AI for Delivery Robots: Navigation, Teleoperation, Fleet Monitoring
Imagine a delivery robot stuck at a curb, failing to see a puddle, or losing GPS under a bridge. In unstructured urban environments, standard algorithms fall short. We solve this with a stack combining computer vision, reinforcement learning, and teleoperation. Our experience: 5+ years in AI for robotics, over 30 deployed projects. Key challenges include terrain irregularities, dynamic obstacles, and lighting variations. We built a system that processes 30 frames per second from multiple data sources, achieving localization accuracy of 10 cm. Unlike traditional approaches, our RL planner reduces manual interventions by 90% compared to rule-based systems, and AI deployment cuts delivery costs by up to 40%.
Project success hinges on precise ODD definition and data quality. We help collect and label datasets for model training and conduct simulation testing before real-world deployment. We'll assess your project and propose a turnkey solution.
What Technical Problems We Solve
City sidewalks are full of edge cases: uneven surfaces, ramps, intersections, unpredictable pedestrians. A delivery robot must:
- Detect objects with latency under 33 ms (30 FPS).
- Distinguish surface types (asphalt, grass, puddle, snow).
- Plan trajectories in real time considering dynamic obstacles.
- Handle temporary obstacles (construction fences, crowds).
Standard SLAM algorithms fail in textureless environments. We use Visual SLAM with IMU fusion and SLAM for 10 cm accuracy.
Why AI Is Critical for Delivery Robots
Without AI, a robot cannot adapt to environmental changes. We combine detection, segmentation, and depth estimation. The sensor package includes:
- 9–12 cameras for 360° coverage.
- 2–4 LiDARs (Livox Mid-360 or solid-state).
- Ultrasonic sensors for close range.
- RTK GPS + Visual SLAM.
Detection is built on YOLOv8 and RT-DETR, segmentation on SegFormer. All optimized for NVIDIA Jetson Orin NX via TensorRT, achieving 30+ FPS per stream.
How We Achieve 95%+ Success Rate in Urban Environments
Global path planning uses an HD map of sidewalks (OSM + custom annotations). The segment graph includes attributes: width, surface type, curb presence, lighting.
The local planner uses RL (TD3) with continuous velocity space. Input is a 64×64 m BEV with semantic layers. The agent is trained in Isaac Sim (NVIDIA Omniverse) with photorealistic urban scenes over a 10-second horizon.
For unusual situations, we deploy an OOD detector with safe-stop and operator request.
| Situation | Strategy |
|---|---|
| Curb without ramp | Bypass via HD map / search for ramp |
| Puddle / snow | Reduce speed, bypass |
| Construction fence | Replan global route |
| Crowd of pedestrians | Stop, wait for passage |
| Off-leash dog | Gentle stop, bypass |
Details of sensor package and models
- Cameras: fisheye, 1–2 MP, 30 FPS. - LiDAR: Livox Mid-360, 360° x 90° FOV. - IMU: inertial module with 6 DOF. - Compute: NVIDIA Jetson Orin NX, 100 TOPS. - Models: YOLOv8l, SegFormer-B2, UniDepth.Comparison: LiDAR vs Cameras
| Parameter | LiDAR | Cameras | Combination |
|---|---|---|---|
| Range | up to 200 m | up to 100 m (stereo) | 200+ m |
| Accuracy in darkness | 100% | low | high |
| Semantic information | no | yes | yes |
| Cost | high | low | medium |
YOLOv8 detection achieves mAP 0.55 on Cityscapes, segmentation IoU 0.78.
Human-in-the-Loop Teleoperation
Full autonomy is achievable only within a well-defined ODD. Initially, some edge cases are handled by teleoperators:
- Video stream from 4 cameras (WebRTC, <200 ms latency).
- Control via gamepad.
- All sessions logged for DAgger training.
- Intervention rate: first month 15–25%, after 6 months 1–3%.
Fleet Management and Monitoring
Centralized Fleet Controller:
- Order dispatching to nearest free robot with charge consideration.
- Predictive charging with 20% buffer.
- Kafka + TimescaleDB for real-time monitoring.
Key metrics: Mission Success Rate >95%, Average Delivery Time deviation <10%, Intervention Rate, MTBF >200 hours.
Implementation Process
- Analysis: environment audit, ODD definition, data collection (3–4 weeks).
- Design: sensor architecture, model selection, pipeline (2–3 weeks).
- Development: calibration, model training, integration (8–12 weeks).
- Testing: simulation, real-world tests, iterations (4–6 weeks).
- Deployment: fleet installation, fleet management setup (2–3 weeks).
Timeline: MVP with basic sidewalk navigation — 4–5 months. Full system with teleoperation and fleet management — 9–12 months. Cost is calculated individually. Request an analysis of your tasks — we'll find the optimal configuration.
What's Included
- Documentation: architecture description, operation manuals.
- Customer team training (2–3 days).
- Access to monitoring dashboard.
- Operational support (SLA).
We guarantee Mission Success Rate >95% after stabilization. Get a consultation on implementing an AI system for your project. Contact us to discuss details.