[T-ITS'23] Sim-to-real goal-oriented mapless autonomous navigation (DRL navigation).
This repository provides an OpenAI Gym-compatible environment for production scheduling tasks, designed to benchmark reinforcement learning agents in job shop and flow shop settings.
A reinforcement learing environment for robotic mobile fulfilment system (RMFS)
Simulating multiple AGVs (Automated Guided Vehicles) with VDA 5050 protocol, sending them orders, and visualizing their movement and status via MQTT.
This repository contains Python scripts that demonstrate how to use Plant Simulation as a learning environment for Reinforcement Learning (RL) algorithms to tackle deadlock situations in Automated Guided Vehicle (AGV) systems. The code is compatible with the Gymnasium and Ray libraries.
Mobile robot data were analyzed with Apache-Spark to extract five different statistical result such as travel time, waiting time, average speed, occupancy and density were produced.
A complete ROS 2 Jazzy workspace for simulating, controlling, and navigating a MiR 100 mobile robot in Gazebo Harmonic, featuring Nav2 integration and an autonomous pick-and-place orchestrator.
This repository contains a simulation of an AGV using ros2, rviz2, gazebo and ros2 control.
This repository collects reference implementations for training and evaluating reinforcement learning agents on multi-agent pathfinding problems. The environments explicitly support deadlocks so that agents must cooperate to resolve them.
One platform for orchestrating robots, equipment, and operations.
This project is aiming to solve multiple vehicle routing problems.
Discrete-event simulation framework for job-shop scheduling and intralogistics in Python
This project is an AGV (Automated Guided Vehicle) path planning simulator using Pygame. Users can click on a location to set different path types (Turning, Idle, Normal, Charging), place an AGV, and move it along selected paths. The simulation runs on a clean map instead of a grid.
A Python implementation of NSGA-II for multi-objective flexible job shop scheduling with AGVs, optimizing makespan and energy consumption.
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