The Autonomous Exploration for Gathering Increased Science (AEGIS) system enables automated data collection by planetary rovers. AEGIS software was uploaded to the Mars Exploration Rover (MER) mission’s Opportunity rover in December 2009 and has successfully demonstrated automated onboard targeting based on scientist-specified objectives. Prior to AEGIS, images were transmitted from the rover to the operations team on Earth; scientists manually analyzed the images, selected geological targets for the rover’s remote-sensing instruments, and then generated a command sequence to execute the new measurements. AEGIS represents a significant paradigm shift---by using onboard data analysis techniques, the AEGIS software uses scientist input to select high-quality science targets with no human in the loop. This approach allows the rover to autonomously select and sequence targeted observations in an opportunistic fashion, which is particularly applicable for narrow field-of-view instruments (such as the MER Mini-TES spectrometer, the MER Panoramic camera, and the 2011 Mars Science Laboratory (MSL) ChemCam spectrometer). This article provides an overview of the AEGIS automated targeting capability and describes how it is currently being used onboard the MER mission Opportunity rover.

We are developing onboard planning and execution technologies to support the exploration and characterization of geological features by autonomous rovers. In order to generate high quality mission plans, an autonomous rover must reason about the relative importance of the observations it can perform. In this paper we look at the scientific criteria of selecting observations that improve the quality of the area covered by samples. Our approach makes use of a priori information, if available, and allows scientists to mark sub-regions of the area with relative priorities for exploration. We use an efficient algorithm for prioritizing observations based on spatial coverage that allows the system to update observation rankings as new information is gained during execution.

This paper presents technology for performing autonomous commanding of a planetary rover. Through the use of AI planning, scheduling and execution techniques, the OASIS autonomous science system provides capabilities for the automated generation of a rover activity plan based on science priorities, the handling of opportunistic science, including new science targets identified by onboard data analysis software, other dynamic decision-making such as modifying the rover activity plan in response to problems or other state and resource changes. We first describe some of the particular challenges this work has begun to address and then describe our system approach. Finally, we report on our experience testing this software with a Mars rover prototype.

Anyone who has ever driven a car on an icy road is aware of the impact the environment can have on our actions. In order to build effective plans, we must be aware of these environmental conditions and predict the effects they will have on our ability to act. We present an application of regression trees that allows a robot to learn action models through experience so that it can make similar predictions. We use this approach to allow a mobile robot to learn models to predict the effects of its navigation actions under various terrain conditions and use them in order to produce efficient plans.

Computer-aided process planning (CAPP) tools have the potential for assisting designers and manufacturing engineers in producing high-quality, low-cost products. AI planning research offers promising technologies for the development of such tools. However, current planning models limit the role that the planner can play in problem solving. Designers and manufacturers do more than simply generate process plans, they re-evaluate design decisions and design new tools to improve the manufacturability of a product. To assist in this effort, we need planners that understand the relationships between design and planning, and can suggest modifications to the part or to the tools used to create parts. To this end, we present a model for integrating planning and design. This model allows a planner to suggest changes to a problem description if it cannot solve the problem or to suggest new tools that will enable it to solve the problem in its original form. Thus, the model allows planners to take a more active role in the design process. We illustrate the use of this model by describing a system we have implemented which generates re-design suggestions to improve the manufacturability of machined parts.

This paper presents Coordinator, a robust, resource adaptive process planner for CNC machining centers that uses a blend of geometric and manufacturing information to detect a broad variety of manufacturing interactions and create setups plans. This system is designed to be used by expert users to reduce the time and effort required to produce good plans. One of Coordinator's contributions is the ability to detect fixture-feature interactions, interferences between manufacturing operations in which one operation destroys the clamping surfaces required by another. These are an important class of interactions, which have a major (if not the major) impact on ordering setup plans. However, they are rarely addressed in the literature, mainly because traditional process planning organizations do not compute the information required to detect them at the right time. Fixturing information, which is needed for identifying feature-fixture interactions, is usually computed after all plan sequencing has been done. Coordinator is part of a large system, MAPP, which reorganized the tasks in process planning by computing the required abstract fixturing information prior to sequencing so that Coordinator can detect a wide variety of interaction types, including feature-fixture interactions. Coordinator represents an important advance in making practical, robust, and flexible manufacturing planners.