Max Robotics — Global Service Robot Directory + US Certification

About

Alison Eele is a researcher whose work sits at the intersection of optimal control, real-time estimation, and reconfigurable computing. Her most impactful contribution is a globally optimal path-planning method that uses nonlinear branch-and-bound optimization to guide vehicles while avoiding obstacles—a breakthrough that retains full nonlinear dynamics, making it uniquely suited for unmanned aerial vehicle guidance. This foundational paper has earned 69 citations. Eele also pioneered adaptive Sequential Monte Carlo (particle filter) approaches for real-time applications, significantly reducing computational complexity by dynamically adjusting particle counts. She extended this work by mapping adaptive particle filters onto heterogeneous reconfigurable systems combining FPGAs and CPUs, enabling efficient, runtime-reconfigurable implementations for time-critical tasks. Her multi-vehicle avoidance extension further demonstrates the scalability of her optimization framework. With a portfolio spanning optimization theory, probabilistic estimation, and hardware-software co-design, Eele’s contributions are particularly valuable for autonomous systems operating under strict real-time and safety constraints. Her work provides a bridge between theoretical guarantees and practical deployment in robotics and aerospace.