Checkpoint 2

docs/project.md

@@ -6,28 +6,28 @@
 - Nelson Neto <up202108117@up.pt>
 
 ## (i) Title and General objectives
-**RL-Based Autonomous Shepherd Robot for Livestock Herding**
+**Autonomous Shepherd Robot for Livestock Herding (Strömbom)**
 
 - 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
+- Develop a mobile robot capable of autonomously guiding a flock of sheep into a designated target area using the Strömbom heuristic approach
 
 
 # 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
+- Implement lightweight 2D herding environment for algorithm evaluation
 - Design a Sheep and Dog robot
-- Implement a sheep flocking model for fast RL iteration
+- Implement a sheep flocking model for fast Strömbom 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
+- State-of-the-art survey on shepherding algorithms with focus on Strömbom herding
+- Implement and tune Strömbom controller 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
@@ -35,7 +35,7 @@
 - Article, demo video, and final presentation
 
 ## (iv) Extra Merit
-- Curriculum Learning (scaling from 1 sheep to a flock)
+- Progressive evaluation (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)
@@ -46,11 +46,10 @@
 
 ## (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)
+- Gymnasium (OpenAI) for the simulation wrapper and evaluation tooling
 - 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
+- Computational Power: Large batch evaluation and parameter sweeps can still be time-consuming
 - 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