Data checked 2026-08-13. Data may be stale - beyond the review cycle.
Review cycles are documented on the Methodology page. Methodology
ZJU-FAST-Lab/ego-planner
Algorithm Tier A autonomous_navigation GPL-3.0
EGO-Planner is a local trajectory planner that does not require a prebuilt global map, designed for onboard UAV replanning in cluttered environments.
Engineering Snapshot
Use cases & tasks 4
- Best suited for
-
- UAVs operating in unknown or cluttered environments
- Onboard replanning with limited compute
- Primary tasks
-
- Autonomous navigation in GPS-denied or unstructured environments
- Obstacle avoidance and local path optimization
Stack & ecosystem
- Resource type
- Algorithm
- Ecosystem
- autonomous_navigation
License & compliance
- License
-
GPL-3.0(Inferred)
Lifecycle & freshness Show
- Maintenance
- Active
- Latest version
- Not recorded
- Last activity
- 2026-08-13
- Last checked
- 2026-08-13
- Verification
- Official confirmed
What It Solves
Local trajectory planning for UAVs in cluttered environments without a prebuilt global map, enabling onboard replanning.
Primary use cases
- Autonomous navigation in GPS-denied or unstructured environments
- Obstacle avoidance and local path optimization
Secondary use cases
- Research benchmark for local planners
- Integration with SLAM systems for full autonomy
When to Use
Consider when
- Need mapless local planning
- Real-time replanning at high frequencies
- GPL-3.0 license compatibility
Verify before adopting
- GPL-3.0 license implications for proprietary use
- Real-time performance on target hardware
- Integration effort with chosen flight stack (PX4, ArduPilot, etc.)
- Sensor requirements (depth camera, LiDAR)
Start Here
Adoption Checklist
- Needs verification GPL-3.0 license implications for proprietary use
- Needs verification Real-time performance on target hardware
- Needs verification Integration effort with chosen flight stack (PX4, ArduPilot, etc.)
- Needs verification Sensor requirements (depth camera, LiDAR)
Each check stays "needs verification" until an official source confirms it; unconfirmed items are never marked verified.
Known Limitations & Unknowns
Known limitations
- Requires local 3D sensor data (depth camera or LiDAR)
- No global path planning capability
- GPL-3.0 copyleft license may restrict commercial use
- Limited documentation on hardware-specific tuning
Not publicly verified
- Minimum compute requirements for real-time performance
- Supported flight stack integration examples
- Dynamic obstacle handling capabilities
- Multi-robot coordination support
Alternatives & Related Tools
Alternatives
- fast-planner — Alternative to
How is it used?
Start from the recorded entry points below, then validate against the technical checklist.
- Official site https://github.com/ZJU-FAST-Lab/ego-planner
- Repository https://github.com/ZJU-FAST-Lab/ego-planner
- Documentation https://github.com/ZJU-FAST-Lab/ego-planner
Technical checklist
- OK License identified Recorded: GPL-3.0
- OK Maintenance signal Active
- OK Verification status Official confirmed
- OK Source evidence attached 1 source record(s)
- NEEDS REVIEW Latest version recorded Not recorded
Official Links
Metadata & Governance
| License | GPL-3.0 — Inferred |
|---|---|
| Commercial model | Unknown |
| Maintenance status | Active |
| Verification status | Official confirmed — Confirmed via the official repository API responses in SourceRefs below. |
| Latest version | Not recorded |
| Latest release | Not recorded |
| Last activity | 2026-08-13 |
| Last checked | 2026-08-13 |
| First seen | Not recorded |
Dataset facts
Facts above come from the official dataset card only; unconfirmed fields stay unknown.
Related Resources & Dependencies
- fast-planner — alternative to (verified)
Recent Activity
New resource: ZJU-FAST-Lab/ego-planner
New repository resource added by the sprint promote pipeline.
Related Knowledge
Collections
- Autonomous Navigation & Planning — Open-source planning, avoidance, MPC and multi-agent stacks for autonomous UAV flight.