Competitions

As demand for unmanned aerial systems (UAS) surges across both civil and military sectors—including logistics, inspection, security, agriculture, disaster response, and defence—efficient and scalable drone production has become a strategic necessity.

Current assembly processes often rely heavily on manual labour, which limits throughput, increases production cost, and introduces variability in quality. To reach the required scale while ensuring precision, safety, and flexibility, the robotics community must develop new methods for autonomous, adaptable assembly of drone platforms.

Robotic manipulation systems offer a powerful pathway toward automated, modular drone assembly. However, challenges remain in part recognition, fine manipulation, and adaptability to component variability.

This hackathon invites students to address these challenges head-on.

Teams are tasked with developing a solution for the assembly of a drone from provided components.

The system must be capable of:

1. Perform the assembly of multiple configurations of drones (battery configuration, battery sizes, motor sizes).
2. Place components in such a way that weight distribution in the drone is correct.
3. Deal with changes in part weights and sizes to accommodate changes in the supply chain.

This challenge will be executed on a cobot with an adaptive two finger gripper, by teams of 3-4 students from March 24th to March 26th.

By the end of the hackathon, participants should aim to:

• Demonstrate the assembly process of a drone  with variable configurations
• Ensure accurate picking, alignment, and placement of drone components.
• Provide a user friendly way of dealing with changes in drone configuration and components.
• Validate approaches that can support scalable, modular drone manufacturing.

Participants may choose their own approach, but we suggest the following elements to be investigated:

• Balancing of the drone: dealing with different drone configurations and part weights requires an adaptive approach on determining the locations of parts to be placed.
• Correct gripping of parts: the robot is equipped with a generic, adaptive gripper that must be used for all the parts supplied.
• Creating accurate pick and place routines that can handle different placement positions and deal with the shape of the drone’s body.
• Creating a user friendly interface for configuration of the assembly.
• Create a robust solution that can deal with failures during the execution of the assembly

Teams will present:

• A working prototype
• A demonstration showing product and component configuration, execution of a fully automatic drone assembly and evaluation of correct parts placement by a centre of gravity test.

Entries will be evaluated on:

1. Autonomy – minimal manual intervention throughout the assembly process
2. Precision – accuracy in component placement, which will be evaluated by a balance test of the assembled drone
3. Robustness – ability to adapt to variation in parts placement and configuration
4. Speed – how much time does it cost to perform the assembly procedure
5. User friendliness – how easy is it for the user to configure the drone and to deal with part
6. Innovation – creativity in design, architecture, or approach

Rapid urbanization and the accelerating transition to new building materials and energy systems—such as solar panel installations—have led to an increased frequency and complexity of indoor fire incidents. In these environments, fires often occur in enclosed, partially obstructed, or structurally complex rooms where visibility is low and access for first responders is limited or dangerous.

Timely and precise intervention is critical. The sooner a fire is detected, localized, and contained, the more lives can be saved and the less property damage occurs. However, relying solely on human intervention in unpredictable or hazardous indoor environments places rescue teams at unacceptable risk.

To address this global safety challenge, we invite the international research and innovation community to push the boundaries of autonomous systems for emergency response.

Participants are tasked with developing an autonomous drone system capable of:

1. Exploring a previously unknown enclosed indoor room, without prior maps
2. Detecting and localizing a fire source, under constraints such as smoke, obstacles, and low visibility.
3. Implementing a safe fire-extinguishing action, using onboard tools or mechanisms appropriate to the scale of the demonstration scenario.

The solution should operate fully autonomously, making real-time decisions in dynamic, uncertain conditions.

By the end of this hackathon, teams should aim to:

• Demonstrate advanced autonomy in confined indoor environments.
• Integrate sensing, perception, navigation, and actuation for emergency-response scenarios.

Participants are free to choose their own methods, a drone equipped with rgb camera, thermal camera and laser will be provided.

Each team will present:

• A working prototype or simulation of the autonomous drone system
• A demonstration of autonomous exploration, fire detection, and extinguishing during ERF26
• A technical report or presentation explaining the approach, design decisions, and limitations

Solutions will be evaluated on:

1. Autonomy – minimal human intervention
2. Effectiveness – accuracy in locating and mitigating fire
3. Robustness – ability to handle obstacles, reduced visibility, and uncertainty
4. Speed – how fast the fires are extinguished
5. Safety – risk mitigation in hardware and algorithmic design