Control for Situational Awareness in 3D, Dynamic Environments

1
Control for Situational Awareness in 3-D, Dynamic
Environments Dr. Vijay Kumar University Of
Pennsylvania October 15th, 2008
Ron Arkin (GT) Henrik Christensen (GT) Frank
Dellaert (GT) MaryAnne Fields (ARL) Ali Jadbabaie
(Penn) Vijay Kumar (Penn) Daniela Rus
(MIT) Claire Tomlin (Berkeley)
Distribution authorized to DoD and DoD
Contractors Only Critical Technology (March
2007). Other requests for this document Shall be
referred to Director, US Army Research
Laboratory, ATTN AMSRD-ARL-DP-P, Adelphi, MD
20783-1197
2
MAST Vision
To develop autonomous, multifunctional,
collaborative ensembles of agile, mobile
microsystems to enhance tactical situational
awareness in urban and complex terrain for small
unit operations.
Group Behaviors
Exploration, Coverage, Mapping
3
Examples of MAST tasks

• Achieve eyes on target in an urban environment
  without commanding individual assets with poor
  GPS and lossy communications
• Enter into a building, organize into subteams and
  clear the building, and then secure the building
  
• Provide an integrated picture for situational
  awareness to war ?ghters

• Track hostile targets and dynamic threats in 3-D
  environments
• Find victims and IEDs in damaged buildings with
  crawling, climbing, ?ying, or hovering vehicles
• Maintain persistent surveillance of a rural or
  urban environment
• Provide situational awareness of poorly lit,
  uneven, gusty, unstructured environments (caves,
  subterranean pipes)
• Surround perimeters of buildings or compounds for
  perimeter defense

Multi-Vehicle Situational Awareness
Maps State estimates Mutual info
Tasks Plans Controllers
Software
Maps State estimates Mutual info
Tasks Plans Controllers
models of platforms, environment,
resources, algorithms
Group Navigation, Control, Communication
Distributed Sensing and Perception
Fading Shadowing Interference SNR Link State

Fading Shadowing Interference SNR Link State
MAST Environments
4
Decentralized Control for MAST
50
20
Environment degree of difficulty
Number of vehicles
10
5
complexity of coordinated control task
3
State of the art
1. Coordinated Control in unstructured
environments
2. Coordinated control in 2.5D indoor/outdoor
environments
3. Coordinated control in 3D, feature-rich
environments (rubble, caves)
4. Perimeter surveillance and coverage in outdoor
environments
5
Four Projects

• A.5.1 Active Simultaneous Localization and
  Mapping
• Christensen (GT), Dellaert (GT), Jadbabaie
  (Penn), Kumar (Penn)
• A.5.2 Decentralized coverage verification and
  cooperative surveillance
• Jadbabaie (Penn), Pappas (Penn), Tomlin
  (Berkeley)
• A.5.3 Autonomous adaptive mobility of
  heterogeneous teams
• Kumar (Penn), Rus (MIT)
• A.5.4 Composition of group behaviors for
  scouting, reconnaissance, and surveillance
• Kumar (Penn), Pappas (Penn)

6
Active Simultaneous Localization and Mapping

• Control for mapping
• Localization for control
• Mapping for localization
• Localization for mapping

Potential Project Fields (ARL/VTD)
7
Control for Search, Detection, Identification,
Localization of Features
UGV identification and localization of potential
targets
Grocholsky et al, ISER 2004, Singapore
8
Proposed Work

• Formulate Active SLAM as a multi-objective
  optimization problem
• Explore unexplored areas
• Minimize uncertainty (maximize information gain)
• Minimize effort (path)

9
Decentralized coverage verification and
cooperative surveillance

• Verify coverage in a robot network with no
  location information
• Localize coverage holes
• Find the minimal number of sensors necessary to
  cover the region.

Minimalist approach without localization (no GPS,
map)
In a distributed manner
10
Coverage Verification Proposed Work
1. Coverage represented by a simplicial complex
de Silva and Ghrist 2006
3. Control platforms to ensure coverage in
dynamic setting
11
Autonomous adaptive mobility of heterogeneous
teams
MAST
ARO SWARMS
12
Coverage

• Coverage functional

Cortes et al 2004, Mohlis et al 2007
13
Adaptive Coverage Proposed Work

• MIT/Penn Collaboration
• Potential collaboration with ARL (Sadler) on
  localization

14
Composition of group behaviors for scouting,
reconnaissance, and surveillance
5 helicopters and 25 ground vehicles navigate to
a designated building and surround the site
15
Proposed Work

• Assumptions
• Minimal communication for control
• Local information
• Link to base station not modeled
• Approach
• Toolbox of behaviors
• model-predictive control for group navigation,
  adaptive coverage controllers, perimeter
  surrounding behaviors,
• Create abstract models of behaviors
• Enable composition of behaviors
• Allow human operator to command and control at
  the group level

16
MAST Integrated Simulation Environment
Wiki site, repository
17
Model-Based Design, Integration and Validation of
MAST Software
Language
Penn
Mission specification
BAE, GT
Penn
Planning
Controller synthesis
Berkeley
U Md
Model-based code generation
Vanderbilt
End-to-end verification/validation
Penn
Testing and validation with simulation
and experimental platforms
Berkeley, GT, Penn
18
Concluding Remarks

• Close coordination with other thrusts
• Small size, unstructured environment and
  heterogeneity require unique approaches
• Real-world constraints introduce new challenges
• Complex environments
• Communications
• Stochastic processes
• Noisy sensors