175 research outputs found
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Analysis and synthesis of collaborative opportunistic navigation systems
textNavigation is an invisible utility that is often taken for granted with considerable societal and economic impacts. Not only is navigation essential to our modern life, but the more it advances, the more possibilities are created. Navigation is at the heart of three emerging fields: autonomous vehicles, location-based services, and intelligent transportation systems. Global navigation satellite systems (GNSS) are insufficient for reliable anytime, anywhere navigation, particularly indoors, in deep urban canyons, and in environments under malicious attacks (e.g., jamming and spoofing). The conventional approach to overcome the limitations of GNSS-based navigation is to couple GNSS receivers with dead reckoning sensors. A new paradigm, termed opportunistic navigation (OpNav), is emerging. OpNav is analogous to how living creatures naturally navigate: by learning their environment. OpNav aims to exploit the plenitude of ambient radio frequency signals of opportunity (SOPs) in the environment. OpNav radio receivers, which may be handheld or vehicle-mounted, continuously search for opportune signals from which to draw position and timing information, employing on-the-fly signal characterization as necessary. In collaborative opportunistic navigation (COpNav), multiple receivers share information to construct and continuously refine a global signal landscape. For the sake of motivation, consider the following problem. A number of receivers with no a priori knowledge about their own states are dropped in an environment comprising multiple unknown terrestrial SOPs. The receivers draw pseudorange observations from the SOPs. The receivers' objective is to build a high-fidelity signal landscape map of the environment within which they localize themselves in space and time. We then ask: (i) Under what conditions is the environment fully observable? (ii) In cases where the environment is not fully observable, what are the observable states? (iii) How would receiver-controlled maneuvers affect observability? (iv) What is the degree of observability of the various states in the environment? (v) What motion planning strategy should the receivers employ for optimal information gathering? (vi) How effective are receding horizon strategies over greedy for receiver trajectory optimization, and what are their limitations? (vii) What level of collaboration between the receivers achieves a minimal price of anarchy? This dissertation addresses these fundamental questions and validates the theoretical conclusions numerically and experimentally.Electrical and Computer Engineerin
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Motion Planning for Optimal Information Gathering in Opportunistic Navigation Systems
Motion planning for optimal information gathering in an opportunistic navigation (OpNav)
environment is considered. An OpNav environment can be thought of as a radio
frequency signal landscape within which a receiver locates itself in space and time by extracting
information from ambient signals of opportunity (SOPs). The receiver is assumed
to draw only pseudorange-type observations from the SOPs, and such observations are
fused through an estimator to produce an estimate of the receiver’s own states. Since
not all SOP states in the OpNav environment may be known a priori, the receiver must
estimate the unknown SOP states of interest simultaneously with its own states. In this
work, the following problem is studied. A receiver with no a priori knowledge about its
own states is dropped in an unknown, yet observable, OpNav environment. Assuming that
the receiver can prescribe its own trajectory, what motion planning strategy should the
receiver adopt in order to build a high-fidelity map of the OpNav signal landscape, while
simultaneously localizing itself within this map in space and time? To answer this question,
first, the minimum conditions under which the OpNav environment is fully observable are
established, and the need for receiver maneuvering to achieve full observability is highlighted.
Then, motivated by the fact that not all trajectories a receiver may take in the
environment are equally beneficial from an information gathering point of view, a strategy
for planning the motion of the receiver is proposed. The strategy is formulated in a
coupled estimation and optimal control framework of a gradually identified system, where
optimality is defined through various information-theoretic measures. Simulation results
are presented to illustrate the improvements gained from adopting the proposed strategy
over random and pre-defined receiver trajectories.Aerospace Engineering and Engineering Mechanic
The Price of Anarchy in Active Signal Landscape Map Building
Multiple receivers with a priori knowledge about
their own initial states are assumed to be dropped in an unknown
environment comprising multiple signals of opportunity (SOPs)
transmitters. The receivers draw pseudorange observations from
the SOPs. The receivers’ objective is to build a high-fidelity
signal landscape map of the environment, which would enable
the receivers to navigate accurately with the aid of the SOPs.
The receivers could command their own maneuvers and such
commands are computed so to maximize the information gathered
about the SOPs in a greedy fashion. Several information
fusion and decision making architectures are possible. This
paper studies the price of anarchy in building signal landscape
maps to assess the degradation in the map quality should the
receivers produce their own maps and make their own maneuver
decisions versus a completely centralized approach. In addition,
a hierarchical architecture is proposed in which the receivers
build their own maps and make their own decisions, but share
relevant information. Such architecture is shown to produce maps
of comparable quality to the completely centralized approach.Aerospace Engineering and Engineering Mechanic
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Adaptive Estimation of Signals of Opportunity
To exploit unknown ambient radio frequency signals of
opportunity (SOPs) for positioning and navigation, one
must estimate their states along with a set of parameters
that characterize the stability of their oscillators. SOPs
can be modeled as stochastic dynamical systems driven
by process noise. The statistics of such process noise is
typically unknown to the receiver wanting to exploit the
SOPs for positioning and navigation. Incorrect statistical
models jeopardize the estimation optimality and may
cause filter divergence. This necessitates the development
of adaptive filters, which provide a significant improvementover fixed filters through the filter learning process. This
paper develops two such adaptive filters: an innovationbased
maximum likelihood filter and an interacting multiple
model filter and compares their performance and complexity.
Numerical and experimental results are presented
demonstrating the superiority of these filters over fixed,
mismatched filters.Aerospace Engineering and Engineering Mechanic
Non-Centralized Navigation for Source Localization by Cooperative UAVs
In this paper, we propose a distributed solution to the navigation of a
population of unmanned aerial vehicles (UAVs) to best localize a static source.
The network is considered heterogeneous with UAVs equipped with received signal
strength (RSS) sensors from which it is possible to estimate the distance from
the source and/or the direction of arrival through ad-hoc rotations. This
diversity in gathering and processing RSS measurements mitigates the loss of
localization accuracy due to the adoption of low-complexity sensors. The UAVs
plan their trajectories on-the-fly and in a distributed fashion. The collected
data are disseminated through the network via multi-hops, therefore being
subject to latency. Since not all the paths are equal in terms of information
gathering rewards, the motion planning is formulated as a minimization of the
uncertainty of the source position under UAV kinematic and anti-collision
constraints and performed by 3D non-linear programming. The proposed analysis
takes into account non-line-of-sight (NLOS) channel conditions as well as
measurement age caused by the latency constraints in communication.Comment: 5 pages, 3 figures, conferenc
A Real-Time Game Theoretic Planner for Autonomous Two-Player Drone Racing
To be successful in multi-player drone racing, a player must not only follow
the race track in an optimal way, but also compete with other drones through
strategic blocking, faking, and opportunistic passing while avoiding
collisions. Since unveiling one's own strategy to the adversaries is not
desirable, this requires each player to independently predict the other
players' future actions. Nash equilibria are a powerful tool to model this and
similar multi-agent coordination problems in which the absence of communication
impedes full coordination between the agents. In this paper, we propose a novel
receding horizon planning algorithm that, exploiting sensitivity analysis
within an iterated best response computational scheme, can approximate Nash
equilibria in real time. We also describe a vision-based pipeline that allows
each player to estimate its opponent's relative position. We demonstrate that
our solution effectively competes against alternative strategies in a large
number of drone racing simulations. Hardware experiments with onboard vision
sensing prove the practicality of our strategy
Belief-space Planning for Active Visual SLAM in Underwater Environments.
Autonomous mobile robots operating in a priori unknown environments must be able to integrate path planning with simultaneous localization and mapping (SLAM) in order to perform tasks like exploration, search and rescue, inspection, reconnaissance, target-tracking, and others. This level of autonomy is especially difficult in underwater environments, where GPS is unavailable, communication is limited, and environment features may be sparsely- distributed. In these situations, the path taken by the robot can drastically affect the performance of SLAM, so the robot must plan and act intelligently and efficiently to ensure successful task completion.
This document proposes novel research in belief-space planning for active visual SLAM in underwater environments. Our motivating application is ship hull inspection with an autonomous underwater robot. We design a Gaussian belief-space planning formulation that accounts for the randomness of the loop-closure measurements in visual SLAM and serves as the mathematical foundation for the research in this thesis. Combining this planning formulation with sampling-based techniques, we efficiently search for loop-closure actions throughout the environment and present a two-step approach for selecting revisit actions that results in an opportunistic active SLAM framework. The proposed active SLAM method is tested in hybrid simulations and real-world field trials of an underwater robot performing inspections of a physical modeling basin and a U.S. Coast Guard cutter.
To reduce computational load, we present research into efficient planning by compressing the representation and examining the structure of the underlying SLAM system. We propose the use of graph sparsification methods online to reduce complexity by planning with an approximate distribution that represents the original, full pose graph. We also propose the use of the Bayes tree data structure—first introduced for fast inference in SLAM—to perform efficient incremental updates when evaluating candidate plans that are similar. As a final contribution, we design risk-averse objective functions that account for the randomness within our planning formulation. We show that this aversion to uncertainty in the posterior belief leads to desirable and intuitive behavior within active SLAM.PhDMechanical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/133303/1/schaves_1.pd
A Real-Time Game Theoretic Planner for Autonomous Two-Player Drone Racing
In this article, we propose an online 3-D planning algorithm for a drone to race competitively against a single adversary drone. The algorithm computes an approximation of the Nash equilibrium in the joint space of trajectories of the two drones at each time step, and proceeds in a receding horizon fashion. The algorithm uses a novel sensitivity term, within an iterative best response computational scheme, to approximate the amount by which the adversary will yield to the ego drone to avoid a collision. This leads to racing trajectories that are more competitive than without the sensitivity term. We prove that the fixed point of this sensitivity enhanced iterative best response satisfies the first-order optimality conditions of a Nash equilibrium. We present results of a simulation study of races with 2-D and 3-D race courses, showing that our game theoretic planner significantly outperforms amodel predictive control (MPC) racing algorithm. We also present results of multiple drone racing experiments on a 3-D track in which drones sense each others'' relative position with onboard vision. The proposed game theoretic planner again outperforms the MPC opponent in these experiments where drones reach speeds up to 1.25m/s
Obstacle Avoidance for a Game Theoretically Controlled Formation of Unmanned Vehicles
The thesis provides a game theoretical approach to the control of a formation of unmanned vehicles. The objectives of the formation are to follow a prescribed trajectory, avoiding obstacle(s) while maintaining the geometry of the formation. Formation control is implemented using game theory while obstacles are avoided using Null Space Based Behavioral Control algorithm. Different obstacle avoidance scenarios are analyzed and compared. Numerical simulation results are presented, to validate the proposed approach
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