Multi-Robot Systems

CSCI 7000-006Monday, September 28, 2009

NikolausCorrell

Crafting a Research Project

• What is “research”?• Preliminary requirement: open question• Secondary: how to solve it• Hypothesis: states question and leads to

methodology• Sources of confusion

– You need to investigate what the questions are– You need to design your experiment– You need to optimize your system– You need to develop tools to investigate

Collaborative Lifting

• Problem: Lifting a box collaboratively

• Hypothesis: Problem can be encoded in a single cost function that allows gradient-based control

• Method: formal stability analysis

Gregory Brown

Collaborative Bouncing

• Problem: Bouncing a ball back and forth between two robots

• Hypothesis: Use a particle-filter for predicting system dynamics

• Method: Dynamical model and implementation

Mikael Ian Pryor

Probabilistic Patrolling

• Problem: Patrol an environment efficiently but unpredictable to the adversary

• Hypothesis: Use a balance between exploration and exploitation during coverage

• Method: Probabilistic algorithm, model, implementation

VijethRai

Probabilistic Localization with Geometric Constraints

• Problem: Localizing “intelligent” objects

• Hypothesis: Using the object geometry and simulated physics in a particle filterfor an RFID reader can improve localization accuracy

• Method: Particle filter combined with physics-based simulator

Neeti Shared Wagle

Reactive Coverage with Connectivity Constraints

• Problem: cover an environment while maintain connectivity

• Hypothesis: Constraints can be encoded in a global cost function

• Method: Stability analysis of gradient-based controller MaciejStachura

Probabilistic Path Generation for Data Ferrying in Unknown Sensor Deployments

• Problem: collecting data from sensor network using mobile robot

• Hypothesis: optimal planning always better or same than randomized even if node location is unknown

• Method: analysis and hardware validation

Anthony Carfang

Policy-space Learning of Tunable Locomotion Primitives

• Problem: learn to locomote unknown actuator configurations

• Hypothesis: The Natural Policy Gradient method can allow to find optimal policies in high-dimensional, continuous state space in real time

• Method: implementation in realistic simulation

Ben Pearre

Resource sharing in Multi-Robot Systems

• Problem: improve individual performance by relying on team sensors

• Hypothesis: Can Resource Sharing Make Up for Perception Deficiencies in a Multi-Robot Team?

• Method: Demonstration in real hardware

GPS

Peter Klein

Informed Flocking in Honey Bees

• Question: how do honeybees communicate the location of a new nesting site

• Hypothesis: Can the Robustness to Disturbances Shed Light into the Preferred Method of Informed Flocking in Honey-Bees?

• Approach: mathematical model and numerical simulation

Apratim Shaw

Mothership/DaughtershipCoverage Control Problem

• Question: how to best distribute capabilities in a system?

• Hypothesis: A hierarchical mothership(MS)/daughtership (DS) system can be applied to coverage control problems and is more efficient and scalable than a team of all MS or all DS.

• Method: mathematical model and numerical simulation Jason Durrie

An agent based approach to music generation

• Problem: generate nice music automatically

• Hypothesis: A threshold agent based model where each agent represents a note on the piano is capable of creating “good” sounding music.

• Approach: mathematical model and numerical simulation

Stephen Heck

MROS: Multi-Robot Operating System

• Problem: message passing in ROS limited to a single agent

• Hypothesis: broadcast message proxies can turn local message bus into message graph

• Implementation: Message proxy using BioNet

MarekSotola

Smart Sand

• Problem: Mapping hard to access environments

• Hypothesis: We can reconstruct the topology and sensing landscape of a cavity using large numbers of smart spheres that can establish their local position

• Method: implementation in ODE, analysis

Monish Prabhakar

Towards Truly Soft Robots

• Problem: Creating shape deformation and actuation from soft components

• Hypothesis: Given a soft smart sheet composed of cells that can be individuallyactuated and that can as a result actively change its shape, it is possible to createarbitrary 3D polygons by combining and contorting the 1D sheets in novel ways

• Method: Implementation of spring-mass model of actuator meshes in ODE

SwamyAnanthanarayan

Optimal plant placement

• Problem: place plants such that light and water are optimally used

• Hypothesis: Genetic algorithms will outperform gradient-based optimization in strongly-coupled, non-linear dynamic systems

• Method: Mathematical model, numerical simulation

Rhonda Hoenigman

Implementation

• Common resources/goals

– Manipulation

– Communication

– Mobile base

– ODE

– Matlab

• Create clusters and collaborate

Project report

• Motivation for your research

• Hypothesis

• Materials and Methods

• Results

• Discussion

• Conclusion

Scientific thesis in general

• Principally you need a hypothesis and write a dissertation to defend it

• The reality is often different

– Investigate interesting problem and variations

– Funding driven (not necessarily scientific)

– Change in direction/advising

• Solution: what is the most interesting question my material can answer? Drop all the rest.

This week

• Wednesday: Probabilistic Modeling

• Friday: Start course projects