The reliability of individual robots influences the success of multirobot missions. When one robot fails, others
must be retasked to complete the failed robot's tasks. This increases the failure likelihood for these other robots.
Existing multirobot task allocation systems consider robot failures only after the fact, via replanning. In this
paper we show that mission performance for multirobot missions can be improved by using knowledge of robot
failure rates to inform the initial task allocation.
Earth science research must bridge the gap between the atmosphere and the ocean to foster understanding of Earth's
climate and ecology. Typical ocean sensing is done with satellites or in situ buoys and research ships which are slow to
reposition. Cloud cover inhibits study of localized transient phenomena such as Harmful Algal Blooms (HAB). A fleet
of extended-deployment surface autonomous vehicles will enable in situ study of characteristics of HAB, coastal
pollutants, and related phenomena. We have developed a multiplatform telesupervision architecture that supports
adaptive reconfiguration based on environmental sensor inputs. Our system allows the autonomous repositioning of
smart sensors for HAB study by networking a fleet of NOAA OASIS (Ocean Atmosphere Sensor Integration System)
surface autonomous vehicles. In situ measurements intelligently modify the search for areas of high concentration.
Inference Grid and complementary information-theoretic techniques support sensor fusion and analysis. Telesupervision
supports sliding autonomy from high-level mission tasking, through vehicle and data monitoring, to teleoperation when
direct human interaction is appropriate. This paper reports on experimental results from multi-platform tests conducted
in the Chesapeake Bay and in Pittsburgh, Pennsylvania waters using OASIS platforms, autonomous kayaks, and multiple
simulated platforms to conduct cooperative sensing of chlorophyll-a and water quality.
We are developing a multi-robot science exploration architecture and system called the Telesupervised Adaptive Ocean
Sensor Fleet (TAOSF). TAOSF uses a group of robotic boats (the OASIS platforms) to enable in-situ study of ocean
surface and sub-surface phenomena. The OASIS boats are extended-deployment autonomous ocean surface vehicles,
whose development is funded separately by the National Oceanic and Atmospheric Administration (NOAA). The
TAOSF architecture provides an integrated approach to multi-vehicle coordination and sliding human-vehicle autonomy.
It allows multiple mobile sensing assets to function in a cooperative fashion, and the operating mode of the vessels to
range from autonomous control to teleoperated control. In this manner, TAOSF increases data-gathering effectiveness
and science return while reducing demands on scientists for tasking, control, and monitoring. It combines and extends
prior related work done by the authors and their institutions. The TAOSF architecture is applicable to other areas where
multiple sensing assets are needed, including ecological forecasting, water management, carbon management, disaster
management, coastal management, homeland security, and planetary exploration. The first field application chosen for
TAOSF is the characterization of Harmful Algal Blooms (HABs). Several components of the TAOSF system have been
tested, including the OASIS boats, the communications and control interfaces between the various hardware and
software subsystems, and an airborne sensor validation system. Field tests in support of future HAB characterization
were performed under controlled conditions, using rhodamine dye as a HAB simulant that was dispersed in a pond. In
this paper, we describe the overall TAOSF architecture and its components, discuss the initial tests conducted and
outline the next steps.
Small unmanned aerial vehicles (UAVs) are hindered by their limited payload and duration. Consequently, UAVs spend little time in their area of operation, returning frequently to base for refueling. The effective payload and duration of small UAVs is increased by moving the support base closer to the operating area; however this increases risk to personnel. Performing the refueling operations autonomously allows the support base to be located closer to the operating area without increasing risk to personnel. Engineers at SPAWAR Systems Center San Diego (SSC San Diego) are working to develop technologies for automated launch, recovery, refueling, rearming, and re-launching of small UAVs. These technologies are intended to provide forward-refueling capabilities by teaming small UAVs with large unmanned ground vehicles (UGVs). The UGVs have larger payload capacities so they can easily carry fuel for the UAVs in addition to their own fuel and mission payloads. This paper describes a prototype system that launched and recovered a remotely-piloted UAV from a UGV and performed automated refueling of a UAV mockup.