AI & Machine Learning
Develop innovative AI solutions that push the boundaries of machine learning, computer vision, and natural language processing.
Team: 4 max • Age: 17+
Due: Mar 28, 2026
6 Categories
Competition Categories
Explore our diverse range of innovation tracks and find your perfect challenge
Choose Your Track
Each category offers unique challenges designed to push your creative and technical boundaries
Autonomous Robotics
Designing and building autonomous robots capable of navigating complex environments and completing challenging tasks without human intervention requires an interdisciplinary engineering approach that integrates robotics, artificial intelligence, control systems, and embedded hardware design. The process begins with defining operational requirements: terrain type, environmental variability, task complexity, and safety constraints. At the hardware level, robots must be equipped with robust locomotion mechanisms (wheeled, tracked, or legged systems), high-fidelity sensors such as LiDAR, stereo cameras, IMUs, ultrasonic sensors, and GPS modules, and sufficient onboard computation. Sensor fusion algorithms are essential to combine multiple data streams into a coherent environmental model. For navigation, simultaneous localization and mapping (SLAM) techniques enable the robot to construct and update maps while estimating its position in real time. Path planning algorithms such as A*, D*, or RRT generate collision-free trajectories, while feedback control systems ensure stable motion execution. Task autonomy is achieved through machine learning models, computer vision systems, and decision-making frameworks like behavior trees or reinforcement learning policies. These components allow the robot to detect objects, interpret context, adapt to dynamic obstacles, and recover from uncertainty. Rigorous simulation, iterative prototyping, and field testing are crucial to validate reliability, robustness, and fault tolerance. Ultimately, fully autonomous robots enhance efficiency, safety, and scalability across industries including manufacturing, logistics, healthcare, agriculture, and space exploration.
Team: 4 max • Age: 16+
Due: Mar 28, 2026
Drone Innovation
Engineer advanced drone systems with specialized capabilities to perform high-impact operations in delivery, surveillance, agriculture, and emergency response by integrating aerospace engineering, embedded systems, and intelligent autonomy. The development process begins with airframe design optimization, balancing aerodynamics, payload capacity, endurance, and structural integrity. Material selection, propulsion systems, battery management, and flight efficiency directly influence operational range and reliability. Core subsystems include flight controllers, GNSS modules, inertial measurement units (IMUs), barometers, and vision sensors. Sensor fusion algorithms combine these inputs to ensure stable flight, accurate positioning, and obstacle avoidance. Advanced navigation frameworks incorporate real-time kinematics (RTK), computer vision, and AI-based path planning to enable autonomous missions in dynamic environments. For delivery applications, drones require secure payload mechanisms, route optimization algorithms, and geofencing compliance. Surveillance drones integrate high-resolution cameras, thermal imaging, and real-time data transmission systems. In agriculture, multispectral imaging and NDVI analysis support precision farming by monitoring crop health and irrigation needs. Rescue drones employ thermal sensors, LiDAR mapping, and robust communication systems to locate survivors in hazardous terrain. Edge computing capabilities allow onboard processing to reduce latency, while cloud integration enables fleet management and analytics. Safety redundancy, fail-safe protocols, encrypted communication, and regulatory compliance are essential for deployment. Through the convergence of robotics, AI, control theory, and aerospace systems engineering, advanced drone platforms expand operational efficiency, situational awareness, and rapid-response capabilities across critical sectors.
Team: 4 max • Age: 18+
Due: Mar 28, 2026
Industrial Automation
Design automation solutions that enhance efficiency, safety, and productivity in manufacturing and industrial processes by integrating control systems engineering, robotics, and industrial informatics. The objective is to reduce human error, optimize throughput, minimize downtime, and ensure consistent product quality across production cycles. The foundation of industrial automation lies in programmable logic controllers (PLCs), distributed control systems (DCS), and supervisory control and data acquisition (SCADA) platforms. These systems coordinate machinery, sensors, actuators, and production lines in real time. Industrial communication protocols such as Modbus, PROFINET, EtherCAT, and OPC UA enable seamless interoperability between devices and centralized monitoring systems. Robotic arms, automated guided vehicles (AGVs), and conveyor systems streamline repetitive and hazardous tasks such as material handling, welding, assembly, and packaging. Advanced control algorithms, including PID controllers and model predictive control (MPC), maintain process stability and precision. Machine vision systems enhance quality assurance by detecting defects and ensuring compliance with production standards. Predictive maintenance strategies leverage IoT sensors and machine learning to analyze vibration, temperature, and operational metrics, reducing unplanned downtime. Human-machine interfaces (HMIs) provide operators with real-time insights and diagnostic capabilities. Cybersecurity, redundancy, and safety standards such as ISO and IEC guidelines are critical to ensuring reliable and compliant deployment. By combining intelligent automation, data analytics, and robust system architecture, industrial automation solutions significantly improve operational performance, cost efficiency, and workplace safety.
Team: 6 max • Age: 18+
Due: Mar 31, 2026
IoT & Smart Systems
Create interconnected smart systems that leverage Internet of Things (IoT) technology to solve real-world problems across residential, urban, and industrial environments by integrating sensing, communication, data processing, and intelligent decision-making. These systems rely on distributed networks of sensors and actuators embedded in physical infrastructure to collect real-time data such as temperature, humidity, motion, energy consumption, traffic flow, and equipment performance. At the edge layer, microcontrollers and embedded devices perform local data acquisition and preliminary processing. Connectivity protocols such as MQTT, HTTP, LoRaWAN, Zigbee, or 5G enable reliable data transmission to centralized cloud platforms or edge servers. Cloud computing infrastructure supports scalable storage, analytics, and machine learning models that transform raw sensor data into actionable insights. In smart homes, IoT systems optimize energy usage, automate lighting and climate control, enhance security monitoring, and improve convenience through voice or mobile interfaces. In smart cities, interconnected systems manage traffic congestion, waste collection, public safety, and environmental monitoring. In industrial settings, Industrial IoT (IIoT) enables predictive maintenance, supply chain optimization, and real-time process control. Cybersecurity, data privacy, interoperability standards, and fault tolerance are critical design considerations to ensure reliability and user trust. By combining hardware engineering, networking, cloud computing, and artificial intelligence, IoT-driven smart systems improve efficiency, sustainability, safety, and operational intelligence across multiple domains.
Team: 5 max • Age: 16+
Due: Mar 28, 2026
Space Robotics
Innovate robotic systems engineered specifically for space exploration, satellite servicing, and extraterrestrial operations by integrating aerospace engineering, advanced robotics, and autonomous control architectures. These systems must operate reliably in extreme environments characterized by microgravity, radiation exposure, vacuum conditions, and severe temperature fluctuations. Space robotics development begins with lightweight, high-strength structural design using advanced composite materials and radiation-hardened electronics. Mobility mechanisms vary depending on mission context: articulated robotic arms for satellite servicing, wheeled or legged rovers for planetary exploration, and free-flying robotic platforms for orbital inspection. Precision actuators and fault-tolerant control systems ensure stability and maneuverability in low-gravity or zero-gravity conditions. Autonomy is critical due to communication latency between spacecraft and ground control. Artificial intelligence algorithms enable real-time decision-making, terrain analysis, obstacle avoidance, and adaptive mission planning. Vision systems, LiDAR, and multispectral imaging sensors support navigation, mapping, and scientific data collection. For satellite servicing, robotic manipulators perform tasks such as refueling, repair, and component replacement using force-feedback control and high-precision guidance. Energy management systems incorporate solar arrays, advanced batteries, and efficient power distribution units to sustain long-duration missions. Redundancy, modularity, and radiation shielding enhance system resilience. Rigorous simulation, hardware-in-the-loop testing, and environmental validation are essential prior to launch. By advancing space robotics technologies, these systems expand human capability beyond Earth, enabling sustainable exploration, orbital maintenance, and long-term extraterrestrial infrastructure development.
Team: 4 max • Age: 16+
Due: Mar 31, 2026
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