Hive Autonomy provides a supervised autonomy platform for heavy machinery and intralogistics equipment, enabling semi‑autonomous operation under human oversight. By fusing LiDAR, radar, and camera data with real‑time motion planning and a lightweight supervisory UI, the system lets operators monitor, intervene, or override equipment remotely, reducing manual labor, improving throughput, and maintaining safety in construction, mining, ports, and warehouse logistics.
Funding
Funding not disclosed
Founders
Product
Problem
Operators of heavy machinery and intralogistics equipment often require constant manual control to ensure safety and productivity, leading to high labor costs, limited operational efficiency, and increased risk of human error.
Solution
Hive Autonomy develops a supervised autonomy system that enables heavy machinery and intralogistics assets to operate semi‑autonomously under human oversight. The platform combines sensor fusion, real‑time perception, and adaptive control algorithms to handle routine motions while allowing operators to intervene when needed. By integrating a lightweight supervisory interface, the system provides situational awareness, remote monitoring, and instant override capabilities. This approach reduces the need for continuous manual control, improves throughput, and maintains safety standards in complex industrial environments.
Target Audience
Primary customers are operators and fleet managers in construction, mining, ports, and warehouse logistics who seek to augment heavy equipment with safe, semi‑autonomous capabilities.
Features
- Sensor‑fusion stack that aggregates LiDAR, radar, and camera data for robust environment mapping
- Real‑time motion planning engine optimized for heavy‑load dynamics and constrained spaces
- Supervisory UI that visualizes machine intent, alerts operators, and supports one‑click manual takeover
- Remote monitoring dashboard with live telemetry, health diagnostics, and predictive maintenance alerts
- Scalable software architecture allowing integration with existing fleet management and PLC systems