Monitoring using Heterogeneous Autonomous Agents.

This dissertation studies problems involving different types of autonomous agents observing objects of interests in an area. Three types of agents are considered: mobile agents, stationary agents, and marsupial agents, i.e., agents capable of deploying other agents or being deployed themselves. Objects can be mobile or stationary. The problem of a mobile agent without fuel constraints revisiting stationary objects is formulated. Visits to objects are dictated by revisit deadlines, i.e., the maximum time that can elapse between two visits to the same object. The problem is shown to be NP-complete and heuristics are provided to generate paths for the agent. Almost periodic paths are proven to exist. The efficacy of the heuristics is shown through simulation. A variant of the problem where the agent has a finite fuel capacity and purchases fuel is treated. Almost periodic solutions to this problem are also shown to exist and an algorithm to compute the minimal cost path is provided. A problem where mobile and stationary agents cooperate to track a mobile object is formulated, shown to be NP-hard, and a heuristic is given to compute paths for the mobile agents. Optimal configurations for the stationary agents are then studied. Several methods are provided to optimally place the stationary agents; these methods are the maximization of Fisher information, the minimization of the probability of misclassification, and the minimization of the penalty incurred by the placement. A method to compute optimal revisit deadlines for the stationary agents is given. The placement methods are compared and their effectiveness shown using numerical results. The problem of two marsupial agents, one carrier and one passenger, performing a general monitoring task using a constrained optimization formulation is stated. Necessary conditions for optimal paths are provided for cases accounting for constrained release of the passenger, termination conditions for the task, as well as retrieval and constrained retrieval of the passenger. A problem involving two marsupial agents collecting information about a stationary object while avoiding detection is then formulated. Necessary conditions for optimal paths are provided and rectilinear motion is demonstrated to be optimal for both agents.PhDAerospace EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/111439/1/jfargeas_1.pd

Persistent Visitation with Fuel Constraints

AbstractThis work is motivated by the periodic vehicle routing problem (PVRP) where a vehicle is to perpetually visit customers within a given area. In this work there is no sense of horizon or days as in classic PVRP. Instead, it is assumed that each customer has a rate at which it must be visited for the vehicle to satisfy its mission. The vehicle's fuel limitations are taken into account and fuel depots with a fixed fuel price are included. The problem of finding paths that satisfy the locations’ revisit rates and minimize the total cost of fuel is treated. An algorithm that provides solutions to this problem under given constraints is presented

Wind tunnel testing of a novel wingsuit design

A wingsuit is a special suit that is worn to allow the user to fly after jumping off of a high cliff. The wingsuit creates an airfoil shape by adding wings of material between the arms and the sides as well as a tail consisting of material between the legs. The wingsuit allows for the creation of lift and thus human flying. A new and novel wingsuit design is proposed based on the design of a delta wing aircraft. This new wingsuit has material leading from the side of the head and connecting to the top of the arms, extending the area of the forward wing. Using a mannequin in a wind tunnel, the aerodynamic performance of the new wingsuit will be measured and compared to that of the current wingsuit design. The results show that the redesigned wingsuit had a lower lift-to-drag ratio in most testing scenarios. The decrease in lift-to-drag ratio was due to the combination of an increased lift and a higher increased drag

EFFECTS OF TIME PRESSURE ON THE USE OF AN AUTOMATED DECISION SUPPORT SYSTEM FOR STRIKE PLANNING

This paper describes the results of an experiment designed to examine the effects of time pressure on behavioral patterns. The main research hypothesis is that people under time pressure tend to increasingly rely on automation in order to cope with the added workload. The context is that of a missile strike planner having to create a set of matches between resources (missiles) and requirements (missions). We introduce time pressure by changing the temporal requirements towards the end of the mission. Overall performance, calls to automation and qualitative strategies are recorded and analyzed using ANOVA and other nonparametric tests. The main finding of this study is that while the number of calls to the automation did significantly increase under time pressure, there did not seem to be a statistically significant shift in problem solving strategies under time pressure. The experimental results show the importance of good automation-human interface design so as to gain maximum benefit from the use of an automated decision support systems.Boeing Corporatio

Cooperative Surveillance and Pursuit Using Unmanned Aerial Vehicles and Unattended Ground Sensors

This paper considers the problem of path planning for a team of unmanned aerial vehicles performing surveillance near a friendly base. The unmanned aerial vehicles do not possess sensors with automated target recognition capability and, thus, rely on communicating with unattended ground sensors placed on roads to detect and image potential intruders. The problem is motivated by persistent intelligence, surveillance, reconnaissance and base defense missions. The problem is formulated and shown to be intractable. A heuristic algorithm to coordinate the unmanned aerial vehicles during surveillance and pursuit is presented. Revisit deadlines are used to schedule the vehicles’ paths nominally. The algorithm uses detections from the sensors to predict intruders’ locations and selects the vehicles’ paths by minimizing a linear combination of missed deadlines and the probability of not intercepting intruders. An analysis of the algorithm’s completeness and complexity is then provided. The effectiveness of the heuristic is illustrated through simulations in a variety of scenarios