BioMorphic Intelligence Lab
Biologically inspired solutions for aerial robotics
Aerial robots are now ubiquitous. Thanks to their nimbleness, manoeuvrability, and affordability, drones are used in many sectors to monitor, map, and inspect. As a next step, flying robots offer more when interacting with their surroundings via anthropomorphic-like manipulation capabilities. Some overarching challenges remain for this new class of aerial robots, and solutions inspired by biology can be implemented across three key areas for robot performance: sensing their environment, processing this information, and acting upon the results.
Bio-inspired perception (e.g., visual or tactile feedback) can provide the drone with information on its environment, mimicking animals’ sensory feedback. Using retina-like event cameras, drones can avoid obstacles and detect objects at a fraction of the power and latency of conventional hardware and algorithms. Enhancing tactile feedback can also prompt different behaviors in response to different force stimuli.
Bio-inspired, brain-like models from Neuromorphic AI can help lower the computational load and speed up sensory data processing for navigation. This boosts real-time control and autonomy. Compliance embedded in the control of the robot also favors safe and robust interaction with unknown environments and targets.
Bio-inspired design and materials make the drone's body fit for interaction with unknown objects and enable a safe response to external disturbances. Robot morphology can be inspired by flying animals’ shape, configuration, and materials. Together, these features create embodied intelligence and can partially offset the behavior complexity handled by the brain.
The BioMorphic Intelligence Lab aims to tackle robustness and efficiency challenges for interacting drones, using biologically inspired solutions for both the 'body' and the 'brain' and applying embodied intelligence and neuromorphic AI techniques.
The BioMorphic Intelligence Lab is part of the TU Delft AI Labs programme.
- Every Semester: Design Synthesis Exercise (DSE) for Bachelor Students. The projects are always within the field of “Drones for Environmental Monitoring”.
- Project supervision in the Deep Learning (CS4240) course for MSc Computer Science.
- Project supervision in the Research Project (CSE3000) course for BSc Computer Science.
- Guest lecture on "Event-Based Vision" in the Computer Vision by Deep Learning (CS4245) course for MSc Computer Science
- Guest lecture on "Neuromorphic Computing" in the Machine Learning 2 (CS4230) course for MSc Computer Science.
- Supervision of MSc Graduation Projects for the Computer Science program (IN5000), see below for open project topics.
AI minor and AI Master Block programs
- Project supervision for the Capstone Applied AI Project (TI3150TU)
- Fundamentals of AI (IFEEMCS520100) and
- Interdisciplinary Advanced AI Project (IFEEMCS520200) courses.
- MSc Course “BioMorphic Intelligence for Interacting Drones”
- Contour Following of Tunnel Walls with the OmniDrone
- Soft Manipulation for Perching with Aerial Robots
- Soft Aerial Robots Dealing with Collisions
- An AI-based Approach to Dynamic In-gust Flight of flapping wing MAVs
- Pantograph: A Dynamically Balanced Manipulator for Aerial Applications
- Battle the Wind: Improving Flight Stability of a Flapping Wing Micro Air Vehicle under Wind Disturbance
- Automated Aerial Screwing with a Fully Actuated Aerial Manipulator