A software architecture for hard real-time execution of automatically synthesized plans or control laws

The design of a flexible, real-time software architecture for trajectory planning and automatic control of redundant manipulators is described. Emphasis is placed on a technique of designing control systems that are both flexible and robust yet have good real-time performance. The solution presented involves an artificial intelligence algorithm that dynamically reprograms the real-time control system while planning system behavior

Using Visual Odometry to Estimate Position and Attitude

A computer program in the guidance system of a mobile robot generates estimates of the position and attitude of the robot, using features of the terrain on which the robot is moving, by processing digitized images acquired by a stereoscopic pair of electronic cameras mounted rigidly on the robot. Developed for use in localizing the Mars Exploration Rover (MER) vehicles on Martian terrain, the program can also be used for similar purposes on terrestrial robots moving in sufficiently visually textured environments: examples include low-flying robotic aircraft and wheeled robots moving on rocky terrain or inside buildings. In simplified terms, the program automatically detects visual features and tracks them across stereoscopic pairs of images acquired by the cameras. The 3D locations of the tracked features are then robustly processed into an estimate of overall vehicle motion. Testing has shown that by use of this software, the error in the estimate of the position of the robot can be limited to no more than 2 percent of the distance traveled, provided that the terrain is sufficiently rich in features. This software has proven extremely useful on the MER vehicles during driving on sandy and highly sloped terrains on Mars

The SuperCam Instrument Suite on the Mars 2020 Rover: Science Objectives and Mast-Unit Description

On the NASA 2020 rover mission to Jezero crater, the remote determination of the texture, mineralogy and chemistry of rocks is essential to quickly and thoroughly characterize an area and to optimize the selection of samples for return to Earth. As part of the Perseverance payload, SuperCam is a suite of five techniques that provide critical and complementary observations via Laser-Induced Breakdown Spectroscopy (LIBS), Time-Resolved Raman and Luminescence (TRR/L), visible and near-infrared spectroscopy (VISIR), high-resolution color imaging (RMI), and acoustic recording (MIC). SuperCam operates at remote distances, primarily 2-7 m, while providing data at sub-mm to mm scales. We report on SuperCam's science objectives in the context of the Mars 2020 mission goals and ways the different techniques can address these questions. The instrument is made up of three separate subsystems: the Mast Unit is designed and built in France; the Body Unit is provided by the United States; the calibration target holder is contributed by Spain, and the targets themselves by the entire science team. This publication focuses on the design, development, and tests of the Mast Unit; companion papers describe the other units. The goal of this work is to provide an understanding of the technical choices made, the constraints that were imposed, and ultimately the validated performance of the flight model as it leaves Earth, and it will serve as the foundation for Mars operations and future processing of the data.In France was provided by the Centre National d'Etudes Spatiales (CNES). Human resources were provided in part by the Centre National de la Recherche Scientifique (CNRS) and universities. Funding was provided in the US by NASA's Mars Exploration Program. Some funding of data analyses at Los Alamos National Laboratory (LANL) was provided by laboratory-directed research and development funds

Representation and automatic synthesis of reaction plans

Rather than committing to a particular future course of events, reaction plans prescribe reactions to be used if various situations should occur. In this respect reaction plans are closely related to both production systems and process control programs. The "Universal Plans" representation is an extreme member of the class of reaction plans, making no assumptions whatever about the future. This Dissertation introduces Universal Plans and shows how they can be automatically constructed from a declarative description of the effects of individual actions.Even before there has been any planning, the Universal Plans executor will be able--in most situations, and with searching--to find something to do next. Of course, the reaction suggested by such an "unplanned plan" might be both too late and seriously wrong. Universal Plans at this stage of inexperience might be regarded as "muddling along". The planner solves search problems and avoids pitfalls for the executor by adding new rules to the rulebase initially received as the domain description. The planner's additions are best regarded as advice for the plan executor.Because Universal Plans are highly conditional, almost all the effects of any plan are conditional. Further, the conditional effects of plans can be conveniently controlled by limiting the circumstances in which a plan is allowed to execute. This possibility gives rise to a new planning technique I call "confinement" that is capable of solving even mutual goal conflicts without interleaving the goal trees of the interacting subplans. This has several important consequences. (1) Subplans can now be reused without modification: a plan that achieves P $\wedge$ Q utilizes in a straightforward way the subplans that achieve P and Q separately. Thus the Universal Plans representation is also a programming language with an effect-preserving composition construct. (2) Because Universal Plans are reactive rather than predictive, the planner is liberated from part of the classical frame problem (the part recently renamed the "persistence" or "inertia" problem).U of I OnlyETDs are only available to UIUC Users without author permissio

Reactive Combination of Belief Over Time Using Direct Perception

One issue for autonomous mobile robots operating in unknown, or partially known, domains is how to handle uncertainty in their sensor observations over time. Methods such as probablistic belief networks and survivor functions are generally unsatisfactory because they require explicit models of the robot's interactions with its environment, including possible contravening events. This information is difficult to obtain, and is philosophically incompatible with reactive behaviors. This paper presents an approach which eliminates the need for explicit models and reasoning; instead, it relies solely on directly perceivable attributes of the robot, object, and environment. The attributes qualitatively rate whether the robot's current observations are from an inherently more informed state than previous readings (e.g., from a better viewpoint) . Observations from more informed states have different rates for the accrual and attrition of belief than those taken from less informed states. This..