Human-Robot Team Task Scheduling for Planetary Surface Missions

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/77042/1/AIAA-2007-2972-351.pd

System of Terrain Analysis, Energy Estimation and Path Planning for Planetary Exploration by Robot Teams

NASA’s long term plans involve a return to manned moon missions, and eventually sending humans to mars. The focus of this project is the use of autonomous mobile robotics to enhance these endeavors. This research details the creation of a system of terrain classification, energy of traversal estimation and low cost path planning for teams of inexpensive and potentially expendable robots. The first stage of this project was the creation of a model which estimates the energy requirements of the traversal of varying terrain types for a six wheel rocker-bogie rover. The wheel/soil interaction model uses Shibly’s modified Bekker equations and incorporates a new simplified rocker-bogie model for estimating wheel loads. In all but a single trial the relative energy requirements for each soil type were correctly predicted by the model. A path planner for complete coverage intended to minimize energy consumption was designed and tested. It accepts as input terrain maps detailing the energy consumption required to move to each adjacent location. Exploration is performed via a cost function which determines the robot’s next move. This system was successfully tested for multiple robots by means of a shared exploration map. At peak efficiency, the energy consumed by our path planner was only 56% that used by the best case back and forth coverage pattern. After performing a sensitivity analysis of Shibly’s equations to determine which soil parameters most affected energy consumption, a neural network terrain classifier was designed and tested. The terrain classifier defines all traversable terrain as one of three soil types and then assigns an assumed set of soil parameters. The classifier performed well over all, but had some difficulty distinguishing large rocks from sand. This work presents a system which successfully classifies terrain imagery into one of three soil types, assesses the energy requirements of terrain traversal for these soil types and plans efficient paths of complete coverage for the imaged area. While there are further efforts that can be made in all areas, the work achieves its stated goals

ABSTRACT Coordinating Multiple Rovers with Interdependent Science Objectives

This paper describes an integrated system for coordinating multiple rover behavior with the overall goal of collecting planetary surface data. The MISUS system combines techniques from planning and scheduling with machine learning to perform autonomous scientific exploration with cooperating rovers. A distributed planning and scheduling approach is used to generate efficient, multi-rover coordination plans, monitor plan execution, and perform re-planning when necessary. A machine learning clustering component is used to deduce geological relationships among collected data and select new science activities. A key concept promoted by this system is the use of goal interdependency information to perform plan optimization and increase the value of collected science data. We discuss how we represent and reason about goal dependency and utility information in our planning system and explain how this information can change dynamically during system use. We show through experimental results that our approach significantly increases overall plan quality versus a standard approach that treats goal utilities independently. Categories and Subject Descriptors I.2.11 [Distributed Artificial Intelligence]: Coherence and coordination, intelligent agents and multiagent systems