{"adoption": {"forks": 144, "observed_at": "2026-08-28T04:03:47.292571+00:00", "stars": 1153}, "canonical_url": "https://ross.abutalabs.com/products/yopo", "card": {"archived": false, "artifact_type": "library", "description": "You Only Plan Once: A Learning Based Quadrotor Planner", "domain": ["robotics", "autonomous-vehicles", "machine-learning", "deep-learning"], "enriched": true, "function": ["machine-learning", "deep-learning", "robotics", "simulation"], "health_score": 95, "homepage": null, "language": "C++", "license": "NOASSERTION", "license_family": "other", "maturity": "active", "member_repos": ["TJU-Aerial-Robotics/YOPO"], "name": "TJU-Aerial-Robotics/YOPO", "platform": ["cpp"], "pushed_at": "2026-08-15T03:50:26+00:00", "repo": "TJU-Aerial-Robotics/YOPO", "stars": 1153, "tags": ["quadrotor", "path-planning", "drone-navigation", "guidance-learning", "motion-primitives", "obstacle-avoidance", "research-code", "linux", "ros"], "topics": [], "urls": [], "use_cases": ["plan trajectories for a quadrotor in obstacle-dense environments", "train a neural network drone planner with guidance learning", "build an autonomous navigation stack for a drone", "run agile tracking and navigation from perception to action", "replace classical front-end search and back-end optimization with a single network", "reproduce research on learning-based quadrotor planning"], "what_it_is": "YOPO is a learning-based one-stage planner for quadrotor autonomous navigation in obstacle-dense environments, integrating perception, mapping, path searching, and trajectory optimization into a single neural network. It uses motion primitive anchors with a guidance-learning training strategy that back-propagates trajectory cost gradients instead of imitation or reinforcement learning.", "when_to_avoid": ["you need a general-purpose planner for ground robots or manipulators", "you require a fully certified or safety-verified flight stack", "you want a plug-and-play commercial product rather than research code", "your project does not use ROS or C++"], "when_to_choose": ["you need fast, one-stage trajectory planning for quadrotors in cluttered spaces", "you want a learning-based planner without simulator-in-the-loop RL training", "you are doing research on drone navigation and want a strong open-source baseline", "you want matching open-source drone hardware designs for real-world experiments"]}, "data_as_of": "2026-08-30T08:39:29.467469+00:00", "members": [{"path": "/products/yopo", "repo": "TJU-Aerial-Robotics/YOPO", "role": "main", "score": 79}], "provenance": {"archived": {"kind": "observed", "observed_at": "2026-08-28T04:03:47.292571+00:00", "source": "github"}, "artifact_type": {"confidence": null, "enriched_at": "2026-08-30T06:33:26.539020+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "c4fd764fc6e702935fb509c65db31c475342cf0232e431755f3873ff56178934", "fetched_at": "2026-08-28T04:03:47.292571+00:00", "kind": "readme", "missing": false, "url": "https://github.com/TJU-Aerial-Robotics/YOPO"}], "taxonomy_version": 1}, "description": {"kind": "observed", "observed_at": "2026-08-28T04:03:47.292571+00:00", "source": "github"}, "domain": {"confidence": null, "enriched_at": "2026-08-30T06:33:26.539020+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "c4fd764fc6e702935fb509c65db31c475342cf0232e431755f3873ff56178934", "fetched_at": "2026-08-28T04:03:47.292571+00:00", "kind": "readme", "missing": false, "url": "https://github.com/TJU-Aerial-Robotics/YOPO"}], "taxonomy_version": 1}, "enriched": {"inputs": [], "kind": "computed", "method": "enrichment_status"}, "function": {"confidence": null, "enriched_at": "2026-08-30T06:33:26.539020+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "c4fd764fc6e702935fb509c65db31c475342cf0232e431755f3873ff56178934", "fetched_at": "2026-08-28T04:03:47.292571+00:00", "kind": "readme", "missing": false, "url": "https://github.com/TJU-Aerial-Robotics/YOPO"}], "taxonomy_version": 1}, "health_score": {"inputs": ["days_since_push", "days_since_release", "archived"], "kind": "computed", "method": "health_v1"}, "homepage": {"kind": "observed", "observed_at": "2026-08-28T04:03:47.292571+00:00", "source": "github"}, "language": {"kind": "observed", "observed_at": "2026-08-28T04:03:47.292571+00:00", "source": "github"}, "license": {"kind": "observed", "observed_at": "2026-08-28T04:03:47.292571+00:00", "source": "github"}, "license_family": {"inputs": ["license"], "kind": "computed", "method": "license_family"}, "maturity": {"confidence": null, "enriched_at": "2026-08-30T06:33:26.539020+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "c4fd764fc6e702935fb509c65db31c475342cf0232e431755f3873ff56178934", "fetched_at": "2026-08-28T04:03:47.292571+00:00", "kind": "readme", "missing": false, "url": "https://github.com/TJU-Aerial-Robotics/YOPO"}], "taxonomy_version": 1}, "member_repos": {"kind": "observed", "observed_at": "2026-08-28T04:03:47.292571+00:00", "source": "github"}, "name": {"kind": "observed", "observed_at": "2026-08-28T04:03:47.292571+00:00", "source": "github"}, "platform": {"confidence": null, "enriched_at": "2026-08-30T06:33:26.539020+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "c4fd764fc6e702935fb509c65db31c475342cf0232e431755f3873ff56178934", "fetched_at": "2026-08-28T04:03:47.292571+00:00", "kind": "readme", "missing": false, "url": "https://github.com/TJU-Aerial-Robotics/YOPO"}], "taxonomy_version": 1}, "pushed_at": {"kind": "observed", "observed_at": "2026-08-28T04:03:47.292571+00:00", "source": "github"}, "repo": {"kind": "observed", "observed_at": "2026-08-28T04:03:47.292571+00:00", "source": "github"}, "stars": {"kind": "observed", "observed_at": "2026-08-28T04:03:47.292571+00:00", "source": "github"}, "tags": {"confidence": null, "enriched_at": "2026-08-30T06:33:26.539020+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "c4fd764fc6e702935fb509c65db31c475342cf0232e431755f3873ff56178934", "fetched_at": "2026-08-28T04:03:47.292571+00:00", "kind": "readme", "missing": false, "url": "https://github.com/TJU-Aerial-Robotics/YOPO"}], "taxonomy_version": 1}, "topics": {"kind": "observed", "observed_at": "2026-08-28T04:03:47.292571+00:00", "source": "github"}, "urls": {"kind": "observed", "observed_at": "2026-08-28T04:03:47.292571+00:00", "source": "github"}, "use_cases": {"confidence": null, "enriched_at": "2026-08-30T06:33:26.539020+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "c4fd764fc6e702935fb509c65db31c475342cf0232e431755f3873ff56178934", "fetched_at": "2026-08-28T04:03:47.292571+00:00", "kind": "readme", "missing": false, "url": "https://github.com/TJU-Aerial-Robotics/YOPO"}], "taxonomy_version": 1}, "what_it_is": {"confidence": null, "enriched_at": "2026-08-30T06:33:26.539020+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "c4fd764fc6e702935fb509c65db31c475342cf0232e431755f3873ff56178934", "fetched_at": "2026-08-28T04:03:47.292571+00:00", "kind": "readme", "missing": false, "url": "https://github.com/TJU-Aerial-Robotics/YOPO"}], "taxonomy_version": 1}, "when_to_avoid": {"confidence": null, "enriched_at": "2026-08-30T06:33:26.539020+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "c4fd764fc6e702935fb509c65db31c475342cf0232e431755f3873ff56178934", "fetched_at": "2026-08-28T04:03:47.292571+00:00", "kind": "readme", "missing": false, "url": "https://github.com/TJU-Aerial-Robotics/YOPO"}], "taxonomy_version": 1}, "when_to_choose": {"confidence": null, "enriched_at": "2026-08-30T06:33:26.539020+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "c4fd764fc6e702935fb509c65db31c475342cf0232e431755f3873ff56178934", "fetched_at": "2026-08-28T04:03:47.292571+00:00", "kind": "readme", "missing": false, "url": "https://github.com/TJU-Aerial-Robotics/YOPO"}], "taxonomy_version": 1}}, "score": {"components": {"activity": 97, "longevity": 70, "rhythm": 62}, "computed_at": "2026-09-02T17:46:02.011165+00:00", "flags": ["no_license"], "formula": "round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)", "inputs": {"age_days": 993, "days_push": 18, "days_rel": 253, "gap_med": 29, "n_releases_24m": 2}, "score": 79, "version": 2}, "staleness": {"enrichment_outdated": false, "low_confidence": false, "scrape_days": 9, "stale_scrape": false}}