l26b_bw_wrapup

l26b_bw_wrapup - Principles of Autonomy and Decision Making...

Info iconThis preview shows page 1. Sign up to view the full content.

View Full Document Right Arrow Icon
This is the end of the preview. Sign up to access the rest of the document.

Unformatted text preview: Principles of Autonomy and Decision Making Brian Williams 16.410/16.413 Session 26b 1 Today’s Assignment Study for the final! Outline • Objectives and Logistics • Appendix: Agents and Their Building Blocks 1 Course Objective 1: Agent Architectures 1. To appreciate the major types of agents, their major functions and the applications they support. 2. To understand the common architectures used to develop agents. • Understanding exercised through case studies. Agent Architecture (Objective 1) Communicate and Interpret Locate in World Monitor & Diagnosis Plan Execute Map Plan Routes Maneuver and Track Types of Agents (Objective 1) • • • • • Mission-oriented Agents Self-repairing Agents Mobile Agents Agile Agents Communicating Agents 2 Course Objective 2: Principles of Agents 16.410/13: To learn the modeling and algorithmic building blocks for creating reasoning and learning agents: 1. To formulate reasoning problems in an appropriate formal representation. 2. To describe, analyze and demonstrate the application of reasoning algorithms to solve these problem formulations. • Understanding demonstrated on paper and through implementation. � Introduction to modeling, algorithms and analysis the next two Wednesday. � Introduction to implementation the next two Mondays. Agent Building Blocks • • • • • • Activity Planning Execution/Monitoring Diagnosis Repair Scheduling Resource Allocation • • • • • Path Planning Localization Map Building Trajectory Design Policy Construction Course Objective 3: Implementing Agents 16.413: To appreciate the challenges of building a state of the art autonomous explorer: Fall 03: • Mars Exploration Rover shadow mode demonstration. Fall 04: • Gnu Robot competition. Fall 05: • Model-based autonomy toolbox • The virtual solar system • Stay tuned for more. 3 Outline • Objectives and Logistics • Appendix: Agents and Their Building Blocks Types of Agents (objective 1) 1. 2. 3. 4. 5. Mission-oriented Agents Self-repairing Agents Mobile Agents Agile Agents Communicating Agents 1. Mission-Oriented Agents courtesy JPL ``Our vision in NASA is to open the Space Frontier . . . We must establish a virtual presence, in space, on planets, in aircraft and spacecraft.’’ - Daniel S. Goldin, NASA Administrator, May 29, 1996 Courtesy of Kanna Rajan, NASA Ames. Used with permission. 4 One day in the life of a Mars rover rover operation (7 hr) tactical sci assess & obs planning (5 hr) tactical end-of-sol eng assess (5 hr) Activity Name DTE Night Time Rover Operations Durati 10 on 4.50 0.75 16.97 11 12 13 14 15 16 17 18 19 20 21 22 23 0 1 2 3 4 5 6 7 8 DFE 9 DTE period Sleep Night Time Rover Operations Wakeup Pre-Comm Session Sequence Plan Review Current Sol Sequence Plan Review 1 . 5 0 1.50 2.00 4.50 0.75 2.75 5.00 1.00 0.50 Current Sol Sequence Plan Review Prior Sol Sequence Plan Review Real-TIme Monitoring Downlink Product Generation Tactical Science Assessment/Observation Planning Science DL Assessment Meeting Payload DL/UL Handoffs Tactical End-of-Sol Engr. Assessment & Planning DL/UL Handover Meeting Skeleton Activity Plan Update SOWG Meeting Uplink Kickoff Meeting Activity Plan Integration & Validation Activity Plan Approval Meeting Build & Validate Sequences UL1/UL2 Handover Complete/Rework Sequences Margin 1 Command & Radiation Approval Margin 2 Radiation Real-TIme Monitoring Prior Sol Sequence Plan Review Real-TIme Monitoring D o w n l i n k P r o d u c t G e n e.r a t. i o n . Tactical Science Assessment/Observation Planning Science DL Assessment Meeting Payload DL/UL Handoffs T a c t i c a l E n d - o f - S o l E n g r . A s s e s s m e n t. 5 0 5& P lanning 0.50 DL/UL Handover Meeting Skeleton Activity Plan Update SOWG Meeting Uplink Kickoff Meeting Activity Plan Integration & Validation 2.50 2.00 0.25 1.75 0.50 2.25 1.00 2.50 0.75 0.50 1.25 0.50 SOWG mtg (2 hr) activity integration & validation (3.5 hr) Activity Plan Approval Meeting Build & Validate Sequences UL1/UL2 Handover Complete/Rework Sequences Margin 1 Command & Radiation Approval Margin 2 Radiation MCT Team 7.00 4.00 10 11 12 13 14 15 16 17 18 19 20 21 22 23 0 1 2 3 4 5 6 7 8 9 sequence development (5.5 hr) sequence integration & validation (4 hr) Courtesy of Kanna Rajan, NASA Ames. Used with permission. Agent Building Blocks (objective 2) • Activity Planning • Execution/Monitoring Types of Agents (objective 1) 1. 2. 3. 4. 5. Mission-oriented Agents Self-repairing Agents Mobile Agents Agile Agents Communicating Agents 6 2. Self-Repairing Agents • 7 year cruise • ~ 150 - 300 ground operators •~ 1 billion $ • 7 years to build •150 million $ •2 year build • 0 ground ops Affordable Missions Cassini Maps Titan courtesy JPL Four launches in 7 months Mars Climate Orbiter: 12/11/98 Mars Polar Lander: 1/3/99 Stardust: 2/7/99 QuickSCAT: 6/19/98 courtesy of JPL Mars Polar Lander Spacecraft require a good physical commonsense… Launch: 1/3/99 courtesy of JPL 7 Remote Agent Goals • Goal-directed • First time correct • projective • reactive • Commonsense models • Heavily deductive Planner/ Scheduler Scripts Mission Description Executive & Repair Diagnosis Diagnosis Mission-level actions & resources component models Remote Agent Experiment May 17-18th experiment • Generate plan for course correction and thrust • Diagnose camera as stuck on – Power constraints violated, abort current plan and replan • Perform optical navigation • Perform ion propulsion thrust May 21th experiment . • Diagnose faulty device and – Repair by issuing reset. • Diagnose switch sensor failure. – Determine harmless, and continue plan. • Diagnose thruster stuck closed and – Repair by switching to alternate method of thrusting. • Back to back planning Agent Architecture (Objective 1) Plan Monitor & Diagnosis Execute 9 Agent Building Blocks (Objective 2) • • • • • • Activity Planning Execution/Monitoring Diagnosis Repair Scheduling Resource Allocation Types of Agents (Objective 1) 1. 2. 3. 4. 5. Mission-oriented Agents Self-repairing Agents Mobile Agents Agile Agents Communicating Agents 3. Mobile Agents Day 2 Day 2 Traverse Estimated Error Circle Initial Position; Followed by “Close Approach” Day 2 Traverse Estimated Error Circle Target Autonomous On-Board Navigation Changes, as needed During the Day Day 3 Science Prep (if Required) Day 1 Long-Distance Traverse (<20-50 meters) Day 4 During the Day Science Activities Courtesy of Kanna Rajan, NASA Ames. Used with permission. 10 Agent Building Blocks (Objective 2) • • • • • • Activity Planning Execution/Monitoring Diagnosis Repair Scheduling Resource Allocation • Path Planning • Localization • Map Building Agent Architecture (Objective 1) Plan Monitor & Diagnosis Map Locate in World Execute Plan Routes 13 Types of Agents (objective 1) 1. 2. 3. 4. 5. Mission-oriented Agents Self-repairing Agents Mobile Agents Agile Agents Communicating Agents 4. Agile Agents 14 Agent Building Blocks • • • • • • Activity Planning Execution/Monitoring Diagnosis Repair Scheduling Resource Allocation • • • • • Path Planning Localization Map Building Trajectory Design Policy Construction Agent Architecture (Objective 1) Plan Monitor & Diagnosis Map Locate in World Execute Plan Routes Maneuver and Track Types of Agents (objective 1) 1. 2. 3. 4. 5. Mission-oriented Agents Self-repairing Agents Mobile Agents Agile Agents Communicating Agents 15 5. Communicating Agents Agent Building Blocks (Objective 1) • • • • • • Activity Planning Execution/Monitoring Diagnosis Repair Scheduling Resource Allocation • • • • • • • • Path Planning Localization Map Building Trajectory Design Policy Construction Plan Adaptation Dialogue Management People Tracking Agent Architecture (Objective 1) Communicate and Interpret Locate in World Monitor & Diagnosis Plan Execute Map Plan Routes Maneuver and Track 16 Example: surface exploration • ERA – EVA robotic assistant follows astronaut and helps with sample collection, instrument placement Example Mission Scenario: Task Execution • Robot walks to its sample area • Begins collecting samples • Walks back to astronaut – Stumbles over unseen rock along the way, but recovers using appropriate limb motions 18 ...
View Full Document

Ask a homework question - tutors are online