In general, my research interests are robot motion planning, software validation and verification, SLAM, and Computer Science Education. Below are specific areas I have contributed to.

Real-time Adaptive Motion Planning (RAMP)

Robot motion in practical environments is still a challenge despite decades of research. My research focuses around navigating a robot at real-time in an unpredictable environment to reach a goal. Our contributions to this approach are a hybrid trajectory representation that contains both non-holonomic and holonomic segments, a method for real-time trajectory switching using non-holonomic trajectories, adaptive control cycles based on a robot's dynamics, and a straight-forward method for uncertainty reduction based on cycle interactions in the RAMP framework.

Sterling McLeod and Jing Xiao. Real-time adaptive non-holonomic motion planning in unforeseen dynamic environments. In IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2016. (PDF) bibtex

Model-based Test Generation for RAMP

Real-world environments are dynamic, unpredictable, and large. It is impossible to generate an exhaustive list of test scenarios for a motion planning algorithm. However, we still desire a way to validate software implementing a motion planning algorithm. Our research studied how to generate tests for both Component-Integration testing and System-level testing of a RAMP system. Our work shows a use-case of applying a model-based approach to RAMP for Component-Integration testing, and a method to generate System-level tests for RAMP.

Mahmoud Abdelgawad, Sterling McLeod, Anneliese Andrews, Jing Xiao. Model-Based Testing of Real-Time Adaptive Motion Planning (RAMP). In IEEE International Conference on Simulation, Modeling, and Programming for Autonomous Robots (SIMPAR), 2016. (PDF) bibtex