Fig: An illustration of a motion planning problem, where the robotic car is given the task of inspecting the regions labeled "p1," and "p2."

Mailing Address:

Erion Plaku
Rice University
Computer Science Dept.
6100 Main St., MS-132
Houston, TX 77005 USA

Contact Information:

phone: 713-348-2286
fax: 713-348-5930
email: plakue/AT/cs.rice.edu

Research

A significant challenge confronting autonomous robotics in transportation, exploration of unknown and hazardous environments, search-and-rescue missions, surgery, entertainment and education, and air traffic management lies in the area of automatic motion planning. While progress has been made in planning paths that avoid collisions, it remains particularly challenging to plan motions that enable robots with nonlinear dynamics to complete high-level tasks:

"Some of the most significant challenges confronting autonomous robotics lie in the area of automatic motion planning. The goal is to be able to specify a task in a high-level language and have the robot automatically compile this specification into a set of low-level motion primitives, or feedback controllers, to accomplish this task."
[Principles of Robot Motion, Choset et al., MIT Press 2005]

The approach I have followed in my Ph.D. research to make progress toward this fundamental goal in autonomous robotics has been through the development of novel algorithms and computational methods that combine research in robotics, hybrid systems, logic, artificial intelligence (AI), and machine learning.

The algorithms and methods I have developed in robotics have made it possible to automatically and efficiently plan low-level motions that enable increasingly complex robotic and intelligent systems such as
  • high-dimensional and multi-robot systems (see SRT project)
  • systems with nonlinear dynamics such as cars, differential drives, unicycles, tractor trailors, and others (see DSLX project)
  • hybrid systems equipped with sophisticated embedded controllers that use discrete logic to modify the underlying dynamics in response to changes in the environment, mishaps, or partial failures (see HyDICE project)
not only avoid collisions with obstacles and reach a desired destination, but also complete high-level tasks specified using the expresiveness of linear temporal logic (LTL). LTL allows for complex specifications, such as sequencing, coverage, and other combinations of intermediate objectives.

Physics-based engines have been used to obtain physically accurate simulations of different robotic systems and environments on which these methods have been extensively tested.

This work has also led to new approaches in hybrid-system verification, nearest neighbors, dimensionality reduction (with applications in computational biology), large-scale distributed computing, and the implementation of a publicly-available package, OOPSMP, that can be used for research, teaching, or developing applications in robotics.

Computational Methods Developed