computational robotics

from tasks to motions


“A Synergistic Framework for Motion and Task Planning in Mixed Continuous and Discrete Spaces”

  • PI: Plaku E
  • Funding awarded: $99,924.00
  • Duration: 09/01/2015--08/31/2017

This exploratory project is to increase the ability of robots to plan and act on their own in order to safely complete high-level tasks. Whether the task is to search, inspect, manipulate objects, or navigate to target destinations, it generally involves abstractions into discrete, logical actions, where each discrete action often requires complex collision-free motions in order to be implemented. This project establishes a unified framework for handling the discrete and continuous spaces that arise in these problems.

Crucial to this goal, as robots are deployed into less and less structured environments, is their ability to reason and plan at multiple levels of discrete and continuous abstractions. This research makes it possible to specify sophisticated tasks using Planning Domain Definition Languages and automatically compute a sequence of control inputs such that the resulting trajectory satisfies the PDDL task specification, avoids collisions with obstacles, and obeys the motion dynamics and the constraints imposed by physics-based interactions with the environment.


“Toward Supervised Autonomy for Robotic Systems”

  • PI: Plaku E
  • Funding awarded: $149,995.00
  • Duration: 09/01/2014--08/31/2016

This project seeks to make the supervision of robotic systems operating in complex domains similar to that of humans so as to increase productivity and capabilities. The software framework resulting from this project frees supervisors from the burden of unnatural low-level commands and instead allows them to describe tasks in a structured language that has the ability to express global and local objectives across time spans. The framework then automatically computes the necessary motions to enable the robotic system accomplish the assigned tasks. This necessitates a comprehensive treatment of planning to account for sophisticated tasks, robot dynamics, collision avoidance, robust replanning, and interactions with human supervisors. To addresses such complexity, the framework employs a novel probabilistic search with discrete abstractions and enhanced sampling capability to focus the search on the space of feasible motions that enable the robotic system to make progress toward accomplishing the assigned task. The framework also provides critical feedback information about the progress made to help supervisors adapt the specifications in response to challenges encountered during planning and execution. This project is expected to establish a new paradigm for supervised autonomy and impact the development of research and commercial software for robotic systems. Doing so has the potential to enhance applications of robotic systems such as underwater vehicles in surveying marine wildlife, inspecting harbors and offshore platforms.

NSF ACI Software Infrastructure for Sustained Innovation (ACI1440581)

“A plug-and-play software platform of robotics-inspired algorithms for modeling biomolecular structures and motions”

  • PIs: Shehu A (GMU), Plaku E, Roitberg A (U Florida)
  • Funding awarded to Plaku E: $215,476.00 (overall: $499,999)
  • Duration: 02/01/2015--01/31/2018

This project aims to develop a novel plug-and-play platform of open-source software elements to advance algorithmic research in molecular biology. The focus is on addressing the algorithmic impasse faced by computational chemists and biophysicists in structure-function related problems involving dynamic biomolecules central to our biology. The software platform resulting from this project provides the critical software infrastructure to support transformative research in molecular biology and computer science that benefits society at large by advancing our modeling capabilities and in turn our understanding of the role of biomolecules in critical mechanisms in a living and diseased cell.

The project addresses the current impasse on the length and time scales that can be afforded in biomolecular modeling and simulation. It does so by integrating cutting-edge knowledge from two different research communities, computational chemists and bio-physicists focused on detailed physics-based simulations, and AI researchers focused on efficient search and optimization algorithms. The software elements integrate sophisticated energetic models and molecular representations with powerful search and optimization algorithms for complex modular systems inspired from robot motion planning. The plug-and-play feature of the software platform supports putting together novel algorithms, such as wrapping Molecular Dynamics or Monte Carlo as local search operators within larger robotics-inspired exploration frameworks, and adding emerging biomolecular representations, models, and search techniques even beyond the timeline of this project.

U.S. Naval Research Laboratory

“Adaptive Mission and Motion Planning to Enhance the Autonomy of Underwater Vehicles”

  • PI: Plaku E
  • Funding: $55,298.00
  • Duration: 05/30/2015--05/29/2016

This project seeks to enhance the autonomy of underwater vehicles both in terms of mission and motion-planning capabilities. By quickly adapting mission and motion plans in response to unanticipated obstacles, changes in the environment, or new information gathered during exploration, the proposed approach seeks to provide the AUV with decision-making mechanisms that will greatly enhance its capabilities to carry out sophisticated missions.

CUA School of Engineering Seed Grants

  • PI:Plaku E (2014): Motion Planning for Unmanned Aerial Vehicles $14,000
  • PI:Plaku E (2012): Robotic Manipulation with the KUKA youBot $40,000
  • PI:Plaku E (2011): Motion Planning and Exploration with iCreate Robots $13,000