97 research outputs found
Architectural design-by-features
Design tasks, in particular architectural design tasks, have been found hard to support by means of computers. The main reason for this is that design is a problem solving process, which requires a dynamic way of handling information involved in the design process. The research presented in this paper focuses on this aspect of CAAD: the support of design tasks with dynamic, flexible information modelling techniques. The basic concepts for the developed approach is taken from the field of Feature-based modelling. We briefly review these concepts and then interpret and transport them to the context of architectural design. In defining types of Features, a distinction is made between domain-specific Features and generic Features for which we propose a classification. A framework for the definition and modelling of Features is discussed as well as a prototype Feature-based Modelling Shell based on this framework
Neuromorphic Control using Input-Weighted Threshold Adaptation
Neuromorphic processing promises high energy efficiency and rapid response
rates, making it an ideal candidate for achieving autonomous flight of
resource-constrained robots. It will be especially beneficial for complex
neural networks as are involved in high-level visual perception. However, fully
neuromorphic solutions will also need to tackle low-level control tasks.
Remarkably, it is currently still challenging to replicate even basic low-level
controllers such as proportional-integral-derivative (PID) controllers.
Specifically, it is difficult to incorporate the integral and derivative parts.
To address this problem, we propose a neuromorphic controller that incorporates
proportional, integral, and derivative pathways during learning. Our approach
includes a novel input threshold adaptation mechanism for the integral pathway.
This Input-Weighted Threshold Adaptation (IWTA) introduces an additional weight
per synaptic connection, which is used to adapt the threshold of the
post-synaptic neuron. We tackle the derivative term by employing neurons with
different time constants. We first analyze the performance and limits of the
proposed mechanisms and then put our controller to the test by implementing it
on a microcontroller connected to the open-source tiny Crazyflie quadrotor,
replacing the innermost rate controller. We demonstrate the stability of our
bio-inspired algorithm with flights in the presence of disturbances. The
current work represents a substantial step towards controlling highly dynamic
systems with neuromorphic algorithms, thus advancing neuromorphic processing
and robotics. In addition, integration is an important part of any temporal
task, so the proposed Input-Weighted Threshold Adaptation (IWTA) mechanism may
have implications well beyond control tasks
An Adaptive Control Strategy for Neural Network based Optimal Quadcopter Controllers
Developing optimal controllers for aggressive high-speed quadcopter flight is
a major challenge in the field of robotics. Recent work has shown that neural
networks trained with supervised learning can achieve real-time optimal control
in some specific scenarios. In these methods, the networks (termed G&CNets) are
trained to learn the optimal state feedback from a dataset of optimal
trajectories. An important problem with these methods is the reality gap
encountered in the sim-to-real transfer. In this work, we trained G&CNets for
energy-optimal end-to-end control on the Bebop drone and identified the
unmodeled pitch moment as the main contributor to the reality gap. To mitigate
this, we propose an adaptive control strategy that works by learning from
optimal trajectories of a system affected by constant external pitch, roll and
yaw moments. In real test flights, this model mismatch is estimated onboard and
fed to the network to obtain the optimal rpm command. We demonstrate the
effectiveness of our method by performing energy-optimal hover-to-hover flights
with and without moment feedback. Finally, we compare the adaptive controller
to a state-of-the-art differential-flatness-based controller in a consecutive
waypoint flight and demonstrate the advantages of our method in terms of energy
optimality and robustness.Comment: 7 pages, 11 figure
End-to-end Reinforcement Learning for Time-Optimal Quadcopter Flight
Aggressive time-optimal control of quadcopters poses a significant challenge
in the field of robotics. The state-of-the-art approach leverages reinforcement
learning (RL) to train optimal neural policies. However, a critical hurdle is
the sim-to-real gap, often addressed by employing a robust inner loop
controller -an abstraction that, in theory, constrains the optimality of the
trained controller, necessitating margins to counter potential disturbances. In
contrast, our novel approach introduces high-speed quadcopter control using
end-to-end RL (E2E) that gives direct motor commands. To bridge the reality
gap, we incorporate a learned residual model and an adaptive method that can
compensate for modeling errors in thrust and moments. We compare our E2E
approach against a state-of-the-art network that commands thrust and body rates
to an INDI inner loop controller, both in simulated and real-world flight. E2E
showcases a significant 1.39-second advantage in simulation and a 0.17-second
edge in real-world testing, highlighting end-to-end reinforcement learning's
potential. The performance drop observed from simulation to reality shows
potential for further improvement, including refining strategies to address the
reality gap or exploring offline reinforcement learning with real flight data.Comment: 6 pages, 6 figures, 1 tabl
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