Tutorial on Ant Robotics - Sven Koenig

100 Pages·2006·1.23 MB·English

Tutorial on Ant Robotics
Sven Koenig
University of Southern California
[email protected]
joint work with Jonas Svennebring, Boleslaw
Szymanski, Yaxin Liu, and Craig Tovey
Overview
• Cheap robots
Robomow
– Limited capabilities
(cid:127) Computation capability
(cid:127) Sensing capability
(cid:127) Actuation capability
– Groups of robots
Cye
(cid:127) Fault tolerance
(cid:127) Parallelism
Roomba
Overview
(cid:127) Ant robots
– Robots with limited capabilities
– Robots that leave information in the terrain
(cid:127) Ant robots cannot use conventional
planning methods. Rather, their behavior
is driven by local interactions. This can
result in very robust navigation.
Recommended special journal issue for further reading:
AMAI special issue on Ant Robotics edited by Wagner and Bruckstein
Overview
(cid:127) Our motivating task is the one-time or
repeated coverage of known or unknown
terrain with single robots or teams of
robots
– Mine sweeping
– Surveillance
– Search-and-rescue
– Guarding
– Surface inspection
Overview
(cid:127) This topic is a bit far out. However, …
(cid:127) We will touch on different areas of AI and CS
– Agent coordination
(swarms)
– Robotics
(robot architectures, ant robots, sensor networks)
– Search
(real-time search)
– Complexity analysis of graph algorithms
(cid:127) We will see several good dissertation topics.
Structure
(cid:127) Motivation
(cid:127) Real-time search
(cid:127) Results on real-time search
(cid:127) Application to ant robots and results
(cid:127) Serious application: smart markers
Motivating Toy Task
(cid:127) Guarding a museum at night
– Robots
(cid:127) Computation is slow
(cid:127) Sensing is noisy
(cid:127) Actuation is noisy
(cid:127) Robots can fail
– Terrain
(cid:127) Terrain might be unknown initially
(cid:127) Terrain can change over time
First Approach
(cid:127) Good location estimates - e.g. probabilities
(cid:127) Path planning – e.g. POMDPs
(cid:127) Explicit coordination – e.g. auctions
First Approach
goal location
First Approach
path planning
Path Planning path
policy generation
mapping from poses to directives (“policy”)
current pose distribution directive selection
POMDP
topological map
compilation
prior actuator model
POMDP pose estimation
prior sensor model
prior distance model
model learning
using GROW-BW
(based on Baum-Welch)
sensor report motion report desired directive
sensor
interpretation
occupancy grid [Elfes]
Path Following
motion generation
Obstacle Avoidance
raw sonar data raw odometer data motor commands
Real-Time Control