Publications

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A semantic understanding of the environment is needed to enable high level autonomy in robotic systems. Recent results have demonstrated rapid progress in underlying technology areas, but few results have been reported on end-to-end systems that enable effective autonomous perception in complex environments. In this paper, we describe an approach for rapidly and autonomously mapping unknown environments with integrated semantic and geometric information. We use surfel-based RGB-D SLAM techniques, with incremental object segmentation and classification methods to update the map in realtime. Information theoretic and heuristic measures are used to quickly plan sensor motion and drive down map uncertainty. Preliminary experimental results in simple and cluttered environments are reported.

Current approaches to physical security suffer from high false alarm rates and frequent human operator involvement, despite the relative rarity of real-world threats. We present a novel architecture for autonomous adaptive physical security called autonomous detection and assessment with moving sensors (ADAMS). ADAMS is a framework for reducing nuisance and false alarms by placing mobile robotic platforms equipped with sensors outside the normal asset perimeter. These robotic agents integrate sensor data from multiple perspectives over time, and autonomously move to obtain the best new data to reduce uncertainty in the threat scene. Inferences drawn from data fused over time provide ultimate decisions regarding whether to alert human operators. This paper describes the framework and algorithms used in a prototype ADAMS implementation. We describe the results of simulations comparing this framework to alternate paradigms. These simulations show ADAMS has a 4x increase in the range at which threats are identified versus traditional static sensors, and a 5x reduction in false alarms triggered versus frameworks where all sensor detections become alarms, leading to reduced operator load. Further, these simulations show this framework for reacting to new potential threats significantly outperforms methods which merely patrol the site. We also present the results of preliminary hardware trials of an exemplar prototype system, providing limited validation of the simulations in a real-time physical demonstration.

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We discuss the multiple pursuer-based intercept of a threat unmanned aerial system (UAS) with stochastic dynamics via multiple pursuing UASs, using forward stochastic reachability and receding horizon control techniques. We formulate a stochastic model for the threat that can emulate the potentially adversarial behavior and is amenable to the existing scalable results in forward stochastic reachability literature. The optimal state for the intercept for each individual pursuer is obtained via a log-concave optimization problem, and the open-loop control paths are obtained via a convex optimization problem. With stochasticity modeled as a Gaussian process, we can approximate the optimization problem as a quadratic program, to enable real-time path planning. We also incorporate real-time sensing into the path planning by using a receding horizon controller, to improve the intercept probabilities. We validate the proposed framework via hardware experiments.

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As unmanned systems (UMS) proliferate for security and defense applications, autonomous control system capabilities that enable them to perform tactical operations are of increasing interest. These operations, in which UMS must match or exceed the performance and speed of people or manned assets, even in the presence of dynamic mission objectives and unpredictable adversary behavior, are well beyond the capability of even the most advanced control systems demonstrated to date. In this paper we deconstruct the tactical autonomy problem, identify the key technical challenges, and place them into context with the autonomy taxonomy produced by the US Department of Defense's Autonomy Community of Interest. We argue that two key capabilities beyond the state of the art are required to enable an initial fieldable capability: rapid abstract perception in appropriate environments, and tactical reasoning. We summarize our work to date in tactical reasoning, and present initial results from a new research program focused on abstract perception in tactical environments. This approach seeks to apply semantic labels to a broad set of objects via three core thrusts. First, we use physics-based multi-sensor fusion to enable generalization from imperfect and limited training data. Second, we pursue methods to optimize sensor perspective to improve object segmentation, mapping and, ultimately, classification. Finally, we assess the potential impact of using sensors that have not traditionally been used by UMS to perceive their environment, for example hyperspectral imagers, on the ability to identify objects. Our technical approach and initial results are presented.