Closed-loop Bayesian Semantic Data Fusion for Collaborative Human-Autonomy Target Search

In search applications, autonomous unmanned vehicles must be able to efficiently reacquire and localize mobile targets that can remain out of view for long periods of time in large spaces. As such, all available information sources must be actively leveraged -- including imprecise but readily available semantic observations provided by humans. To achieve this, this work develops and validates a novel collaborative human-machine sensing solution for dynamic target search. Our approach uses continuous partially observable Markov decision process (CPOMDP) planning to generate vehicle trajectories that optimally exploit imperfect detection data from onboard sensors, as well as semantic natural language observations that can be specifically requested from human sensors. The key innovation is a scalable hierarchical Gaussian mixture model formulation for efficiently solving CPOMDPs with semantic observations in continuous dynamic state spaces. The approach is demonstrated and validated with a real human-robot team engaged in dynamic indoor target search and capture scenarios on a custom testbed.Comment: Final version accepted and submitted to 2018 FUSION Conference (Cambridge, UK, July 2018

Home alone: autonomous extension and correction of spatial representations

In this paper we present an account of the problems faced by a mobile robot given an incomplete tour of an unknown environment, and introduce a collection of techniques which can generate successful behaviour even in the presence of such problems. Underlying our approach is the principle that an autonomous system must be motivated to act to gather new knowledge, and to validate and correct existing knowledge. This principle is embodied in Dora, a mobile robot which features the aforementioned techniques: shared representations, non-monotonic reasoning, and goal generation and management. To demonstrate how well this collection of techniques work in real-world situations we present a comprehensive analysis of the Dora system’s performance over multiple tours in an indoor environment. In this analysis Dora successfully completed 18 of 21 attempted runs, with all but 3 of these successes requiring one or more of the integrated techniques to recover from problems

Dynamic Estimation of Rigid Motion from Perspective Views via Recursive Identification of Exterior Differential Systems with Parameters on a Topological Manifold

We formulate the problem of estimating the motion of a rigid object viewed under perspective projection as the identification of a dynamic model in Exterior Differential form with parameters on a topological manifold. We first describe a general method for recursive identification of nonlinear implicit systems using prediction error criteria. The parameters are allowed to move slowly on some topological (not necessarily smooth) manifold. The basic recursion is solved in two different ways: one is based on a simple extension of the traditional Kalman Filter to nonlinear and implicit measurement constraints, the other may be regarded as a generalized "Gauss-Newton" iteration, akin to traditional Recursive Prediction Error Method techniques in linear identification. A derivation of the "Implicit Extended Kalman Filter" (IEKF) is reported in the appendix. The ID framework is then applied to solving the visual motion problem: it indeed is possible to characterize it in terms of identification of an Exterior Differential System with parameters living on a C0 topological manifold, called the "essential manifold". We consider two alternative estimation paradigms. The first is in the local coordinates of the essential manifold: we estimate the state of a nonlinear implicit model on a linear space. The second is obtained by a linear update on the (linear) embedding space followed by a projection onto the essential manifold. These schemes proved successful in performing the motion estimation task, as we show in experiments on real and noisy synthetic image sequences

Design and Autonomous Stabilization of a Ballistically Launched Multirotor

Aircraft that can launch ballistically and convert to autonomous, free flying drones have applications in many areas such as emergency response, defense, and space exploration, where they can gather critical situational data using onboard sensors. This paper presents a ballistically launched, autonomously stabilizing multirotor prototype (SQUID, Streamlined Quick Unfolding Investigation Drone) with an onboard sensor suite, autonomy pipeline, and passive aerodynamic stability. We demonstrate autonomous transition from passive to vision based, active stabilization, confirming the ability of the multirotor to autonomously stabilize after a ballistic launch in a GPS denied environment.Comment: Accepted to 2020 International Conference on Robotics and Automatio

Verification of Uncertain POMDPs Using Barrier Certificates

We consider a class of partially observable Markov decision processes (POMDPs) with uncertain transition and/or observation probabilities. The uncertainty takes the form of probability intervals. Such uncertain POMDPs can be used, for example, to model autonomous agents with sensors with limited accuracy, or agents undergoing a sudden component failure, or structural damage [1]. Given an uncertain POMDP representation of the autonomous agent, our goal is to propose a method for checking whether the system will satisfy an optimal performance, while not violating a safety requirement (e.g. fuel level, velocity, and etc.). To this end, we cast the POMDP problem into a switched system scenario. We then take advantage of this switched system characterization and propose a method based on barrier certificates for optimality and/or safety verification. We then show that the verification task can be carried out computationally by sum-of-squares programming. We illustrate the efficacy of our method by applying it to a Mars rover exploration example.Comment: 8 pages, 4 figure

A Concurrency and Time Centered Framework for Certification of Autonomous Space Systems

Future space missions, such as Mars Science Laboratory, suggest the engineering of some of the most complex man-rated autonomous software systems. The present process-oriented certification methodologies are becoming prohibitively expensive and do not reach the level of detail of providing guidelines for the development and validation of concurrent software. Time and concurrency are the most critical notions in an autonomous space system. In this work we present the design and implementation of the first concurrency and time centered framework for product-oriented software certification of autonomous space systems. To achieve fast and reliable concurrent interactions, we define and apply the notion of Semantically Enhanced Containers (SEC). SECs are data structures that are designed to provide the flexibility and usability of the popular ISO C++ STL containers, while at the same time they are hand-crafted to guarantee domain-specific policies, such as conformance to a given concurrency model. The application of nonblocking programming techniques is critical to the implementation of our SEC containers. Lock-free algorithms help avoid the hazards of deadlock, livelock, and priority inversion, and at the same time deliver fast and scalable performance. Practical lock-free algorithms are notoriously difficult to design and implement and pose a number of hard problems such as ABA avoidance, high complexity, portability, and meeting the linearizability correctness requirements. This dissertation presents the design of the first lock-free dynamically resizable array. Our approach o ers a set of practical, portable, lock-free, and linearizable STL vector operations and a fast and space effcient implementation when compared to the alternative lock- and STM-based techniques. Currently, the literature does not offer an explicit analysis of the ABA problem, its relation to the most commonly applied nonblocking programming techniques, and the possibilities for its detection and avoidance. Eliminating the hazards of ABA is left to the ingenuity of the software designer. We present a generic and practical solution to the fundamental ABA problem for lock-free descriptor-based designs. To enable our SEC container with the property of validating domain-specific invariants, we present Basic Query, our expression template-based library for statically extracting semantic information from C++ source code. The use of static analysis allows for a far more efficient implementation of our nonblocking containers than would have been otherwise possible when relying on the traditional run-time based techniques. Shared data in a real-time cyber-physical system can often be polymorphic (as is the case with a number of components part of the Mission Data System's Data Management Services). The use of dynamic cast is important in the design of autonomous real-time systems since the operation allows for a direct representation of the management and behavior of polymorphic data. To allow for the application of dynamic cast in mission critical code, we validate and improve a methodology for constant-time dynamic cast that shifts the complexity of the operation to the compiler's static checker. In a case study that demonstrates the applicability of the programming and validation techniques of our certification framework, we show the process of verification and semantic parallelization of the Mission Data System's (MDS) Goal Networks. MDS provides an experimental platform for testing and development of autonomous real-time flight applications

The Complexity of Online Manipulation of Sequential Elections

Most work on manipulation assumes that all preferences are known to the manipulators. However, in many settings elections are open and sequential, and manipulators may know the already cast votes but may not know the future votes. We introduce a framework, in which manipulators can see the past votes but not the future ones, to model online coalitional manipulation of sequential elections, and we show that in this setting manipulation can be extremely complex even for election systems with simple winner problems. Yet we also show that for some of the most important election systems such manipulation is simple in certain settings. This suggests that when using sequential voting, one should pay great attention to the details of the setting in choosing one's voting rule. Among the highlights of our classifications are: We show that, depending on the size of the manipulative coalition, the online manipulation problem can be complete for each level of the polynomial hierarchy or even for PSPACE. We obtain the most dramatic contrast to date between the nonunique-winner and unique-winner models: Online weighted manipulation for plurality is in P in the nonunique-winner model, yet is coNP-hard (constructive case) and NP-hard (destructive case) in the unique-winner model. And we obtain what to the best of our knowledge are the first P^NP[1]-completeness and P^NP-completeness results in the field of computational social choice, in particular proving such completeness for, respectively, the complexity of 3-candidate and 4-candidate (and unlimited-candidate) online weighted coalition manipulation of veto elections.Comment: 24 page