DRDO
Browse and strategize across all 7 official problem statements submitted by DRDO for Smart India Hackathon 2026.
Problem Statements (7)
Match My TeamModifications to improve the reliability, efficiency,and lifespan of electrical and electronic equipment and systems in the ambient condition of subzero temperature and low pressure of High Altitude Areas(HAA) and Super High Altitude Areas (SHAA) of Ladakh region.
Unmanned Aerial Vehicles (UAVs) and drones deployed in extreme desert and border regions face rapid motor overheating, bearing wear, and battery degradation under high ambient dust and temperature. Build a predictive digital twin and hardware telemetry analysis platform that ingests ESC telemetry, motor vibration FFT signatures, and thermal sensors to forecast drone subsystem failures and optimize propulsion lifespan for DRDO.
High Altitude Performance Optimization and Robust Design of Anti-Drone System.
Anti-drone jammers and directed energy counter-UAS systems deployed at high-altitude Himalayan forward posts (Ladakh/Sikkim >14,000 ft) suffer severe RF power amplifier overheating, sub-zero battery failure, and reduced RF beam propagation in thin air. Build an AI-assisted high-altitude anti-drone optimization and thermal-RF simulation platform that dynamically tunes RF jamming frequencies, manages thermal Peltier cooling, and maximizes jammer neutralization range.
Software Based Model Development for Design of Area Specific Shelter for Thermal Comfort Maintenance.
Indian armed forces stationed at extreme high-altitude forward posts (Siachen, Ladakh, Tawang >18,000 ft) require specialized military shelters that balance thermal insulation (-50°C), structural resistance against 150 km/h blizzard winds and heavy snow loads, and rapid modular assembly. Build a software-based parametric engineering simulation and CAD generative design platform that optimizes high-altitude shelter geometry, thermal composite materials, and solar heating efficiency.
To develop an AI/ML-enabled adaptive noise cancellation (ANC) system that effectively suppresses stationary, non-stationary, and impulsive defence noises while maintaining high speech intelligibility and real-time performance on embedded hardware.
Soldiers in tanks, armored combat vehicles, and military cockpits operate under extreme acoustic noise (>115 dB from diesel engines, gunshots, rotor blades) that renders tactical radio communication unintelligible and causes hearing loss. Build an AI-enabled low-latency Adaptive Noise Cancellation (ANC) and speech enhancement system in DSP/FPGA that suppresses non-stationary industrial/combat noise while preserving mission-critical voice clarity in under 5 milliseconds.
Adaptive Variable Resolution 2.5D Lidar Mapping for Dynamic Environment Perception
Autonomous military ground vehicles (UGVs) operating in dynamic tactical combat zones encounter high-speed incoming threats and complex rugged terrain, where fixed-resolution 3D LiDAR creates processing bottlenecks on irrelevant empty space while missing distant hazards. Build an Adaptive Variable-Resolution 2.5D/3D LiDAR Mapping and semantic obstacle segmentation engine that dynamically concentrates laser point density on regions of interest (ROI), moving threats, and traversability bottlenecks.
AI-Enabled Real-Time Digital Twin System for Health Monitoring, Fault Prediction and Mission Reliability Enhancement of Aero Piston Engines used in MALE UAVs.
Marine gas turbine propulsion engines on Indian Navy warships operate under brutal marine conditions (salt-air corrosion, thermal shock, variable combat load), where unexpected compressor fouling or turbine blade failure can disable a warship at sea. Build an AI-enabled real-time Digital Twin and predictive health monitoring system for marine gas turbines that ingests high-rate telemetry (spool speeds, exhaust gas temperatures, vibration FFT, fuel flow) to detect thermal degradation and predict component Remaining Useful Life (RUL).
Smart Scan strategy for Electronic Warfare
In modern contested electromagnetic battlefields, enemy radars constantly change pulse repetition frequencies (PRF), beam scanning patterns, and frequency-hopping sequences to evade detection, while conventional wideband Electronic Warfare (EW) receivers suffer from blind spots and high search latency. Build an AI-driven Smart Scan Strategy and cognitive radar interception platform for DRDO that uses Reinforcement Learning and Bayesian search to dynamically optimize receiver dwell times, anticipate agile radar sweeps, and maximize probability of intercept (POI).