Styling and sheep behaviour

docs/project.md

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+# Group G25 - Formal & Title & Goals
+
+## Team members
+- Diogo Costa <up202502576@up.pt>
+- Johnny Fernandes <up202402612@up.pt>
+- Nelson Neto <up202108117@up.pt>
+
+## (i) Title and General objectives
+**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
+
+
+# Group G25 - (ii) Intermediate Goals
+
+## Intermediate goals
+- Set up the Webots simulation environment with an open field and target zone
+- Implement lightweight Gymnasium-based 2D herding environment
+- Design a Sheep and Dog robot
+- Implement a sheep flocking model for fast RL iteration
+- Validate LiDAR sensor feedback for sheep detection and distance estimation
+
+
+# Group G25 - Course Project (Final) Goals
+
+## (iii) Main goals
+- 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
+
+## (iv) Extra Merit
+- 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
+
+
+# Group G25 - Tools & Limitations
+
+## (v) Tools
+- 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)
+
+## (vi) Limitations
+- 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