Lingfeng Smart Delivery | Air-Ground Collaborative Robot

Lingfeng Smart Delivery was an air-ground collaborative delivery concept for semi-enclosed environments such as campuses and residential communities. The proposed service combined an autonomous ground vehicle, a drone, and a WeChat Mini Program: the ground vehicle handled longer routes on flat roads, the drone extended delivery to upper floors, windows, or corridors, and the Mini Program connected ordering, route visibility, and delivery feedback. I served as CAO in a 15-person interdisciplinary team and owned competitive analysis, business-model design, and financial forecasting, translating the technical concept into differentiation, revenue paths, and funding assumptions.

The problem was broader than delivery speed. First, peak demand and access restrictions could overwhelm human delivery and make arrival times unreliable. Second, ground-only autonomous vehicles depended on accessible roads and could not reach elevated or precise last-mile destinations. Third, delivery inside a campus or residential compound was shaped by site access, safety, and institutional approval: the product had to work for students and residents while also earning permission from a school, property manager, or logistics partner. The opportunity was therefore to connect complementary transport modes to a workable service system rather than simply add another device.

The product and business goals were to identify viable campus and community entry scenarios; distinguish site partners, buyers, and end users; define differentiation from human delivery and ground-only vehicles; connect user orders, site partnerships, and device or service fees into a coherent business model; and surface safety, regulatory, adoption, competitive, and cost risks. Delivery efficiency, unit cost, order volume, and adoption outcomes are [Placeholder: pilot metrics and results]. The page does not present intended outcomes as validated impact.

The competitive analysis compared DJI drones, Qiteng’s power-inspection robot, and the proposed product across operating precision, range, automation, digital capabilities, and price. Mature drone solutions offered three-dimensional reconstruction and road-independent operation, while ground-inspection robots supported real-time transmission and rapid judgment. The proposed differentiation centered on autonomous perception and obstacle avoidance, air-ground collaboration, a ground-station software platform, real-time feedback, and configurable deployment. The business plan also applied Five Forces to suppliers, buyers, entrants, substitutes, and industry rivals, connecting feature comparison to cost, entry barriers, and substitution risk.

The business model combined product, platform, and service revenue. Upstream inputs included airframes, motors, flight control, batteries, sensors, and camera equipment; the team supplied R&D, production, and service capabilities; revenue paths included UAV products, an air-ground management platform, and customized route design. Downstream scenarios covered campus and community delivery as well as the power, bridge, and road inspection extensions shown in the pitch deck. The campus and community concept used a B2B+B2C model with device sales or leasing, platform fees, order commissions, route-design fees, and individual order payments. These were project-stage proposals, not evidence of realized revenue.

My work covered three connected tracks. First, I built a competitor matrix that normalized technical descriptions into precision, range, automation, digital capability, and price, making the product’s advantages and limitations explicit. Second, I mapped industry-chain roles, core capabilities, customer scenarios, and charging mechanisms into a business model connecting R&D, production, platform services, and downstream applications. Third, I used manufacturing, operations, marketing, and staffing assumptions to prepare five-year revenue and profit forecasts and to design the financing size, equity share, and use of funds. The technical concept was developed jointly by team members from computer science, artificial intelligence, software, mechanical engineering, and business disciplines; my responsibility was to keep the technical capabilities, commercial logic, and financial assumptions aligned.

The project won a National Second Prize in the iCAN College Student Innovation and Entrepreneurship Competition and a Provincial Silver Award in the Internet+ College Student Innovation and Entrepreneurship Competition. According to the source material, the team also placed seventh nationally in the 2021 Smart Flight UAV Perception Competition and was the only undergraduate team among the award recipients; the smart-delivery system was then in the intellectual-property application stage. The application number and current legal status, pilot sites, user and order data, delivery efficiency, and cost outcomes are [Placeholder]. Market-size, five-year revenue, profit, and financing figures in the plan were forecasts and are not presented as achieved results.

The project taught me that a complete business plan is not the same as a validated product. The proposal covered market, competition, positioning, marketing, and financial projections, but a stronger next phase would test four questions first: whether institutional decision makers would open their sites and accept the partnership cost, whether end users would pay for delivery, whether air-ground collaboration materially outperformed simpler ground-only or human services, and whether regulation and unit economics supported continued operation. A focused campus MVP, decision-maker interviews, end-user tests, a service blueprint, and a real cost model would provide stronger evidence than broad market assumptions and long-range projections.