Mission Planning Life in the Atacama 2004 Science & Technology Workshop Paul Tompkins Carnegie Mellon

Life in the Atacama 2004 Workshop1Carnegie Mellon Overview Role of Mission Planning Problem Example Module Descriptions Off-board and On-board Architectures Operations Concept Inputs and Outputs Behaviors and Limitations Conclusions

Life in the Atacama 2004 Workshop2Carnegie Mellon Problem How will the science team plan the daily route and event sequence? How will the robot update the plan as conditions change? Planning considerations: Science objectives Desired locations and measurements Rover constraints Terrain limitations, power, daytime ops, sun blinding Operational constraints Time limitations (e.g. daylight, data analysis)

Life in the Atacama 2004 Workshop3Carnegie Mellon Example Planning “Sol 3”: Initial position known Sol 4 destination known Team designates list of desired Sol 3 science activities How to determine… Whether the activities are achievable in Sol 3? The plan route and timing? The plan that satisfies constraints? Rover Wakeup, Sol 3 Panorama Full Survey Panorama/ Overnight Hibernation Sol 4 Investigation

Life in the Atacama 2004 Workshop4Carnegie Mellon Mission Planning Modules LITA mission planning utilizes two modules: Goal Manager (GM) Mission Planner (MP) Each module operates offboard and onboard the rover

Life in the Atacama 2004 Workshop5Carnegie Mellon Mission Planner (MP) Role: Solves for high-level plans that Achieve all goals Satisfy all operational constraints Are optimal in combined terms of energy and plan length Approach: TEMPEST planner World Model Terrain Ephemeris Solar Flux Line-of-Sight Rover Model Locomotion Power Sensors Pointing geometry Constraint Set Time Bounds Position Bounds Geometric Limitations Action Set Drive Battery Charge Hibernate Science Activity Incremental Search Engine Optimal solutions Enforces global constraints Efficient re-planning Mission Objectives Start state Goal states Goal actions Goal constraints TEMPEST Start Survey 1 Survey 2 Hibernate

Life in the Atacama 2004 Workshop6Carnegie Mellon Mission Planner (MP) Addresses problems involving: Long-distance traverses amidst large-scale terrain Coupled position and time- varying parameters Local and global constraints A mix of action types Enables efficient re-planning Adapts to updates in rover state online Limitations: Initial planning slow 15+ minutes/plan Can’t select goal ordering User must order goals Serial actions only Parallel activities not represented Coarse actions only Long look-ahead prevents high-resolution planning

Life in the Atacama 2004 Workshop7Carnegie Mellon Goal Manager (GM) Roles: Selects maximum-reward feasible subset of science activities Creates approximate plans quickly for science team trade studies Approach: Assumptions: Unlimited battery No solar energy Paths are time-independent Search: Uses MP models Finds minimum-cost paths between goals with A* search Maximizes science reward within a given day using paths Keeps list of goals (activities) that yield maximum reward

Life in the Atacama 2004 Workshop8Carnegie Mellon Architecture: off-board Planning in two modes “Quicklook” Mode GM quickly determines feasible goal sets and approximate plans using simplified assumptions Final Planning Mode GM selects from goal list and passes subset to MP MP derives mission plan for a 24-hour period Goal Manager Mission Planner Science Operations Interface Ordered Goal List Mission Plan Ordered Goal List Subset Mission Plan “Quicklook” Mode Final Planning Mode

Life in the Atacama 2004 Workshop9Carnegie Mellon Architecture: on-board Planning same as in off-board final planning: GM stage Selects subset of goal list, based on approximate, reward-based planning MP stage Derives plan that achieves goal subset, while satisfying constraints Re-planning efficiently updates plan as ops evolve Goal Manager Mission Planner Ordered Goal List Mission Plan Ordered Goal List Subset Mission Plan Final Planning Loop Rover Executive

Life in the Atacama 2004 Workshop10Carnegie Mellon Operations Scheme Goal Manager Mission Planner Science Operations Interface Ordered Goal List Mission Plan Ordered Goal List Subset Mission Plan “Quicklook” Mode Final Planning Mode Goal Manager Mission Planner Ordered Goal List Mission Plan Ordered Goal List Subset Mission Plan Planning Loop Rover Executive Final Ordered Goal List Candidate Ordered Goal List Science Team Candidate Mission Plan Offboard Onboard Rover

Life in the Atacama 2004 Workshop11Carnegie Mellon Input Data and Sources Models World Model Rover Model Constraint Set Action Set Rover initial state Position Time Battery level Science activity list Selected from “library” of canonical science activities Via Point (no science activity) Panorama Science Survey Type 1...n Each activity specified by: Position Specific activity type Parameters for model Reward/value of activity Rover Team (a priori) Rover/ Rover Team (real time) Science and Rover Team (a priori) Science Team (each sol)

Life in the Atacama 2004 Workshop12Carnegie Mellon Data Outputs Mission Plans consist of action tokens Each rover action represented as a token Tokens specify Identificatione.g. Panorama1 Token type e.g. Drive, Solar_Charge, Panorama Reward1000 Initial statePosition, time, battery energy Final statePosition, time, battery energy Final state boundst min, t max, e min Plans are sent to GM, Rover Executive for detailed planning and execution

Life in the Atacama 2004 Workshop13Carnegie Mellon Data Outputs Goals Start Color of route signifies battery energy level: Green = high energy Yellow = medium energy Red = low energy

Life in the Atacama 2004 Workshop14Carnegie Mellon Planning Behaviors and Limitations Planning Initial (full) planning may take minutes Re-planning Re-planning (onboard) is fast (~seconds) Plans are a function of initial time Routes may change substantially in re-planning Limitations No automated goal ordering Serial actions only Coarse actions only

Life in the Atacama 2004 Workshop15Carnegie Mellon Conclusion Remaining Development and Testing “Library” of science activity models Composite activities (e.g. periodic sampling traverse) Off-board integration with Event Scope End-to-end system testing Summary Goal Manager and Mission Planner solve for plans that achieve science objectives while satisfying mission constraints