Decision making in a robotic architecture for autonomy
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robot autonomy robotic architecture planning sched↗
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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↗
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↗
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↗
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↗