19 citations · 22 across the 15 of their papers we have counts for
5 papers · 1 filter
MR. POP: Multi-Robot Parallel Optimizing Planner for Almost-Surely Asymptotically Optimal Planning
Chih H. Huang, Roy Xing, Brian Plancher +1
Finding globally optimal paths remains a fundamental challenge in multi-robot motion planning. Despite acceleration of almost-surely asymptotically optimal (a.s.a.o.) planners via…
RAYA: Learning Where and When to Intervene for Robot Recovery
Ishaan Mahajan, Charles Chen, Frederike Dümbgen +1
A robot can predict failure and still be unable to prevent it. By the time a safety mechanism reacts, the nominal plan may already have spent the control authority that recovery re…
AccelMPC: High-Rate, Low-Power FPGA-Accelerated Model Predictive Control for Tiny Drones
Andrea Grillo, Brian Plancher
Unlocking the potential of tiny aerial robots requires order of magnitude improvements in the performance of embedded edge control. In particular, although recent cached model pred…
TinySDP: Real Time Semidefinite Optimization for Certifiable and Agile Edge Robotics
Ishaan Mahajan, Jon Arrizabalaga, Andrea Grillo +4
Semidefinite programming (SDP) provides a principled framework for convex relaxations of nonconvex geometric constraints in motion planning, yet existing solvers are too computatio…
Real-Time, Energy-Efficient, Sampling-Based Optimal Control via FPGA Acceleration
Tanmay Desai, Brian Plancher, R. Iris Bahar
Autonomous mobile robots (AMRs), used for search-and-rescue and remote exploration, require fast and robust planning and control schemes. Sampling-based approaches for Model Predic…