Accession Number : ADA627055


Title :   Robust Coordination of Autonomous Systems through Risk-sensitive, Model-based Programming and Execution


Descriptive Note : Final rept. 1 Sep 2012-31 Aug 2015


Corporate Author : MASSACHUSETTS INST OF TECH CAMBRIDGE COMPUTER SCIENCE AND ARTIFICIAL INTELLIGENCE LAB


Personal Author(s) : Williams, Brian ; Santana, Pedro ; Fang, Cheng ; Timmons, Eric


Full Text : https://apps.dtic.mil/dtic/tr/fulltext/u2/a627055.pdf


Report Date : 09 Oct 2015


Pagination or Media Count : 76


Abstract : Unlike their human counterparts, most autonomous systems to date are not effective at characterizing or bounding mission risk. In this project, we enabled the development of risk-sensitive autonomous systems through three main contributions: first, we introduced cRMPL, an extension of RMPL where one can specify acceptable risk levels for different mission segments through the addition of chance constraints. Second, we extended the continuous planner, used by our executive, to generate and adapt plans that maximize expected utility within the risk bounds specified by the operators. Planning is performed through novel stochastic optimization algorithms that allocate user-specified risk to actions and constraints according to the benefit received. We evaluated the generality of this risk-sensitive paradigm in simulation and hardware, for autonomous air or space vehicles and humanoid logistics support robots. Benefits include increased number and complexity of vehicle missions for a fixed operational cost, increased robot safety around humans; a reduction in unacceptable mission failure or robot loss, and improved mission return within defined risk levels.


Descriptors :   *ROBOTICS , ADAPTIVE SYSTEMS , ALGORITHMS , DECISION MAKING , GOAL PROGRAMMING , LOGISTICS SUPPORT , MAN COMPUTER INTERFACE , MATHEMATICAL MODELS , MISSIONS , MULTISENSORS , NETWORK ARCHITECTURE , OPERATORS(PERSONNEL) , OPTIMIZATION , PLANNING , PROBABILITY , SCENARIOS , SCHEDULING , SELF OPERATION , STOCHASTIC PROCESSES , UNCERTAINTY


Subject Categories : Statistics and Probability
      Cybernetics
      Human Factors Engineering & Man Machine System


Distribution Statement : APPROVED FOR PUBLIC RELEASE