4 citations · 7 across the 5 of their papers we have counts for
5 papers
Model Predictive Path Integral PID Control for Learning-Based Path Following
Teruki Kato, Koshi Oishi, Seigo Ito
Classical proportional--integral--derivative (PID) control remains widely used in industrial control systems, while model predictive control (MPC) is actively studied to achieve hi…
MAPF-HD: Multi-Agent Path Finding in High-Density Environments
Hiroya Makino, Seigo Ito
Multi-agent path finding (MAPF) involves planning efficient paths for multiple agents to move simultaneously while avoiding collisions. In typical warehouse environments, agents ar…
Visual-Based Forklift Learning System Enabling Zero-Shot Sim2Real Without Real-World Data
Koshi Oishi, Teruki Kato, Hiroya Makino +1
Forklifts are used extensively in various industrial settings and are in high demand for automation. In particular, counterbalance forklifts are highly versatile and employed in di…
Online Multi-Agent Pickup and Delivery with Task Deadlines
Hiroya Makino, Seigo Ito
Managing delivery deadlines in automated warehouses and factories is crucial for maintaining customer satisfaction and ensuring seamless production. This study introduces the probl…
MARPF: Multi-Agent and Multi-Rack Path Finding
Hiroya Makino, Yoshihiro Ohama, Seigo Ito
In environments where many automated guided vehicles (AGVs) operate, planning efficient, collision-free paths is essential. Related research has mainly focused on environments with…