Decision-making in a robotic architecture for autonomy
This paper presents an overview of the intelligent decison-making capabilities of the CLARAty robotic architecture for autonomy.
autonomy robotics planning scheduling execution↗
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Decision making in a robotic architecture for autonomy
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robot autonomy robotic architecture planning sched↗
CLEaR: Closed Loop Execution and Recovery High-Level Onboard Autonomy for Rover Operations
This viewgraph presentation covers the following topics: Coupled Layer Architecture for Robotic Autonomy CLARAty system at the functional layer and the decision layer; and CLEaR - AI Planning and Schedule - Task Based Control, A demonstration is of the operation of the system is given, with viewgraphs of the rover movement control.
planning↗
Artificial Intelligence: Powering Human Exploration of the Moon and Mars
Artificial Intelligence (AI) is a growing field of computa- tional science techniques designed to mimic functions per- formed by people. Advancements in autonomy will depend on a portfolio of AI technologies. Automated planning and scheduling is a venerable field of study in AI, and is needed for a variety of mission planning functions. Plan execution technology is less well studied, but important for auton- omy and robotics. Specialized forms of automated reason- ing and machine learning are key technologies to enable fault management. Over the past decade, the NASA Au- tonomous Systems and Operations (ASO) project has devel- oped and demonstrated numerous autonomy enabling tech- nologies employing AI techniques. Our work has employed AI in three distinct ways to enable autonomous mission op- erations capabilities. Crew Autonomy gives astronauts tools to assist in the performance of each of these mission oper-ations functions. Vehicle System Management uses AI tech- niques to turn the astronaut's spacecraft into a robot, allow- ing it to operate when astronauts are not present, or to reduce astronaut workload. AI technology also enables Autonomous Robots as crew assistants or proxies when the crew are not present. When these capabilities are used to enable astro- nauts to operate autonomously, they must be integrated with user interfaces, introducing numerous human factors con- siderations; when these capabilities are used to enable vehi- cle system management, they must be integrated with flight software, and run on embedded processors under the control of real-time operating systems.We first describe human spaceflight mission operations capabilities. The remainder of the paper will describe the ASO project, and the development and demonstration per- formed by ASO since 2011. We will describe the AI tech- niques behind each of these demonstrations, which include a variety of symbolic automated reasoning and machine learn- ing based approaches. Finally, we conclude with an assess- ment of future development needs for AI to enable NASA's future Exploration missions.
Mission Operations↗
Current results from a Rover Science Data Analysis System
In this paper, we provide a brief overview of the OASIS system, and then describe our recent successes in integrating with and using rover hardware. OASIS currently works in a closed loop fashion with onboard control software (e.g., navigation and vision) and has the ability to autonomously perform the following sequence of steps: analyze gray scale images to find rocks, extract the properties of the rocks, identify rocks of interest, retask the rover to take additional imagery of the identified target and then allow the rover to continue on its original mission. We also describe the early 2004 ground test validation of specific OASIS components on selected Mars Exploration Rover (MER) images. These components include the rockfinding algorithm, RockIT, and the rock size feature extraction code. Our team also developed the RockIT GUI, an interface that allows users to easily visualize and modify the rock-finder results. This interface has allowed us to conduct preliminary testing and validation of the rockfinder's performance.
Coupled Layer Architecture for Robotic Autonomy (C↗
FRESCO: A Framework for Spacecraft Systems Autonomy
Achieving the science exploration and defense goals of the following decades will require flight systems capable of operations with limited operator contact, system mode changes and retasking based on sensor data, and complex robotic operations. To support these capabilities, increasingly autonomous flight systems are required that can perform dedicated mission functions, e.g. payload targeting and communications, and system-level functions, e.g. planning and goal monitoring. Architecting an autonomous system requires a well-reasoned, self-consistent framework to avoid \textit{ad hoc} design choices that will introduce complexity and risk. The Framework for Robust Execution and Scheduling of Commands On-Board, FRESCO, is the result of lessons learned in developing a software architecture to enable autonomous solar system exploration. FRESCO generalizes this work to offer a modular, software-agnostic approach to developing verifiable architecture for autonomous space systems. FRESCO specifies guiding principles, functions, interfaces, and interactions from which mission-specific autonomous control architectures can be derived. FRESCO is a principled framework relying on explicit, state-based goal definitions, centralized management of state knowledge, clearly separated control boundaries, and hierarchical reasoning. Using components from FRESCO reference architecture, an autonomous decision-making architecture can be designed for spacecraft which can then be mapped to flight software architecture. FRESCO is flexibly defined to enable autonomous control of flight systems built using extensive software and hardware heritage. Finally, FRESCO-derived architectures support a spectrum of operator/spacecraft interactions, ranging from traditional commanding to goal-driven commanding with the ability to change mission goals autonomously. FRESCO has been used in defining the autonomy architectures for the ASTERIA mission and have been demonstrated in laboratory and software simulation for small body rendezvous and in-space servicing missions.
Kolcio, Ksenia↗
An Approach to Autonomous Operations for Remote Mobile Robotic Exploration
This viewgraph presentation addresses the use of autonomy for remote mobile robotic exploration. The contents include; 1) Why Use Autonomy?; 2) What Are Some Options? JPL (Reasoning); 3) More Options.. . (Modeling); 4) The CLEaR Control System (Closed Loop Execution and Recovery); 5) Method of Response; 6) Overall Goal; 7) CLEaR in Action; 8) Initial Scenario; 9) Initial Scenario - Planned; 10) Unforeseen Events; and 11) Ongoing Research.
rovers↗
Robotic technologies of the Flight Telerobotic Servicer (FTS) including fault tolerance
The original FTS concept for Space Station Freedom (SSF) was to provide telerobotic assistance to enhance crew activity and safety and to reduce crew EVA (Extra Vehicular Activity) activity. The first flight of the FTS manipulator systems would demonstrate several candidate tasks and would verify manipulator performance parameters. These first flight tasks included unlocking a SSF Truss Joint, mating/demating a fluid coupling, contact following of a contour board, demonstrating peg-in-hole assembly, and grasping and moving a mass. Future tasks foreseen for the FTS system included ORU (Orbit Replaceable Unit) change-out, Hubble Space Telescope Servicing, Gamma Ray Observatory refueling, and several in-situ SSF servicing and maintenance tasks. Operation of the FTS was planned to evolve from teleoperation to fully autonomous execution of many tasks. This wide range of mission tasks combined with the desire to evolve toward fully autonomy forced several requirements which may seen extremely demanding to the telerobotics community. The FTS requirements appear to have been created to accommodate the open-ended evolution plan such that operational evolution would not be impeded by function limitations. A recommendation arising from the FTS program to remedy the possible impacts from such ambitious requirements is to analyze candidate robotic tasks. Based on these task analyses, operational impacts against development impacts were weighed prior to requirements definition. Many of the FTS requirements discussed in the following sections greatly influenced the development cost and schedule of the FTS manipulator. The FTS manipulator has been assembled at Martin Marietta and is currently in testing. Successful component tests indicate a manipulator which achieves unprecedented performance specifications.
Chladek, John T.↗
Autonomous Rovers for Mars Exploration
Rovers will play a critical role in the exploration of Mars. Near-term mission plans call for long traverses over unknown terrain, robust navigation and instrument placement, and reliable operations for extended periods of time. Longer-term missions may visit multiple science sites in a single day and perform opportunistic science data collection, as well as complex scouting, construction, and maintenance tasks in preparation for an eventual human presence. The Pathfinder mission demonstrated the potential for robotic Mars exploration but at the same time indicated the need for more rover autonomy. The highly ground-intensive control with infrequent communication and high latency limited the effectiveness of the Sojourner rover. When failures occurred, Sojourner often sat idle for extended periods of time, awaiting further commands from earth. In future missions, the tasks will be more complex and extended; hence there will be even more situations where things do not go exactly as planned. Significant advances in rover autonomy are needed to cope with increasing task complexity and greater execution uncertainty. Towards this end, we have designed an on-board executive architecture that incorporates robust operation, resource utilization, and failure recovery. In addition, we have designed ground tools to produce and refine contingent schedules that take advantage of the on-board architecture's flexible execution characteristics. Together, the on-board executive and the ground tools constitute an integrated rover autonomy architecture. This work draws from our experience with the Deep Space One autonomy experiment, with enhancements to ensure robust operation in the face of the unpredictable, complex environment that the rover will encounter on Mars. The rover autonomy architecture is currently being developed and deployed on the Marsokhod rover platform at NASA Ames Research Center. The capabilities of the rover autonomy architecture to support autonomous operations will be demonstrated concretely in upcoming field tests.
Anderson, Corin↗
Spatial Coverage Planning and Optimization for Planetary Exploration
We are developing onboard planning and scheduling technology to enable in situ robotic explorers, such as rovers and aerobots, to more effectively assist scientists in planetary exploration. In our current work, we are focusing on situations in which the robot is exploring large geographical features such as craters, channels or regional boundaries. In to develop valid and high quality plans, the robot must take into account a range of scientific and engineering constraints and preferences. We have developed a system that incorporates multiobjective optimization and planning allowing the robot to generate high quality mission operations plans that respect resource limitations and mission constraints while attempting to maximize science and engineering objectives. An important scientific objective for the exploration of geological features is selecting observations that spatially cover an area of interest. We have developed a metric to enable an in situ explorer to reason about and track the spatial coverage quality of a plan. We describe this technique and show how it is combined in the overall multiobjective optimization and planning algorithm.
autonomy↗