Using Continuous Planning Techniques to Coordinate Multiple Rovers
This paper describes a dynamic planning system for coordinating multiple rovers in collecting planetary surface data.
Engineering topics
Publications and source records attributed to Chien, S..
This paper describes a dynamic planning system for coordinating multiple rovers in collecting planetary surface data.
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 (i.e., 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.
This paper describes a dynamic planning system for coordinating multiple rovers in collecting planetary surface data.
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 resource margins.
This paper describes an integrated system for coordinating multiple rover behavior with the overall goal of collecting planetary surface data.
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.
The paper presents a rover execution architecture for controlling multiple, cooperating rovers. The overall goal of this architecture is to coordinate multiple rovers in performing complex tasks for planetary science.
This paper considers the problem of learning the ranking of a set of stochastic alternatives based upon incomplete information (i.e., a limited number of samples).
This paper describes an architecture for an autonomous deep space tracking station (DS-T).
This paper describes the application of Artificial Intelligence planning techniques to the problem of antenna track plan generation for a NASA Deep Space communications Station.
Automated planning and scheduling, including automated path planning, has been integrated with an internet-based distributed operations system for planetary rover operations.
In this paper, we describe how rover command generation can be automated to help relieve some of the burden on human operators.
This paper describes the application of Artificial Intelligence planning techniques to the problem of antenna track plan generation for a NASA Deep Space Communications Station.
This paper describes an architecture for an autonomous Deep Space Tracking Station (DS-T).
In recent times, improvements in imaging technology have made available an incredible array of information in image format.
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.
Recently, significant advances have been made in enabling autonomous rovers and robotic vehicles.
In recent times, improvements in imaging technology have made available an incredible array of information in image format.