Terry Huntsberger

Stereo vision–based navigation for autonomous surface vessels

This paper describes a stereo vision–based system for autonomous navigation in maritime environments. The system consists of two key components. The Hammerhead vision system detects geometric hazards (i.e., objects above the waterline) and generates both grid-based hazard maps and discrete contact lists (objects with position and velocity). The R4SA (robust, real-time, reconfigurable, robotic system architecture) control system uses these inputs to implement sensor-based navigation behaviors, including static obstacle avoidance and dynamic target following. As far as the published literature is concerned, this stereo vision–based system is the first fielded system that is tailored for high-speed, autonomous maritime operation on smaller boats. In this paper, we present a description and experimental analysis of the Hammerhead vision system, along with key elements of the R4SA control system. We describe the integration of these systems onto a number of high-speed unmanned surface vessels and present experimental results for the combined vision-based navigation system.

Closed loop control for autonomous approach and placement of science instruments by planetary rovers

The underlying motive of follow the water in the search for evidence of past or present life on Mars has led NASA to deploy increasingly sophisticated robotic missions to the planetary surface. Opportunity and Spirit, the current pair of MER (Mars Exploration Rovers) on Mars for over a year, have both discovered evidence of past surface water. Optimal use of mission resources, such as ground planning time and surface operation duration, for increased science data return becomes critical with each advancement in the capabilities of the onboard instrument suites. This paper presents a novel, end-to-end, fully integrated system being developed at JPL (Jet Propulsion Laboratory) that is called SCAIP (Single Command Approach and Instrument Placement). SCAIP enables a rover to autonomously travel to a designated science target from an extended distance away, and precisely place an instrument on that target with a single command without additional human interaction. The results of some experimental studies with a rover in terrestrial settings and using imagery returned from MER are also described.

TRESSA: Teamed robots for exploration and science on steep areas

Long-duration robotic missions on lunar and planetary surfaces (for example, the Mars Exploration Rovers have operated continuously on the Martian surface for close to 3 years) provide the opportunity to acquire scientifically interesting information from a diverse set of surface and subsurface sites and to explore multiple sites in greater detail. Exploring a wide range of terrain types, including plains, cliffs, sand dunes, and lava tubes, requires the development of robotic systems with mobility enhanced beyond that which is currently fielded. These systems include single as well as teams of robots. TRESSA (Teamed Robots for Exploration and Science on Steep Areas) is a closely coupled three-robot team developed at the Jet Propulsion Laboratory (JPL) that previously demonstrated the ability to drive on soil-covered slopes up to 70 deg. In this paper, we present results from field demonstrations of the TRESSA system in even more challenging terrain: rough rocky slopes of up to 85 deg. In addition, the integration of a robotic arm and instrument suite has allowed TRESSA to demonstrate semi-autonomous science investigation of the cliffs and science sample collection. TRESSA successfully traversed cliffs and collected samples at three Mars analog sites in Svalbard, Norway as part of a recent geological and astrobiological field investigation called AMASE: Arctic Mars Analog Svalbard Expedition under the NASA ASTEP (Astrobiology Science and Technology for Exploring Planets) program.

Intelligent autonomy for unmanned surface and underwater vehicles

As the Autonomous Underwater Vehicle (AUV) and Autonomous Surface Vehicle (ASV) platforms mature in endurance and reliability, a natural evolution will occur towards longer, more remote autonomous missions. This evolution will require the development of key capabilities that allow these robotic systems to perform a high level of on-board decision-making, which would otherwise be performed by human operators. With more decision making capabilities, less a priori knowledge of the area of operations would be required, as these systems would be able to sense and adapt to changing environmental conditions, such as unknown topography, currents, obstructions, bays, harbors, islands, and river channels. Existing vehicle sensors would be dual-use; that is they would be utilized for the primary mission, which may be mapping or hydrographic reconnaissance; as well as for autonomous hazard avoidance, route planning, and bathymetric-based navigation. This paper describes a tightly integrated instantiation of an autonomous agent called CARACaS (Control Architecture for Robotic Agent Command and Sensing) developed at JPL (Jet Propulsion Laboratory) that was designed to address many of the issues for survivable ASV/AUV control and to provide adaptive mission capabilities. The results of some on-water tests with US Navy technology test platforms are also presented.

Using arm and hand gestures to command robots during stealth operations

Command of support robots by the warfighter requires intuitive interfaces to quickly communicate high degree-offreedom (DOF) information while leaving the hands unencumbered. Stealth operations rule out voice commands and vision-based gesture interpretation techniques, as they often entail silent operations at night or in other low visibility conditions. Targeted at using bio-signal inputs to set navigation and manipulation goals for the robot (say, simply by pointing), we developed a system based on an electromyography (EMG) BioSleeve, a high density sensor array for robust, practical signal collection from forearm muscles. The EMG sensor array data is fused with inertial measurement unit (IMU) data. This paper describes the BioSleeve system and presents initial results of decoding robot commands from the EMG and IMU data using a BioSleeve prototype with up to sixteen bipolar surface EMG sensors. The BioSleeve is demonstrated on the recognition of static hand positions (e.g. palm facing front, fingers upwards) and on dynamic gestures (e.g. hand wave). In preliminary experiments, over 90% correct recognition was achieved on five static and nine dynamic gestures. We use the BioSleeve to control a team of five LANdroid robots in individual and group/squad behaviors. We define a gesture composition mechanism that allows the specification of complex robot behaviors with only a small vocabulary of gestures/commands, and we illustrate it with a set of complex orders.

Envisioning Cognitive Robots for Future Space Exploration

Cognitive robots in the context of space exploration are envisioned with advanced capabilities of model building, continuous planning/re-planning, self-diagnosis, as well as the ability to exhibit a level of understanding of new situations. An overview of some JPL components (e.g. CASPER, CAMPOUT) and a description of the architecture CARACaS (Control Architecture for Robotic Agent Command and Sensing) that combines these in the context of a cognitive robotic system operating in a various scenarios are presented. Finally, two examples of typical scenarios of a multi-robot construction mission and a human-robot mission, involving direct collaboration with humans is given.

Applied soft computing strategies for autonomous field robotics

This chapter addresses computing strategies designed to enable field mobile robots to execute tasks requiring effective autonomous traversal of natural outdoor terrain. The primary focus is on computer vision-based perception and autonomous control. Hard computing methods are combined with applied soft computing strategies in the context of three case studies associated with real-world robotics tasks including planetary surface exploration and land survey or reconnaissance. Each case study covers strategies implemented on wheeled robot research prototypes designed for field operations.

Autonomous maritime navigation: developing autonomy skill sets for USVs

Many emerging UV (Unmanned Vehicle) cooperative control systems utilizing mission decomposition and generic UV management techniques are UAV (Unmanned Aerial Vehicle) oriented and transition well from model simulations to hardware due to the relative homogeneity of the air environment. Unmanned Surface Vehicles (USVs) and other ground borne vehicles, to function robustly, must have an additional onboard capacity to negotiate local environmental and indigenous operational factors in order to be commanded by a network, and this capacity is most easily delineated into Skill Sets. The Autonomous Maritime Navigation Program (AMN) is developing USV systems which target full intelligent autonomous operations and autonomy Skill Sets to allow USVs to perform unsupervised complex missions over extended time periods at the platform level with minimum human supervision. Importantly, this allows control systems developed for cooperating UVs to effectively control USVs by enabling local platform issues decision making at the platform level. Using a 40 foot laboratory boat, advanced on-board control, sensing, data fusion, physical plant and payload monitoring and management are being adapted and integrated as a system to replace traditional human crew functions. This paper discusses a path to achieve the goal of full USV autonomy equipped with skills to self manage, survive and navigate, and progress being made with enabling technology pieces. Initiatives and partnerships have been formed with academia, industry, and other DoD laboratories to these ends in both independent and collaborative RDT&E projects. Discussion includes ongoing work in sensing, data fusion, dynamic mission planning, execution and boat operations, and integration to JAUS/TCS control protocols.

Game theory basis for control of long-lived lunar/planetary surface robots

Current and future NASA robotic missions to planetary surfaces are tending toward longer duration and are becoming more ambitious for rough terrain access. For a higher level of autonomy in such missions, the rovers will require behavior that must also adapt to declining health and unknown environmental conditions. The MER (Mars Exploration Rovers) called Spirit and Opportunity have both passed 600 days of life on the Martian surface, with extensions to 1000 days and beyond depending on rover health. Changes in navigational planning due to degradation of the drive motors as they reach their lifetime are currently done on Earth for the Spirit rover. The upcoming 2009 MSL (Mars Science Laboratory) and 2013 AFL (Astrobiology Field Laboratory) missions are planned to last 300–500 days, and will possibly involve traverses on the order of multiple kilometers over challenging terrain. This paper presents a unified coherent framework called SMART (System for Mobility and Access to Rough Terrain) that uses game theoretical algorithms running onboard a planetary surface rover to safeguard rover health during rough terrain access. SMART treats rover motion, task planning, and resource management as a Two Person Zero Sum Game (TPZSG), where the rover is one player opposed by the other player called “nature” representing uncertainty in sensing and prediction of the internal and external environments. We also present preliminary results of some field studies.