Science

The purpose of this research is to develop a generalised model for levels of autonomy and sophistication for autonomous systems. It begins with an introduction to the research, its aims and objectives before a detailed review of related literature is presented as it pertains to the subject matter and the methodology used in the research. The research tasks are carried out using appropriate methods including literature reviews, case studies and semi-structured interviews. Through identifying the gaps in the current work on human assistive robots, a generalised model for assessing levels of autonomy and sophistication for human assistive robots (ALFHAR) is created through logical modelling, semi-structured interview methods and case studies. A web-based tool for the ALFHAR model is also created to support the model application. The ALFHAR model evaluates levels of autonomy and sophistication with regard to the decision making, interaction, and mechanical ability aspects of human assistive robots. The verification of the model is achieved by analysing evaluation results from the web-based tool and ALFHAR model. The model is validated using a set of tests with stakeholders participation through the conduction of a case study using the web-based tool. The main finding from this research is that the ALFHAR model can be considered as a model to be used in the evaluation of levels of autonomy and sophistication for human assistive robots. It can also prove helpful as part of through life management support for autonomous systems. The thesis concludes with a critical review of the research and some recommendations for further research.

This thesis presents a control algorithm to be used by a team of ECAVs (Electronic Combat Air Vehicle) to deceive a network of radars through the generation of a phantom track. Each ECAV has the electronic capability of intercepting and introducing an appropriate time delay to a transmitted pulse of a radar before transmitting it back to the radar, thereby deceiving the radar into seeing a phantom target at a range beyond that of the ECAV. A radar network correlates targets and target tracks to detect range delay based deception. A team of cooperating ECAVs, however, precisely plans their trajectories in a way all the radars in the radar network are deceived into seeing the same phantom. Since each radar in the network confirms the target track of the other, the phantom track is considered valid. An important feature of the algorithm achieving this is that it translates kinematic constraints on the ECAV dynamic system into constraints on the phantom point. The phantom track between two specified way points then evolves without violating any of the system constraints. The evolving phantom track in turn generates the actual controls on the ECAVs so that ECAVs have flyable trajectories. The algorithms give feasible but suboptimal solutions. The main objectives are algorithm development for phantom track generation through a team of cooperating ECAVs, development of the algorithms to be finite dimensional searches and determining necessary conditions for feasible solutions in the immediate horizon of the searches of the algorithm. Feasibility of the algorithm in deceiving a radar network through phantom track generation is demonstrated through simulation results.

An autonomous system has been widely applied for various civil/military research because of its versatile capability of understanding high-level intent and direction of a surrounding environment and targets of interest. However, as autonomous systems can be out of control to cause serious loss, injury, or death in the worst case, the verification of their functionalities has got increasing attention. For that reason, this study is focused on the verification of a heterogeneous multi-agent autonomous system. The thesis first presents an overview of formal methods, especially focuses on model checking for autonomous systems verification. Then, six case studies are presented to verify the decision making behaviours of multi-agent system using two basic scenarios: surveillance and convoy. The initial system considered in the surveillance mission consists of a ground control system and a micro aerial vehicle. Their decision-making behaviours are represented by means of Kripke model and computational tree logic is used to specify the properties of this system. For automatic verification, MCMAS (Model Checker for Multi-Agent Systems) is adopted due to its novel capability to accommodate the multi-agent system. After that, the initial system is extended to include a substitute micro aerial vehicle. These initial case studies are then further extended based on SEAS DTC exemplar 2 dealing with behaviours of convoy protection. This case study includes now a ground control system, an unmanned aerial vehicle, and an unmanned ground vehicle. The MCMAS successfully verifies the targeting behaviours of the team-level unmanned systems. Reversely, these verification results help retrospectively improve the design of decision-making algorithms by considering additional agents and behaviours during four steps of scenario modification. Consequently, the last scenario deals with the system composed of a ground control system, two unmanned aerial vehicles, and four unmanned ground vehicles with fault-tolerant and communications relay capabilities. In conclusion, this study demonstrates the feasibility of model checking algorithms as a verification tool of a multi-agent system in an initial design stage. Moreover, this research can be an important first step of the certification of multi-agent autonomous systems for the domains of robotics, aerospace and aeronautics.

No / This chapter describes a test-generation approach that takes as input a model S of the expected behavior of a robotic system and seeds faults into S, leading to a set of mutants of S. Given a mutant M of S, we check whether M is a valid implementation of S, and, if it is not, we find a test case that demonstrates this: a test case that reveals the seeded fault. In order to automate this approach, we used the Wodel tool to seed faults and a combination of two tools, RoboTool and FDR, to generate tests that detect the seeded faults. The result is an overall test-generation technique that can be automated and that derives test cases that are guaranteed to find certain faults. / EPSRC grant EP/R025134/2 RoboTest: Systematic Model-Based Testing and Simulation of Mobile Autonomous Robots, Spanish MINECO-FEDER grant FAME RTI2018-093608-B-C31 and the Comunidad de Madrid project FORTE-CM S2018/TCS-4314.

Helicopters have been used in applications where they need to carry a slung load for years. More recently, unmanned (UAV) helicopters are being used to deliver supplies to military units on the ground in theaters of war. This thesis presents a helicopter slung vehicle used to carry the payload and furthermore, provide a means of actuation for the payload. This provides more control authority to the system and may ultimately allow a helicopter to fly higher with a longer tether. The vehicle designed in this thesis was designed for use with 100kg class helicopters, such as the Yamaha RMAX operated by the Virginia Tech Unmanned Systems Lab. Each system on the vehicle was custom designed — including the propulsion system, wall detection / localization system, and controller. Three shrouded propellers provided thruster actuation. A scanning laser range finder and inertial measurement unit (IMU) were used to provide localization. A first attempt at a linear full state feedback controller with a complementary filter was used to control the vehicle. All of the systems were tested individually for functionality. The shrouded propellers met their design goals and were capable of producing .7lbf of thrust each. The wall detection system was able to detect walls and windows reliably and with repeatability. Results from the controller however were less than ideal, as it was only able to control yaw in an oscillatory motion, most likely due to model deficiencies. A reaction wheel was used to control yaw of the vehicle with more success. / Master of Science

Small unmanned aircraft systems (UAS) must be able to detect and avoid conflicting traffic, an especially challenging task when the threat is another small UAS. Collision avoidance requires trajectory prediction and the performance of a collision avoidance system can be improved by extending the prediction horizon. In this thesis, an algorithm for predicting the trajectory of a small, fixed-wing UAS using an estimate of its orientation and for maneuvering around the threat, if necessary, is developed. A computer vision algorithm locates specific feature points of a threat aircraft in an image and the POSIT algorithm uses these feature points to estimate the pose (position and attitude) of the threat. A sequence of pose estimates is then used to predict the trajectory of the threat aircraft and to avoid colliding with it. To assess the algorithm's performance, the predictions are compared with predictions based solely on position estimates for a variety of encounter scenarios. Simulation and experimental results indicate that trajectory prediction using orientation estimates provides quicker response to a change in the threat aircraft trajectory and results in better prediction and avoidance performance. / Master of Science / Small unmanned aircraft systems (UAS) must be able to detect and avoid conflicting traffic, an especially challenging task when the threat is another small UAS. Collision avoidance requires trajectory prediction and the performance of a collision avoidance system can be improved by extending the prediction horizon. In this thesis, an algorithm for predicting the trajectory of a small, fixed-wing UAS using an estimate of its orientation and for maneuvering around the threat, if necessary, is developed. A computer vision algorithm locates specific feature points of a threat aircraft in an image and a pose (position and attitude) estimation algorithm uses these feature points to estimate the pose of the threat. A sequence of pose estimates is then used to predict the trajectory of the threat aircraft and to avoid colliding with it. To assess the algorithm’s performance, the predictions are compared with predictions based solely on position estimates for a variety of encounter scenarios. Simulation and experimental results indicate that trajectory prediction using orientation estimates provides quicker response to a change in the threat aircraft trajectory and results in better prediction and avoidance performance.