Tlanāhuatīlli Sky Route AI Ilhuicatlamelahuacan Patlantli tlen Temaca ītēchcopa Xilin Tang cē cencā mahuizzō tēmacēhualōni īpan huēyi 2025 Mahuizolli A' Design Award Bronse īpan Hualmoquetzaz Tlachihchihualiztli Nēhuilīztli Tlamantli.
BronseHualmoquetzaz· 2025
Sky Route AI Ilhuicatlamelahuacan Patlantli tlen Temaca
īpal Xilin Tang
Tlanāhuatīlli: Osrbot
Mahuizolli A' Design Award Bronse Tēmacēhualōni īpan Hualmoquetzaz Tlamantli
Tlaneltīlīlli Tēmacēhualōni
Xictlaneltilia Tēmacēhualli ītēchcopa #171188Tlanēxtilistli
Ītechcopa Sky Route AI Ilhuicatlamelahuacan Patlantli tlen Temaca
Sky Route AI ye se patlantli tlen temaca in tlamantli, tlachihchiuhtli inic miak cahuan can tlamotlalo in tlamantli itzalan altepetl. Miak tlaximaltin quimotquilia ipan ceyoc patlantli, tlayecantli ihuan yolizmachiliztli tlaixmatiliztli (artificial intelligence) inic quitlalia ohtli nochipa yancuic, ihuan ica LiDAR ihuan camera tlatemoliztli quimatih iquin tlahtlacoli onca ohtli. Inacayo tlachihchiuhtli ica carbon fiber tlanechicoltin, tlaxeloltin ica CNC ihuan tlaahuachiuhtli ica resina, ihuan imahuan tlen huel motiloa quicahua ma patlani itzalan pilcaltin altepetl tlen amo huel patlani huey tlamantli. Motorestin amo tlapetlaniah quimacah patlantli ixquich 45 tonalcuepontli ipan ce tlatlatiliztli ihuan huel quitquiliz ixquich 3 kg. In yolo huelitiliztli quinequi ma motemo tomin, ma achiquentzin motemachia, ihuan ma amo miec ihiyotl popoca elehui.
Tēixīmachōtiliztli
Ītechcopa Mahuizolli A' Design Award Bronse
Sky Route AI Ilhuicatlamelahuacan Patlantli tlen Temaca ca in quicuic in Mahuizolli A' Design Award Bronse, hueyi tlamahuizoliztli tlen momaca in cualli tlachihualli in motenehua ipampa yancuic tlamantli, tlamatiliztli ihuan ixtlamatiliztli. In quicuicah Bronse ca tlachihualli in quipia toltecayotl ihuan quinextia in cenca miac nemiliztli ihuan tlamatiliztli intlachihuanime. Ca quinequi in Sky Route AI Ilhuicatlamelahuacan Patlantli tlen Temaca ca tlazohtli tlamantli, mahuiztic ihuan cualli. Ni dizayno in quicuic Bronse quipia cualli toltecayotl ihuan quitta in tlen tepiton. Sky Route AI Ilhuicatlamelahuacan Patlantli tlen Temaca quipaquiltia in tlacah ihuan quitlamahuiztiltia in intlanequiliz.
Tlanēxtilistli Ītēixpan
Āmatl Tlanextīliztli
Tlapōhualli, tēquitqueh īwan tēchmachilīztli ōquinmaca in tlanēxtīlīlli.
- Cē Zan Ītēchcopa
- 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.
- Huēyiliztli īhuān Tlacualtepēhualli
- 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.
- Tōcāitl
- Drone, multi stop delivery, AI powered, carbon fiber, last mile logistics, eco friendly, cost efficient, advanced route planning, LiDAR, real time obstacle detection
- Tlachīhualiztli Māchiyōtl
- 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.
- Tlanāhuatīlli Nētēcahuilīztli
- 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.
- Tlatēmōliztli
- 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.
- Tēyōllālīliztli
- 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.
- Tēquitl Cāhuitl
- The project started in October 2024 in Auburn, Alabama, USA, and ended in January 2025 in Auburn, Alabama, USA.
- Tēquitl Ōhuitl
- 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.
- Īxiptla Tlanēxtīlli
- 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
Tlacōhualli
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Tlanēxtīliztli Tlanāhuatīlli
Xiquitta ōc cequi tlanēxtīliztli tēixīmachōtīlli īpan inīn tlahtlamanilistli, tlapepenalli ītechcopa īnqualli.
