由設計師 Xilin Tang 創作嘅 Sky Route AI 送貨無人機係備受推崇嘅 2025 A' Design Award 銅獎得獎作品，獲獎類別為未來設計。

銅未來· 2025

Sky Route AI 送貨無人機

由 Xilin Tang

客戶: Osrbot

未來類別嘅 A' Design Award 銅獎得獎者

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經驗證嘅獲獎者

驗證設計 #171188 嘅獲獎狀態

喺 A' Design Award 查看新聞稿獎項徽章

色彩

設計

關於 Sky Route AI 送貨無人機

Sky Route AI 係一款專為多點停靠、最後一里路物流而設計嘅送貨無人機。單一航程可以攜帶多件包裹，靠人工智能進行動態路線規劃，並透過LiDAR同鏡頭模組實現實時障礙物偵測。機身採用碳纖維複合材料製造，經CNC切割同樹脂灌注成型，可摺疊機臂令佢能夠喺狹窄嘅城市空間中飛行。無刷馬達每次充電可提供長達45分鐘嘅飛行時間，最大有效載重達3公斤。設計目的在於降低成本、縮短等候時間並減少排放。

榮譽認可

關於A' Design Award 銅獎

Sky Route AI 送貨無人機是 A' Design Award 銅獎得主，這是授予以創造力、獨創性和敏銳見長的傑出設計的極具聲望的認可。銅獎得主是技藝嫻熟、創意驚人的作品，印證了其設計師豐富的經驗、想像力與機智。預計Sky Route AI 送貨無人機將成為一件備受推崇、令人欽佩且被珍愛的物品，兼具顯著的專業性與創新性。這件贏得 A' Design Award 銅獎的設計融入藝術、科學、設計和技術領域的最佳實踐，並因注重細節而出類拔萃。Sky Route AI 送貨無人機出色地滿足需求，觸動渴望，迷住情感，並吸引好奇心。

設計背後

新聞資料包

由設計師提供嘅製作人員名單、團隊成員同技術詳情。

獨特屬性: This drone redefines last mile logistics by combining advanced AI with a carbon fiber body for repeated multi stop operations. Optimized to reduce flight time and emissions, it integrates real time obstacle detection and dynamic route planning. Its efficient multi takeoff capability lowers operational costs and ensures swift deliveries, distinguishing it from conventional single route drones. Additionally, advanced AI simulations accelerate development cycles for consistent and safe performance.
尺寸同包裝: Overall dimensions: 800 mm wide x 800 mm deep x 400 mm high, total weight approximately 3.5 kg. The carbon fiber chassis ensures a payload capacity of up to 3 kg. Brushless motors enable up to 45 minutes of flight time per charge. Integrated LiDAR and multi camera arrays provide real time obstacle detection, while modular battery packs facilitate efficient multi stop operations in various urban settings.
標籤: Drone, multi stop delivery, AI powered, carbon fiber, last mile logistics, eco friendly, cost efficient, advanced route planning, LiDAR, real time obstacle detection
生產技術: Constructed from carbon fiber composites to maximize flight efficiency, the drone is realized through CNC cutting and resin infusion processes. AI driven simulation tools guide iterative refinements, optimizing aerodynamic performance and structural integrity. LiDAR and camera modules integrate seamlessly within modular housings, while brushless motors and a robust flight controller ensure high precision and reliability during multi stop missions.
設計挑戰: Integrating multi stop delivery AI with real time obstacle detection proved the toughest hurdle. Historically, drones had limited payload capacity; socially, public acceptance demanded robust safety features. Externally, evolving regulations demanded compliance, while scarce data and advanced materials constrained prototyping. Overcoming these obstacles shaped a reliable, efficient system.
研究: We conducted applied research aiming to cut delivery costs and boost reliability. Using iterative prototyping and AI simulations, we collected flight and route data with sensor arrays. Test flights with volunteers showed multi package deliveries reduced wait times by 30%. This insight informed safer navigation, driving business efficiency, easing urban congestion, and inspiring new drone design standards.
靈感: Inspired by the global need for more flexible, cost efficient, and eco friendly logistics, we harnessed advanced AI simulations to refine multi stop capabilities in a carbon fiber drone. Rising labor costs and congested roads highlighted the urgency to innovate last mile delivery, so our research focused on optimizing flight performance, payload handling, and dynamic route planning.
項目時長: The project started in October 2024 in Auburn, Alabama, USA, and ended in January 2025 in Auburn, Alabama, USA.
操作流程: SkyRoute AI is operated via a user friendly app or controller. After takeoff, it can fold or unfold its arms to carry multiple packages, navigate narrow urban spaces, and revert to a stable form. With real time obstacle detection and dynamic route updates, it delivers multiple packages to different locations in a single run. This flexible, efficient approach reduces wait times, lowers costs, and outperforms traditional drones in speed and reliability.
圖片來源: Image #1: Designer XilinTang, Auburn, 2024
Image #2: Designer XilinTang, Auburn, 2024
Image #3: Designer XilinTang, Auburn, 2024
Image #4: Designer XilinTang, Auburn, 2024
Image #5: Designer XilinTang, Auburn, 2024