Max Robotics — Global Service Robot Directory + US Certification

About

Adam Seewald’s research lies at the intersection of energy-aware robotics, autonomous systems, and heterogeneous embedded computing. His major contributions focus on developing computational and mechanical energy models that enable drones and multi-agent systems to operate efficiently under real-world battery constraints. In his most cited work, “Coarse-Grained Computation-Oriented Energy Modeling for Heterogeneous Parallel Embedded Systems” (12 citations), Seewald established foundational methods for estimating energy consumption in complex embedded platforms. His 2022 paper on “Energy-Aware Planning-Scheduling for Autonomous Aerial Robots” (10 citations) introduced an innovative online approach that simultaneously plans coverage paths and schedules onboard tasks, featuring a novel variable coverage motion robust to airborne constraints. More recently, his 2024 work on “Energy-Aware Ergodic Search” (5 citations) addresses continuous exploration for multi-agent systems—critical for search and rescue and precision agriculture. His 2020 case study on fixed-wing drone energy estimation (5 citations) presented a generalizable Fourier series-based modeling approach. Seewald’s work is notable for bridging theoretical energy modeling with practical autonomous deployment, making his research highly relevant for students and engineers working on sustainable, long-endurance robotic systems.