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About

Diane Uwacu is a robotics researcher specializing in motion planning, topological guidance, and multi-robot systems. Her work centers on developing efficient algorithms that help robots navigate complex environments, particularly those with narrow passages and congested spaces that challenge conventional planning approaches. Uwacu's most significant contributions leverage topological structures — especially workspace skeletons — to guide sampling-based motion planners. Her 2020 paper on topology-guided roadmap construction with dynamic region sampling (28 citations) demonstrated how probabilistic roadmaps could be dramatically improved by incorporating topological awareness. Building on this foundation, she extended these ideas to multi-robot settings with her scalable framework for congested environments (12 citations), addressing one of robotics' most computationally demanding challenges. Her HAS-RRT algorithm (2025, 8 citations) represents a culmination of this research thread, achieving up to 91% runtime reductions while maintaining solution quality — a remarkable practical improvement. Beyond connectivity, Uwacu uniquely advocates for enriched skeletal representations that encode properties like obstacle clearance and terrain conditions, broadening the applicability of topological guidance. With cumulative citations across six publications and growing recognition in the motion planning community, her research offers both theoretical depth and real-world relevance for students working at the intersection of robot autonomy and algorithmic efficiency.