Effects of Dynamically Weighting Autonomous Rules in a UAS Flocking Model

Within the U.S. military, senior decision-makers and researchers alike have postulated that vast improvements could be made to current Unmanned Aircraft Systems (UAS) Concepts of Operation through inclusion of autonomous flocking. Myriad methods of implementation and desirable mission sets for this technology have been identified in the literature; however, this thesis posits that specific missions and behaviors are best suited for autonomous military flocking implementations. Adding to Craig Reynolds\u27 basic theory that three naturally observed rules can be used as building blocks for simulating flocking behavior, new rules are proposed and defined in the development of an autonomous flocking UAS model. Simulation validates that missions of military utility can be accomplished in this method through incorporation of dynamic event- and time-based rule weights. Additionally, a methodology is proposed and demonstrated that iteratively improves simulated mission effectiveness. Quantitative analysis is presented on data from 570 simulation runs, which verifies the hypothesis that iterative changes to rule parameters and weights demonstrate significant improvement over baseline performance. For a 36 square mile scenario, results show a 100% increase in finding targets, a 40.2% reduction in time to find a target, a 4.5% increase in area coverage, with a 0% attribution rate due to collisions and near misses

Environment Characterization for Non-Recontaminating Frontier-Based Robotic Exploration

This paper addresses the problem of obtaining a concise description of a physical environment for robotic exploration. We aim to determine the number of robots required to clear an environment using non-recontaminating exploration. We introduce the medial axis as a configuration space and derive a mathematical representation of a continuous environment that captures its underlying topology and geometry. We show that this representation provides a concise description of arbitrary environments, and that reasoning about points in this representation is equivalent to reasoning about robots in physical space. We leverage this to derive a lower bound on the number of required pursuers. We provide a transformation from this continuous representation into a symbolic representation. Finally, we present a generalized pursuit-evasion algorithm. Given an environment we can compute how many pursuers we need, and generate an optimal pursuit strategy that will guarantee the evaders are detected with the minimum number of pursuers.Singapore-MIT Alliance for Research and Technology Center (Future Urban Mobility Project)United States. Air Force Office of Scientific Research (Award FA9550-08-1-0159)National Science Foundation (U.S.) (Award CNS-0715397)National Science Foundation (U.S.) (Award CCF-0726514)National Science Foundation (U.S.) (Grant 0735953

MULTI-AGENT UNMANNED UNDERWATER VEHICLE VALIDATION VIA ROLLING-HORIZON ROBUST GAMES

Autonomy in unmanned underwater vehicle (UUV) navigation is critical for most applications due to inability of human operators to control, monitor or intervene in underwater environments. To ensure safe autonomous navigation, verification and validation (V&V) procedures are needed for various applications. This thesis proposes a game theory-based benchmark validation technique for trajectory optimization for non-cooperative UUVs. A quadratically constrained nonlinear program formulation is presented, and a "perfect-information reality" validation framework is derived by finding a Nash equilibrium to various two-player pursuit-evasion games (PEG). A Karush-Kuhn-Tucker (KKT) point to such a game represents a best-case local optimum, given perfect information available to non-cooperative agents. Rolling-horizon foresight with robust obstacles are incorporated to demonstrate incomplete information and stochastic environmental conditions. A MATLAB-GAMS interface is developed to model the rolling-horizon game, and is solved via a mixed complementarity problem (MCP), and illustrative examples show how equilibrium trajectories can serve as benchmarks for more practical real-time path planners

Simulation of Past Life: Controlling Agent Behaviors from the Interactions between Ethnic Groups

International audienceMany efforts have been carried out in preserving the history and culture of Penang and also other regions of Malaysia since George Town was elected as a UNESCO living heritage city. This paper presents a method to simulate life in a local trading port in the 1800s, where various populations with very different social rules interacted with each other. These populations included Indian coolies, Malay vendors, British colonists and Chinese traders. The challenge is to model these ethnic groups as autonomous agents, and to capture the changes of behavior due to inter-ethnic interactions and to the arrival of boats at the pier. Agents from each population are equipped with a specific set of steering methods which are selected and parameterized according to predefined behavioral patterns (graphs of states). In this paper, we propose a new formalism where interactions between the different ethnics groups and with the boats can be either activated globally or locally. Global interactions cause changes of states for all the agents belonging to the target population, while local interactions only take place between specific agents, and result in changes of states for these agents only. The main contributions of our method are: i) Applying microscopic crowd simulation to the complex case of a multi-ethnic trading port, involving different behavioral patterns; ii) Introducing a high-level control method, through the inter- ethnic interactions formalism. The resulting system generates a variety of real-time animations, all reflecting the adequate social behaviors. Such a system would be particularly useful in a virtual tour application