Flapping wing drones show off their skills

Control & Simulatio

Evolution of robust high speed optical-flow-based landing for autonomous MAVs

Automatic optimization of robotic behavior has been the long-standing goal of Evolutionary Robotics. Allowing the problem at hand to be solved by automation often leads to novel approaches and new insights. A common problem encountered with this approach is that when this optimization occurs in a simulated environment, the optimized policies are subject to the reality gap when implemented in the real world. This often results in sub-optimal behavior, if it works at all. This paper investigates the automatic optimization of neurocontrollers to perform quick but safe landing maneuvers for a quadrotor micro air vehicle using the divergence of the optical flow field of a downward looking camera. The optimized policies showed that a piece-wise linear control scheme is more effective than the simple linear scheme commonly used, something not yet considered by human designers. Additionally, we show the utility in using abstraction on the input and output of the controller as a tool to improve the robustness of the optimized policies to the reality gap by testing our policies optimized in simulation on real world vehicles. We tested the neurocontrollers using two different methods to generate and process the visual input, one using a conventional CMOS camera and one a dynamic vision sensor, both of which perform significantly differently than the simulated sensor. The use of the abstracted input resulted in near seamless transfer to the real world with the controllers showing high robustness to a clear reality gap.Control & Simulatio

Autonomous landing algorithm using a sun position predicting model for extended use of solar powered UAVs

In the field of robotics, a major challenge is extending the flight range of micro aerial vehicles. One way to extend the range is by charging batteries with solar arrays on the ground, while resting on intermediate landing positions. The solution we propose in this study differentiates itself from other solutions as it does not focus on improving UAV efficiency but rather on finding the most efficient landing position. In particular, an algorithm is developed to show the usefulness of the approach. This algorithm makes uses of the sonar sensor on board of the Parrot Bebop 1 drone in combination with an OptiTrack system to scan the environment for potential landing opportunities. After these measurements are discretized on a 2D grid, analysis is carried out with a sun position predicting model. Finally, a landing position is chosen within the scanned area and the drone will land accordingly. Little is known on whether a solar powered charge on the ground could be effective in a limited period of time. We present a coarse analysis, showing that the DelftaCopter with solar arrays on its wings charges its batteries in 1.3 days with relatively cheap solar cells in Africa or Australia. Future work includes the use of computer vision instead of sonar as well as the ensurance of a safe landing position using vision.Control & Simulatio

Optimization of swarm behavior assisted by an automatic local proof for a pattern formation task

In this work, we optimize the behavior of swarm agents in a pattern formation task. We start with a local behavior, expressed as a local state-action map, that has been formally proven to lead the swarm to always eventually form the desired pattern. We seek to optimize this for performance while keeping the formal proof. First, the state-action map is pruned to remove unnecessary state-action pairs, reducing the solution space. Then, the probabilities of executing the remaining actions are tuned with a genetic algorithm. The final controllers allow the swarm to form the patterns up to orders of magnitude faster than with the original behavior. The optimization is found to suffer from scalability issues. These may be tackled in future work by automatically minimizing the size of the local state-action map with a further direct focus on performance.Control & Simulatio

Abstraction as a Mechanism to Cross the Reality Gap in Evolutionary Robotics

One of the major challenges of Evolutionary Robotics is to transfer robot controllers evolved in simulation to robots in the real world. In this article, we investigate abstraction on the sensory inputs and motor actions as a potential solution to this problem. Abstraction means that the robot uses preprocessed sensory inputs and closed loop low-level controllers that execute higher level motor commands. We apply abstraction to the task of forming an asymmetric triangle with a homogeneous swarm of MAVs. The results show that the evolved behavior is effective both in simulation and reality, suggesting that abstraction can be a useful tool in making evolved behavior robust to the reality gap. Furthermore, we study the evolved solution, showing that it exploits the environment (in this case the identical behavior of the other robots) and creates behavioral attractors resulting in the creation of the required formation. Hence, the analysis suggests that by using abstraction, sensory-motor coordination is not necessarily lost but rather shifted to a higher level of abstraction.Control & Simulatio

Abstraction, Sensory-Motor Coordination, and the Reality Gap in Evolutionary Robotics

One of the major challenges of evolutionary robotics is to transfer robot controllers evolved in simulation to robots in the real world. In this article, we investigate abstraction of the sensory inputs and motor actions as a tool to tackle this problem. Abstraction in robots is simply the use of preprocessed sensory inputs and low-level closed-loop control systems that execute higher-level motor commands. To demonstrate the impact abstraction could have, we evolved two controllers with different levels of abstraction to solve a task of forming an asymmetric triangle with a homogeneous swarm of micro air vehicles. The results show that although both controllers can effectively complete the task in simulation, the controller with the lower level of abstraction is not effective on the real vehicle, due to the reality gap. The controller with the higher level of abstraction is, however, effective both in simulation and in reality, suggesting that abstraction can be a useful tool in making evolved behavior robust to the reality gap. Additionally, abstraction aided in reducing the computational complexity of the simulation environment, speeding up the optimization process. Preeminently, we show that the optimized behavior exploits the environment (in this case the identical behavior of the other robots) and performs input shaping to allow the vehicles to fly into and maintain the required formation, demonstrating clear sensory-motor coordination. This shows that the power of the genetic optimization to find complex correlations is not necessarily lost through abstraction as some have suggested.Control & Simulatio

Preface

Control & Simulatio

How Do Neural Networks See Depth in Single Images?

Deep neural networks have lead to a breakthrough in depth estimation from single images. Recent work shows that the quality of these estimations is rapidly increasing. It is clear that neural networks can see depth in single images. However, to the best of our knowledge, no work currently exists that analyzes what these networks have learned. In this work we take four previously published networks and investigate what depth cues they exploit. We find that all networks ignore the apparent size of known obstacles in favor of their vertical position in the image. The use of the vertical position requires the camera pose to be known; however, we find that these networks only partially recognize changes in camera pitch and roll angles. Small changes in camera pitch are shown to disturb the estimated distance towards obstacles. The use of the vertical image position allows the networks to estimate depth towards arbitrary obstacles - even those not appearing in the training set - but may depend on features that are not universally present.Control & Simulatio

A Computationally Efficient Moving Horizon Estimator for Ultra-Wideband Localization on Small Quadrotors

We present a computationally efficient moving horizon estimator that allows for real-time localization using Ultra-Wideband measurements on small quadrotors. The estimator uses only a single iteration of a simple gradient descent method to optimize the state estimate based on past measurements, while using random sample consensus to reject outliers. We compare our algorithm to a state-of-the-art Extended Kalman Filter and show its advantages when dealing with heavy-tailed noise, which is frequently encountered in Ultra-Wideband ranging. Furthermore, we analyze the algorithm's performance when reducing the number of beacons for measurements and we implement the code on a 30 g Crazyflie drone, to show its ability to run on computationally limited devices.Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.Control & Simulatio

Design and implementation of a parsimonious neuromorphic PID for onboard altitude control for MAVs using neuromorphic processors

The great promises of neuromorphic sensing and processing for robotics have led researchers and engineers to investigate novel models for robust and reliable control of autonomous robots (navigation, obstacle detection and avoidance, etc.), especially for quadrotors in challenging contexts such as drone racing and aggressive maneuvers. Using spiking neural networks, these models can be run on neuromorphic hardware to benefit from outstanding update rates and high energy efficiency. Yet, low-level controllers are often neglected and remain outside of the neuromorphic loop. Designing low-level neuromorphic controllers is crucial to remove the standard PID, and therefore benefit from all the advantages of closing the neuromorphic loop. In this paper, we propose a parsimonious and adjustable neuromorphic PID controller, endowed with a minimal number of 93 neurons sparsely connected to achieve autonomous, onboard altitude control of a quadrotor equipped with Intel's Loihi neuromorphic chip. We successfully demonstrate the robustness of our proposed network in a set of experiments where the quadrotor is requested to reach a target altitude from take-off. Our results confirm the suitability of such low-level neuromorphic controllers, ultimately with a very high update frequency. Control & Simulatio