{"id":25437700,"url":"https://github.com/christianlin0420/robotic-control","last_synced_at":"2026-02-14T19:02:33.506Z","repository":{"id":113205749,"uuid":"462668719","full_name":"ChristianLin0420/Robotic-Control","owner":"ChristianLin0420","description":"This repository contains a comprehensive collection of robotics control implementations, including path planning, path tracking, and reinforcement learning-based control algorithms from NTHU robotic control class.","archived":false,"fork":false,"pushed_at":"2025-01-15T10:01:30.000Z","size":3248,"stargazers_count":2,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-04-08T09:47:56.376Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":"","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/ChristianLin0420.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2022-02-23T09:35:20.000Z","updated_at":"2025-01-16T03:22:16.000Z","dependencies_parsed_at":null,"dependency_job_id":"faf86bc4-e33f-4bf8-b13a-8b14769335dd","html_url":"https://github.com/ChristianLin0420/Robotic-Control","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/ChristianLin0420/Robotic-Control","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ChristianLin0420%2FRobotic-Control","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ChristianLin0420%2FRobotic-Control/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ChristianLin0420%2FRobotic-Control/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ChristianLin0420%2FRobotic-Control/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/ChristianLin0420","download_url":"https://codeload.github.com/ChristianLin0420/Robotic-Control/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ChristianLin0420%2FRobotic-Control/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":29452590,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-02-14T15:52:44.973Z","status":"ssl_error","status_checked_at":"2026-02-14T15:52:11.208Z","response_time":53,"last_error":"SSL_connect returned=1 errno=0 peeraddr=140.82.121.6:443 state=error: unexpected eof while reading","robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":false,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":[],"created_at":"2025-02-17T09:19:28.878Z","updated_at":"2026-02-14T19:02:33.482Z","avatar_url":"https://github.com/ChristianLin0420.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Robotic Control Project\n\nThis repository contains a comprehensive collection of robotics control implementations, including path planning, path tracking, and reinforcement learning-based control algorithms. The project is structured into multiple assignments (Hw1-4) and laboratory exercises (Lab1-2), each focusing on different aspects of robotic control.\n\n## Project Structure\n\n```\n.\n├── Hw1/         # Basic motion models and path tracking\n├── Hw2/         # Advanced control implementations\n├── Hw3/         # Extended robotics algorithms\n├── Hw4/         # Reinforcement learning for path tracking\n├── Lab1/        # Laboratory exercises for motion models\n└── Lab2/        # Laboratory exercises for path planning\n```\n\n## Features\n\n### Motion Models\n- Basic motion model\n- Differential drive model\n- Bicycle model\n\n### Path Tracking Controllers\n- PID Controller\n- Pure Pursuit Controller\n- Stanley Controller\n- LQR Controller\n\n### Path Planning Algorithms\n- A* Algorithm\n- RRT (Rapidly-exploring Random Tree)\n- RRT* (Optimal RRT)\n\n### Reinforcement Learning\n- PPO (Proximal Policy Optimization) implementation\n- Policy and Value Networks\n- Path tracking environment\n\n## Requirements\n\nThe project requires the following main dependencies:\n- Python 3.x\n- PyTorch\n- OpenCV (cv2)\n- NumPy\n- Matplotlib\n\n## Usage\n\n### Basic Navigation and Path Tracking\n\n```bash\npython navigation.py [-s SIMULATOR] [-c CONTROLLER] [-p PLANNER] [-m MAP]\n\nOptions:\n  -s, --simulator    Select simulator type (basic/diff_drive/bicycle)\n  -c, --controller   Select controller type (pid/pure_pursuit/stanley/lqr)\n  -p, --planner     Select planner type (a_star/rrt/rrt_star)\n  -m, --map         Specify map file path\n```\n\n### Reinforcement Learning (Hw4)\n\nTraining:\n```bash\npython train.py\n```\n\nEvaluation:\n```bash\npython eval.py\n```\n\nPlaying with trained model:\n```bash\npython play.py [--stoch]\n```\n\n## Implementation Details\n\n### Simulators\n- Basic simulator: Simple point-mass model\n- Differential drive: Two-wheeled robot model\n- Bicycle model: Car-like robot with steering\n\n### Controllers\n- PID: Classical proportional-integral-derivative control\n- Pure Pursuit: Geometric path tracking method\n- Stanley: Popular steering control method\n- LQR: Linear Quadratic Regulator for optimal control\n\n### Path Planners\n- A*: Optimal graph search algorithm\n- RRT: Sampling-based motion planning\n- RRT*: Asymptotically optimal version of RRT\n\n### Reinforcement Learning (Hw4)\n- PPO implementation for path tracking\n- Custom environment wrapper\n- Policy and value network architectures\n- Training, evaluation, and visualization tools\n\n## Key Files\n\n- `navigation.py`: Main script for running path planning and tracking\n- `train.py`: Training script for reinforcement learning\n- `eval.py`: Evaluation script for trained models\n- `play.py`: Visualization script for trained models\n\n## Controls\n\n- Mouse Click: Set navigation goal point\n- 'R' key: Reset simulation\n- 'ESC' key: Exit program\n\n## License\n\nThis project is part of an academic course and should be used accordingly.","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fchristianlin0420%2Frobotic-control","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fchristianlin0420%2Frobotic-control","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fchristianlin0420%2Frobotic-control/lists"}