Russell Knight

Bio: Russell Knight is an academic researcher from California Institute of Technology. The author has contributed to research in topics: Automated planning and scheduling & Space exploration. The author has an hindex of 15, co-authored 45 publications receiving 955 citations.

Using Iterative Repair to Improve the Responsiveness of Planning and Scheduling

Abstract: The majority of planning and scheduling research has focused on batch-oriented models of planning. This paper discusses the use of iterative repair techniques to support a continuous planning process as is appropriate for autonomous spacecraft control. This allows the plan to incorporate execution feedback - such as early or late completion of activities, and over-use or under-use of resources. In this approach, iterative repair supports continuous modification and updating of a current working plan in light of changing operating context. .

Iterative Repair Planning for Spacecraft Operations Using the Aspen System

Abstract: This paper describes the Automated Scheduling and Planning Environment (ASPEN) ASPEN encodes complex spacecraft knowledge of operability constraints, flight rules, spacecraft hardware, science experiments and operations procedures to allow for automated generation of low level spacecraft sequences Using a technique called iterative repair, ASPEN classifies constraint violations (ie, conflicts) and attempts to repair each by performing a planning or scheduling operation It must reason about which conflict to resolve first and what repair method to try for the given conflict ASPEN is currently being utilized in the development of automated planner/scheduler systems for several spacecraft, including the UFO-1 naval communications satellite and the Citizen Explorer (CX1) satellite, as well as for planetary rover operations and antenna ground systems automation This paper focuses on the algorithm and search strategies employed by ASPEN to resolve spacecraft operations constraints, as well as the data structures for representing these constraints

Using Iterative Repair to Increase the Responsiveness of Planning and Scheduling for Autonomous Spacecraft

Abstract: An autonomous spacecraft must balance long-term and short-term considerations. It must perform purposeful activities that ensure long-term science and engineering goals are achieved and ensure that it maintains positive resource margins. This requires planning in advance to avoid a series of shortsighted decisions that can lead to failure, However, it must also respond in a timely fashion to a somewhat dynamic and unpredictable environment. Thus, spacecraft plans must often be modified due to fortuitous events such as early completion of observations and setbacks such as failure to acquire a guidestar for a science observation. This paper describes the use of iterative repair to support continuous modification and updating of a current working plan in light of changing operating context.

ASPEN-Automated Planning and Scheduling for Space Mission Operation

Abstract: This paper describes the ASPEN system for automation of planning and scheduling for space mission operations

Integrated planning and execution for autonomous spacecraft

Abstract: An autonomous spacecraft must balance long-term and short-term considerations. It must perform purposeful activities that ensure long-term science and engineering goals are achieved and ensure that it maintains positive resource margins. This requires planning in advance to avoid a series of shortsighted decisions that can lead to failure. However, it must also respond in a timely fashion to a somewhat dynamic and unpredictable environment. Thus, in terms of high-level, goal-oriented activity, spacecraft plans must often be modified due to fortuitous events such as early completion of observations and setbacks such as failure to acquire a guidestar for a science observation. This paper describes an integrated planning and execution architecture that supports continuous modification and updating of a current working plan in light of changing operating context.

Automated Planning and Acting

Abstract: Autonomous AI systems need complex computational techniques for planning and performing actions. Planning and acting require significant deliberation because an intelligent system must coordinate and integrate these activities in order to act effectively in the real world. This book presents a comprehensive paradigm of planning and acting using the most recent and advanced automated-planning techniques. It explains the computational deliberation capabilities that allow an actor, whether physical or virtual, to reason about its actions, choose them, organize them purposefully, and act deliberately to achieve an objective. Useful for students, practitioners, and researchers, this book covers state-of-the-art planning techniques, acting techniques, and their integration which will allow readers to design intelligent systems that are able to act effectively in the real world.

The CLARAty architecture for robotic autonomy

Abstract: This paper presents an overview of a newly developed Coupled Layer Architecture for Robotic Autonomy (CLARAty), which is designed for improving the modularity of system software while more tightly coupling the interaction of autonomy and controls. First, we frame the problem by briefly reviewing previous work in the field and describing the impediments and constraints that been encountered. Then we describe why a fresh approach to the topic is warranted, and introduce our new two-tiered design as an evolutionary modification of the conventional three-level robotics architecture. The new design features a tight coupling of the planner and executive in one Decision Layer, which interacts with a separate Functional Layer at all levels of system granularity. The Functional Layer is an object-oriented software hierarchy that provides basic capabilities of system operation, resource prediction, state estimation, and status reporting. The Decision Layer utilizes these capabilities of the Functional Layer to achieve goals by expanding, ordering, initiating and terminating activities. Both declarative and procedural planning methods are used in this process. Current efforts are targeted at implementing an initial version of this architecture on our research Mars rover platforms, Rocky 7 and 8. In addition, we are working with the NASA robotics and autonomy communities to expand the scope and participation in this architecture, moving toward a flight implementation in the 2007 time-frame.

Using Iterative Repair to Improve the Responsiveness of Planning and Scheduling

Abstract: The majority of planning and scheduling research has focused on batch-oriented models of planning. This paper discusses the use of iterative repair techniques to support a continuous planning process as is appropriate for autonomous spacecraft control. This allows the plan to incorporate execution feedback - such as early or late completion of activities, and over-use or under-use of resources. In this approach, iterative repair supports continuous modification and updating of a current working plan in light of changing operating context. .

Using Autonomy Flight Software to Improve Science Return on Earth Observing One

Abstract: NASA’s Earth Observing One Spacecraft (EO-1) has been adapted to host an advanced suite of onboard autonomy software designed to dramatically improve the quality and timeliness of science-data returned from remote-sensing missions. The Autonomous Sciencecraft Experiment (ASE) enables the spacecraft to autonomously detect and respond to dynamic scientifically interesting events observed from EO-1’s low earth orbit. ASE includes software systems that perform science data analysis, mission planning, and runtime robust execution. In this article we describe the autonomy flight software, as well as innovative solutions to the challenges presented by autonomy, reliability, and limited computing resources.