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Multi-Robot Systems CSCI 7000-006 Monday, 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/Daughtership Coverage 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

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September 28, Course Projects

  • 1. Multi-Robot Systems CSCI 7000-006 Monday, September 28, 2009 NikolausCorrell
  • 2. 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
  • 3. 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
  • 4. 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
  • 5. 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
  • 6. 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
  • 7. 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
  • 8. 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
  • 9. 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
  • 10. 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
  • 11. 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
  • 12. Mothership/Daughtership Coverage 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
  • 13. 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
  • 14. 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
  • 15. 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
  • 16. 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
  • 17. 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
  • 18. Implementation Common resources/goals Manipulation Communication Mobile base ODE Matlab Create clusters and collaborate
  • 19. Project report Motivation for your research Hypothesis Materials and Methods Results Discussion Conclusion
  • 20. 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.
  • 21. This week Wednesday: Probabilistic Modeling Friday: Start course projects