june 10 th, 2002: topics 1,7, 17 joint kickoff adaptive coordinated control in 3-d dynamic...

June 10th, 2002: Topics 1,7, 17 Joint Kickoff

Adaptive Coordinated Control in 3-D Dynamic Battlefield

June 10, 2002



Adaptive Coordinated Control of Intelligent Multi-Agent Teams

Shankar Sastry, Ruzena Bajcsy, Laurent El Ghaoui, Mike Jordan, Jitendra Malik, Stuart Russell, Pravin Varaiya (Berkeley)

Vijay Kumar, Kostas Danillidis, James Ostrowski, George Pappas, C. J. Taylor (Penn)

Howie Choset, Alfred Rizzi, Charles Thorpe (CMU)



Background

ARO-MURI “Integrated Approach to Intelligent Systems” 1996-2001. Partners: Berkeley, Stanford, Cornell. Highlights:

1. Creation of Field of Hybrid Systems: Foundations, Methods, Analysis, Control

2. Vision Based Navigation and Control

3. Major Force Driving Bayesian Networks, Graphical Models, Dynamical Probabilisitic Networks, Learning, Rapproachment of AI and Control



Integrated Approach to Intelligent Systems

• Disciplinary Evolution

– Control Theory

• Optimal control, linear control, nonlinear control, adaptive control, stochastic control

• Mathematics: differential equations

– Artificial Intelligence

• Reasoning, adaptation, neural networks, natural language, expert systems

• Mathematics: formal logic

– Computational Neuroscience & Cognitive Science

• Sensing, vision, taction, olfaction neural networks

• Mathematics (recently developed)



Why Hierarchical Hybrid Control?

Central Control Paradigm. that is sensors and actuators interacting locally, breaks down when dealing with distributed systems due to

• Complexity & scale

• Necessity of “tight” or “optimal” operations

Key Characteristics of Distributed Intelligent Systems– Hierarchical or modular to

control complexity– Globally organized

emergent behavior– Robust, adaptive and fault

tolerant, and degraded modes of operation

– Architectural organization involving the use of compositionality



Why Hybrid Hierarchical Control?

• Intelligence Augmentation for Human-Centered Systems• Autonomous Intelligence

Why integrative? Due to: - the need to merge sensor fusion and hierarchies of sensing with

actuation across many agents, with desired emergent behavior- the need to merge logical decision making and continuous action- the need reconcile the need for safety of individual agents with collective optimality

Control, artificial intelligence and cognitive neuroscience deal with continuous action, logical reasoning and human/machine understanding, respectively



Technology Drivers: Semi-Autonomous Multi-Agent Systems

The need for a theoretical framework for an integrative approach arises from advances in computation, communication, intelligent materials, visualization and other technologies which make it possible to expect more from a multi-agent system than from a centralized control framework.

• Distributed Command and Control• Distributed Communication Systems• Distributed Power Systems• Intelligent Vehicle Highway Systems• Air Traffic Management Systems• Intelligent Telemedical Systems• Intelligent Manufacturing Systems• Unmanned Aerial Vehicle Networks• Mobile Offshore Bases



Theoretical Underpinnings

• Architectural Design for Multi-Agent Systems– Hybrid Systems– Centralization for optimality– Decentralization for safety, reliability and speed of response

• Perception Systems Sharing Many Representations– Hierarchical aggregation– Wide-area surveillance– Low-level perception

• Frameworks for Representing and Reasoning with Uncertainty• Incorporation of Learning, Adaptation and Fault Tolerance• Parametric uncertainty with update and adaptation at the continuous

levels, learning of new “logical entities” --reinforcement learning at the logical levels and metal-learning for redefining architecture



What Are Hybrid Systems?

Dynamical systems with interacting continuous and discrete dynamics



Why Hybrid Systems?

• Modeling abstraction of– Continuous systems with phased operation (e.g. walking robots,

mechanical systems with collisions, circuits with diodes)– Continuous systems controlled by discrete inputs (e.g. switches,

valves, digital computers)– Coordinating processes (multi-agent systems)

• Important in applications– Hardware verification/CAD, real time software– Manufacturing, communication networks, multimedia

• Large scale, multi-agent systems– Automated Highway Systems (AHS)– Air Traffic Management Systems (ATM)– Uninhabited Aerial Vehicles (UAV), Power Networks



Framework Control Theory

Control of individual agentsContinuous modelsDifferential equations

Computer ScienceModels of computationCommunication modelsDiscrete event systems

Hybrid Systems

xç = f 1(x; y)yç = f 2(x; y)

xç = g1(x; y)yç = g2(x; y)

x ô5

q1 q2x > 4à! x 2[0; 1]; y = 1

y > 10à! x = 0; y 2[1; 3]

y ô10

x 2[0; 1]y 2[0; 1]



Control Challenges

• Large number of semiautonomous agents• Coordinate to

– Make efficient use of common resource– Achieve a common goal

• Individual agents have various modes of operation • Agents optimize locally, coordinate to resolve conflicts• System architecture is hierarchical and distributed• Safety critical systems

Challenge: Develop models, analysis, and synthesis tools for designing and verifying the safety of multi-agent

systems



Taking Stock in Hybrid Systems

• Hybrid Systems and Control: established as a discipline, taught to undergrads, grads. Monographs, textbooks being written by all co-PIs: Lee and Varaiya, Henzinger and Alur, Lygeros, Tomlin and Sastry. Workshop on Hybrid Systems established (first was in Berkeley in 1998). Special Issues in IEEE Proceedings, Systems and Control Letters, Automatica, IEEE Transactions on Automatic Control, …

• Software: Programming languages, tools and frameworks for Simulation and Control: Ptolemy II, Giotto, Massaccio all developed. Ongoing work on verification tools.

• Hardware: in the loop demonstrations on the local UAVs, formation flying to follow.

• Embedded software: EMSOFT established, new IEEE Proceedings Special Issue 2003.



UCB/UCSF Laparoscopic Telesurgical Workstation



ANIMAL LAB TRIALS 1998



Suturing with Unimanual System, 1998



Berkeley BEAR Fleet: Ursa Maxima 1

Length: 3.63m Width:0.72mHeight: 1.08mDry Weight: 58 kg Payload: 30kgEngine Output: 21 hpRotor Diameter: 3.115mFlight & system operation time: 60 min

Based on Yamaha RMAX industrial helicopter

IntegratedNav/Comm Module



Flight Control System Experiments

Position+Heading Lock (Dec 1999)

Position+Heading Lock (May 2000)

Landing scenario with SAS (Dec 1999)

Attitude control with mu-syn (July 2000)



Pursuit-Evasion Game Experiment Setup

Ground Command Post

Waypoint Command

Current Position, Vehicle Stats

Current Position, Vehicle Stats

Pursuer: UAV

Evader: UGV

Evader location detected by Vision system



Pursuit-Evasion Game Experiment

PEG with four UGVs• Global-Max pursuit policy• Simulated camera view

(radius 7.5m with 50degree conic view)• Pursuer=0.3m/s Evader=0.5m/s MAX



Experimental Results: Pursuit Evasion Games with 4UGVs (Spring’ 01)



Pursuit-Evasion Game Experiment

PEG with four UGVs and a UAV• Global-Max pursuit policy• Simulated camera view

(radius 7.5m with 50degree conic view)• Pursuer=0.3m/s Evader=0.5m/s MAX



Experimental Results: Pursuit Evasion Games with 4UGVs and 1 UAV (Spring01)



What is Different Today?

1. The world and national security threats are different: mobile operations in urban terrain, hostage rescue, anti terrorism operations, homeland protection.

2. Use of robotic and mixed initiative forces, the need for coordination of manned and unmanned forces

3. The need for dynamic strategies and tactics for dealing with a determined and flexible adversary.

4. Exploitation of the 3rd dimension by organic UAVs



New Technical Innovations

• Control of the 3 D Digital battlefield: need to use 3rd dimension, aerial forces, robotic and mixed initiative forces, untethered communications

• Adaptive Coordinated Control of Multiple Agents: reconfiguration of teams dynamically in response to adversarial action

• Intelligent coordination of multiple agents: ability to discover intent and reconfigure strategies adaptively



Intellectual Organization:Thrust Areas

• Architecture Design for Adaptive, Dynamic Planning

• Integration of Rich Multi-Sensor Information into Virtual Environments incorporating human intervention

• Handling Uncertainty and Adversarial Intent in Adaptive Planning



Challenge Scenarios

• Reconaissance and Intelligence: robotic ranger force for scouting fixed area for time critical targets

• Mixed Initiative Engagement in urban environments using micro-UAVs, UGVs. Emphasis on immersive environments for deploying

• Recognition and Tracking of Unfriendlies: emphasis on networked vision for tracking.



Hierarchical Architectures for Dynamic Adaptive Planning

• Progess to date in hierarchical architectures for decision making in “normal” modes of operation. Main emphasis here will be on replanning in “fault” or “degraded” modes of operation including deviations from hierarchical operation.

• Key technical issues:– Abstractions of Hybrid Systems for Architecture Design

• Hierarchical abstractions

• Assume-guarantee reasoning for abstractions



Thrust I continued

• Control of Hybrid Systems– Numerical Solutions for Controller Synthesis– Hierarchical Solutions of Synthesis Procedures– Liveness and other acceptance conditions

• Controller Libraries– Many world semantics and hierarchy semantics– Modal decomposition– Exceptions

• Team and Task Allocation



Integration of Multi-Sensor Information Into Virtual Environments

• Adaptive Hierarchial Networks for Acquiring and providing information– Networked sensors– Bandwidth utilitzation

• Extraction of 3 D Models from Distributed Sensors– 3 D models from video data– Integration of real and virtual environments

• Environments for Human Intervention & Decision Making– Situational awareness– Display of uncertain data– Triaging of data for decision making



Smart Dust, Dot Motes, MICA Motes

Dot motes, MICA motes and smart dust



Tiny OS (TOS)

Jason Hill, Robert Szewczyk, Alec Woo, David Culler

• TinyOS• Ad hoc networking



Last 2 of 6 motes are dropped from MAV



Field of wireless sensor nodes

• Ad hoc, rather than engineered placement• At least two potential modes of observation

– Acoustic, magnetic, RF



Subset of more powerful assets

• Gateway nodes with pan-tilt camera– Limited instantaneous field of view



Set of objects moving through



Track a distinguished object



Many interesting problems

• Targeting of the cameras so as to have objects of interest in the field of view

• Collaborate between field of nodes and platform to perform ranging and localization to create coordinate system

• Building of a routing structures between field nodes and higher-level resources

• Targeting of high-level assets• Sensors guide video assets in real time• Video assets refine sensor-based estimate• Network resources focused on region of importance



Abstraction of Sensorwebs

• Properties of general sensor nodes are described by

– sensing range, confidence on the sensed data

– memory, computation capability, clock skew

– Communication range, bandwidth, time delay, transmission loss

– broadcasting methods (periodic or event-based)

• To apply sensor nodes for the experiments with UAV/UGVs introduce super-nodes (or gateways), which can

– gather information from sub-nodes ( filtering or fusion of the data from sub-nodes for partial map building)

– communicate with UAV/UGVs



Uncertainty and Adversarial Intent

• Models of Uncertainty– Environmental: non deterministic and probabilistic– Adversarial

• Guarantees of Success in the face of uncertainty– Decision making in the presence of uncertainty

• Learning of Adversarial Strategy– Probing strategies– Games, partial information solution concepts– Adaptation to changing utility functions of adversary

june 10 th, 2002: topics 1,7, 17 joint kickoff adaptive coordinated control in 3-d dynamic...

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