This paper characterizes an algorithm that estimates searcher skill level to support planning for search activities involving heterogeneous robot and human/robot teams. Specifically, we use Monte-Carlo simulations to determine the empirical accuracy of the estimator, to assess the quality of its predicted distribution (nonparametric) of agent skill levels, and the convergence rate of the estimate. The simulation study suggests that a single challenging search task can be used to estimate searcher skill within about 10%; however, the quality of the estimate is higher when searcher skill is high.

This paper presents the alternating-normal iterative method, a novel algorithm that uses angle-of-arrival (or bearing) measurements for relative positioning of networked collaborating robots moving in three dimensions. The method is decentralized, with robots exploiting communication to iteratively refine measurements and enforce geometric consistency. The key result of the paper is a proof of convergence.

This paper discusses how surface surveillance technologies impact the design of automated conflict-detection capability for a next-generation air transportation system dubbed NextGen. In NextGen, automated conflict detection and resolution (CD&R) algorithms will be necessary to assist air traffic controllers in identifying and mitigating potential hazards posed by nonconforming aircraft. Given this high reliance on automation, rigorous specifications for conflict detection (CD) algorithm continuity and integrity risk will be required. Continuity risk refers to the probability that a CD alert interrupts an ongoing operation; integrity risk refers to the probability that the CD algorithm fails to provide an alert rapidly enough to resolve the conflict. The continuity and integrity of CD algorithms strongly depend on the quality of surface surveillance sensor measurements; as such, we propose mechanisms for modifying the CD&R algorithm design to rigorously account for the capabilities and limitations of surveillance sensors.

This paper presents an approach for generating Global Navigation Satellite System (GNSS) differential corrections by distributing GNSS and georeferenced vision measurements through a vehicle-to-vehicle (V2V) communications network. Conventionally, high-quality differential GNSS corrections are generated from a stationary reference receiver in close proximity to a set of mobile users. The proposed method, which is called Collaborative Vision Integrated Pseudorange Error Removal (C-VIPER), instead generates differential corrections using data from moving vehicles, thus eliminating the need for an infrastructure of stationary receivers. An important feature of the proposed algorithm is that individual differential corrections are computed for each satellite, so that corrections can be shared among users with different satellites in view. As demonstrated in simulation, measurement sharing significantly improves positioning accuracy in both the cross-track direction, where the quality of visual lane-boundary measurements is high, and the along-track direction, where the quality is low. Furthermore, because measurements are shared among many vehicles, the networked solution is robust to vision-sensor dropouts that may occur for individual vehicles.

The United States Navy seeks the capability to land manned and unmanned aerial vehicles autonomously on an aircraft carrier using GPS. To deliver this capability, the Navy is developing a navigation system called the Sea-Based Joint Precision Approach and Landing System (JPALS). Because standard GPS is not sufficiently precise to land aircraft on a shortened, constantly moving runway, Sea-Based JPALS leverages dual-frequency, carrier-phase differential GPS navigation. Carrier phase measurements, derived from the sinusoidal waveforms underlying the GPS signal, are very precise but not necessarily accurate unless the user resolves the ambiguity associated with the sinusoid's periodicity. Ensuring the validity of ambiguity resolution is the central challenge for the high-integrity, safety-critical JPALS application. Based on a multi-year, multi-institution collaborative study, this paper proposes a navigation and monitoring architecture designed to meet the guidance quality challenge posed by Sea-Based JPALS. In particular, we propose a two-stage navigation algorithm that meets the aggressive integrity-risk requirement for Sea-Based JPALS by first filtering a combination of GPS observables and subsequently exploiting those observables to resolve the carrier ambiguity. Because JPALS-equipped aircraft may encounter jamming, we also discuss interference mitigation technologies, such as inertial fusion and array antennas, which, with appropriate algorithmic modifications, can ensure integrity under Radio Frequency Interference (RFI) conditions. Lastly, we recommend a fault monitoring strategy tailored to the two-stage navigation algorithm. Monitoring will detect and isolate rare anomalies such as ionosphere storms or satellite ephemeris errors which would otherwise corrupt ambiguity resolution and positioning in Sea-Based JPALS.

Limits on the technology available to marine scientists to study jellyfish in situ motivate the development of an automated robotic tracking system for deployment in the deep ocean. This paper synthesizes a control strategy for robotic jellyfish tracking with a remotely operated vehicle (ROV). The control strategy employs three feedback loops tailored to the jelly-tracking task. A primary loop provides moderate proportional-derivative feedback to track animal swimming motion without producing excessive hydrodynamic disturbances, which might impact the behavior of the animal under study. A second boundary-control loop provides aggressive thrust, in a direction away from the target and only when needed to prevent loss of the target outside the boundaries of the vision sensor. A third disturbance-accommodation loop counters low-frequency bias forces without fighting commands issued by the human pilot. The complete system was implemented and tested in the Monterey Bay using the Monterey Bay Aquarium Research Institute's ROV Ventana. The control system autonomously tracked six animals for durations longer than 15 min, including a Plychogena medusa which was tracked for 89 min

Marine biologists desire an automated system for observing gelatinous zooplankton. This study represents the first attempt at real-time robotic tracking of a gelatinous animal in the open ocean. Challenges stem from uneven lighting, encounters with other marine life and frequent out-of-frame events. Analysis of a large video library of human-controlled jelly tracking supported selection of an appropriate vision algorithm. The vision architecture was implemented as an ROV pilot aid and tested at 600 m below the surface of Monterey Bay.