Carnegie Mellon University THE ROBOTICS INSTITUTE

Thesis Proposal Sankalp Arora

Monday, April 24, 2017NSH 110911:00 a.m.

Sebastian Scherer Chair

William (Red) L. Whittaker

David Wettergreen

Kostas Alexis University of Nevada, Reno

Thesis Committee

Safe, Efficient Data Gathering in Physical Spaces

Abstract

Reliable   and   efficient   acquisi0on   of   data   from   physical   spaces   will   have   countless   applica0ons   in   industry,   policy   and   defense.   The  capability   of   gaining   informa0on   at   different   scales   makes   Micro-­‐Aerial   Vehicles   (MAVs)   excellent   for   aforemen0oned   applica0ons.  However,   reasoning   about   informa0on  gathering   at  mul0ple   resolu0on   is  NP-­‐Hard   and   the   state  of   the   art  methods   are   too   slow   to  present  an  approximate   solu0on  online.  Moreover,   a   robust  data  gathering   system  needs   to   reason  about  mul0-­‐resolu0on  nature  of  informa0on  gathering  while  being  safe,  and  cognizant  of  its  sensory  and  baKery  limita0ons.  

This   thesis   addresses   three   key   aspects   of   enabling   safe,   efficient,  mul0-­‐resolu0on   data   gathering:   online   budgeted  mul0-­‐resolu0on  informa0ve   path   planning   (IPP),   guaranteeing   safety   and,   op0miza0on   of   sensing   bandwidth   for   implicit   and   explicit   data   gathering  requirements.  

Firstly,  we  present  an  online  naviga0on  algorithm  to  guarantee  the  safety  of  the  robot  through  an  Emergency  Maneuver  Library  (EML).  We   discuss   an   efficient   method   to   construct   EML   while   exploi0ng   vehicle's   dynamics   capabili0es.  We   then   present   an   informa0on  gathering  approach  that  op0mizes  the  sensory  ac0ons  to  ensure  vehicle  safety  and  gain  informa0on  relevant  for  mission  progress.  We  validate  these  methods  by  deploying  them  on-­‐board  a  full  scale  helicopter,  demonstra0ng  significant  performance  increase.  We  address  the  IPP  problem  through  Randomized  Any0me  Orienteering  (RAOr),  an  any0me,  asympto0cally  near-­‐op0mal  algorithm,  that  enables  the  planning  for  informa0on  gathering  online.  

We  will  focus  our  future  work  on  three  sub-­‐problems  that  will  lead  to  a  safe,  efficient  data  gathering  framework.  The  first  is  developing  a  receding  horizon  planner  that  enables  the  vehicle  to  stay  safe  while  maximizing  the   informa0on  gathered,   through  embedding  safety  constraint  and  informa0on  theore0c  reward  func0ons  in  sampling  based  planning  framework.  The  second  is  learning  a  set  of  heuris0cs  to   enable   faster  mul0-­‐resolu0on   informa0ve   path   planning   through   RAOr.   The   third   is   to   use   the   safe   data   gathering   framework   to  improve  vehicle's  long-­‐term  performance  through  improving  its  assump0ons  about  the  environment.  

We  will  evaluate  the  performance  of  our  informa0on  gathering  framework  on  an  autonomous  MAV.  We  expect  that  our  framework  will  enable  long  term  deployment  of  autonomous  mul0-­‐resolu0on  data  gathering  systems,  while  guaranteeing  their  safety,  enabling  MAVs  to  realize  their  poten0al  as  efficient  data  gatherers.