We explore the use of transfer learning to reduce the data and computing resources required for training convolutional neural networks used by autonomous vehicles for predicting target behavior and improving target tracking as the scenario/environment changes. We demonstrate the ability to adapt to four different changes to the baseline scenario: a new target behavior, mission, adversary, and environment. The results from all four scenarios demonstrate positive transfer learning with reduced training datasets and show that transfer learning is a robust approach to dealing with changing environments even when the input or output dimensions of the neural network are changed.
Multi agent hybrid dynamical systems are a natural model for collaborative missions in which several steps and behaviors are required to achieve the goal of the mission. Missions are tasks featuring interacting subtasks, such as the decision of where to search, how to search, and when to transition from a search behavior to a rescue behavior. Control in hybrid systems is poorly understood. Theoretical results on state reachability rely on restrictive assumptions which hinder formal verification and optimization of such systems. Further difficulties arise if there are no a priori ordering or termination conditions on the intermediate steps and behaviors. We present a flexible framework to enable decentralized multi agent hybrid control and demonstrate its efficacy in a class of multi-region search and rescue scenarios. We also demonstrate the importance of dynamic target modeling at both levels of the hybrid state, i.e. estimating which region targets are in, how search behavior affects this estimate, and how the targets move between and within regions.
We take the first step to demonstrate the feasibility of using Modular, Extensible, Interoperable Autonomy (MEIA) to support the Internet of Military Things (IoMT) by implementing MEIA for an Aurelia drone. We set up a pipeline for autonomy development (PAD) which includes tools and processes that facilitate autonomy development. We implement both the middleware and internal data management to run our MEIA solution which includes the autonomy algorithms capable of executing various missions. We provide the results of our final demonstrations which mark the completion of this initial step to demonstrate MEIA as a viable cross-domain, autonomy architecture for IoMT.
As warfare looks to the future and the need for the internet of military things (IoMT) grows, we discuss how autonomy fits into this paradigm. We define common terms relating to autonomy to promote common understanding between autonomy developers, and we analyze a variety of autonomy architectures, examining what they do correctly to support IoMT and where they fall short. We discuss our general philosophy concerning autonomy – that it must be multi-layered to be effective – and provide an overview for our Modular, Extensible, Interoperable Autonomy architecture that supports IoMT and the future of warfare.
We implement online deep learning for target behavior prediction. Our online deep learning algorithm provides an autonomous agent the ability to train in real-time while also shaping the frequency of training based on its current performance level. The benefits of our algorithm are twofold: (1) to enable an autonomous agent to train in real-time and continue to learn to accurately predict target behavior even while its target changes the strategy guiding its behavior, and (2) to achieve more efficient usage of its computational resources by managing its training frequency. This trained predictive capability is leveraged in autonomous decision-making to influence a target’s behavior by selecting those actions that produce a predicted response from the target that supports the end goal of the autonomous agent. In our scenario, the goal of the autonomous agent is to influence its target to circle the perimeter of the environment. We test our online deep learning algorithm in environments of varying sizes to demonstrate that the time it takes for an autonomous agent to achieve the target level of accuracy is directly proportional to the size of the environment.
Five algorithms are implemented for coverage control techniques for decentralized swarms of autonomous agents. Some of the algorithms are used for covering an area in general while others are intended for covering an area with an underlying priority density function. Standard Lloyd’s algorithm and Basic Weighted Lloyd’s algorithm are used as baselines in the analysis against three new algorithms: Even Distribution, Biased Weighted Lloyd’s, and Evolutionary. Experimental results demonstrate that each of these new algorithms improve the quality of the agents’ performance by either reducing coverage cost or distance travelled and/or time spent settling into an equilibrium formation.
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