Johnny Fernandes
56 lines
2.6 KiB
Markdown
56 lines
2.6 KiB
Markdown
# Group G25 - Formal & Title & Goals
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## Team members
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- Diogo Costa <up202502576@up.pt>
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- Johnny Fernandes <up202402612@up.pt>
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- Nelson Neto <up202108117@up.pt>
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## (i) Title and General objectives
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**RL-Based Autonomous Shepherd Robot for Livestock Herding**
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- Implement effective herding behaviors through proximity and movement strategies
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- Build a 3D environment with realistic robot dynamics and LIDAR-based perception
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- Develop a mobile robot capable of autonomously guiding a flock of sheep into a designated target area using Reinforcement Learning
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# Group G25 - (ii) Intermediate Goals
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## Intermediate goals
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- Set up the Webots simulation environment with an open field and target zone
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- Implement lightweight Gymnasium-based 2D herding environment
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- Design a Sheep and Dog robot
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- Implement a sheep flocking model for fast RL iteration
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- Validate LiDAR sensor feedback for sheep detection and distance estimation
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# Group G25 - Course Project (Final) Goals
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## (iii) Main goals
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- State-of-the-art survey on shepherding algorithms and multi-agent RL herding
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- Train the robot using PPO to successfully herd a single sheep into the goal
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- Achieve fully autonomous herding of multiple sheep and a full flock into the target area
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- Optimize robot trajectory to minimize the time required to group the flock
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- Ensure zero collisions between the robot and the sheep during the task
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- Quantitative evaluation: success rate, time-to-pen, flock dispersion metrics
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- Article, demo video, and final presentation
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## (iv) Extra Merit
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- Curriculum Learning (scaling from 1 sheep to a flock)
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- Comparison of performance between Differential Drive and Mecanum wheels
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- Robustness testing under sensor noise or varying sheep speeds, configurations and parameters
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- Multi-shepherd cooperative mode: 2 dogs learn role specialization (collector vs. driver)
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- Obstacles and terrain (walls, narrow gates, corridors) the flock must be funneled through
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# Group G25 - Tools & Limitations
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## (v) Tools
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- Webots for 3D physics simulation with ROS2 integration via `webots_ros2` package
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- Stable-Baselines3 for the PPO algorithm implementation
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- Gymnasium (OpenAI) for the RL environment wrapper (lightweight 2D herding env for fast RL training)
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- Python as the primary programming language (sheep flocking model, reward shaping, evaluation)
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## (vi) Limitations
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- Computational Power: Training time might be high for complex flock behaviors
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- Sim-to-Real Gap: No real-world validation of the herding controller; project is simulation-only (2D + Webots 3D)
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- Model Complexity: Simplified sheep behavior (scripted) may not account for all biological livestock nuances |