Group G25 - Formal & Title & Goals

Team members

RL-Based Autonomous Shepherd Robot for Livestock Herding

Implement effective herding behaviors through proximity and movement strategies
Build a 3D environment with realistic robot dynamics and LIDAR-based perception
Develop a mobile robot capable of autonomously guiding a flock of sheep into a designated target area using Reinforcement Learning

State-of-the-art survey on shepherding algorithms and multi-agent RL herding
Train the robot using PPO to successfully herd a single sheep into the goal
Achieve fully autonomous herding of multiple sheep and a full flock into the target area
Optimize robot trajectory to minimize the time required to group the flock
Ensure zero collisions between the robot and the sheep during the task
Quantitative evaluation: success rate, time-to-pen, flock dispersion metrics
Article, demo video, and final presentation

Curriculum Learning (scaling from 1 sheep to a flock)
Comparison of performance between Differential Drive and Mecanum wheels
Robustness testing under sensor noise or varying sheep speeds, configurations and parameters
Multi-shepherd cooperative mode: 2 dogs learn role specialization (collector vs. driver)
Obstacles and terrain (walls, narrow gates, corridors) the flock must be funneled through

Webots for 3D physics simulation with ROS2 integration via webots_ros2 package
Stable-Baselines3 for the PPO algorithm implementation
Gymnasium (OpenAI) for the RL environment wrapper (lightweight 2D herding env for fast RL training)
Python as the primary programming language (sheep flocking model, reward shaping, evaluation)

Computational Power: Training time might be high for complex flock behaviors
Sim-to-Real Gap: No real-world validation of the herding controller; project is simulation-only (2D + Webots 3D)
Model Complexity: Simplified sheep behavior (scripted) may not account for all biological livestock nuances