arXiv subjects

Guido C. H. E. de Croon

Publications and source records attributed to Guido C. H. E. de Croon.

41 records · Page 3Linked to original sources

arXiv2018

Provable Emergent Pattern Formation by a Swarm of Anonymous, Homogeneous, Non-Communicating, Reactive Robots with Limited Relative Sensing and no Global Knowledge or Positioning

In this work, we explore emergent behaviors by swarms of anonymous, homogeneous, non-communicating, reactive robots that do not know their global position and have limited relative sensing. We introduce a novel method that enables such severely limited robots to autonomously arrange in a desired pattern and maintain it. The method includes an automatic proof procedure to check whether a given pattern will be achieved by the swarm from any initial configuration. An attractive feature of this proof procedure is that it is local in nature, avoiding as much as possible the computational explosion that can be expected with increasing robots, states, and action possibilities. Our approach is based on extracting the local states that constitute a global goal (in this case, a pattern). We then formally show that these local states can only coexist when the global desired pattern is achieved and that, until this occurs, there is always a sequence of actions that will lead from the current pattern to the desired pattern. Furthermore, we show that the agents will never perform actions that could a) lead to intra-swarm collisions or b) cause the swarm to separate. After an analysis of the performance of pattern formation in the discrete domain, we also test the system in continuous time and space simulations and reproduce the results using asynchronous agents operating in unbounded space. The agents successfully form the desired patterns while avoiding collisions and separation.

cs.RO↗

arXiv2017

Vertical Landing for Micro Air Vehicles using Event-Based Optical Flow

Small flying robots can perform landing maneuvers using bio-inspired optical flow by maintaining a constant divergence. However, optical flow is typically estimated from frame sequences recorded by standard miniature cameras. This requires processing full images on-board, limiting the update rate of divergence measurements, and thus the speed of the control loop and the robot. Event-based cameras overcome these limitations by only measuring pixel-level brightness changes at microsecond temporal accuracy, hence providing an efficient mechanism for optical flow estimation. This paper presents, to the best of our knowledge, the first work integrating event-based optical flow estimation into the control loop of a flying robot. We extend an existing 'local plane fitting' algorithm to obtain an improved and more computationally efficient optical flow estimation method, valid for a wide range of optical flow velocities. This method is validated for real event sequences. In addition, a method for estimating the divergence from event-based optical flow is introduced, which accounts for the aperture problem. The developed algorithms are implemented in a constant divergence landing controller on-board of a quadrotor. Experiments show that, using event-based optical flow, accurate divergence estimates can be obtained over a wide range of speeds. This enables the quadrotor to perform very fast landing maneuvers.

cs.CV↗

arXiv2017

On-board Communication-based Relative Localization for Collision Avoidance in Micro Air Vehicle teams

Micro Air Vehicles (MAVs) will unlock their true potential once they can operate in groups. To this end, it is essential for them to estimate on-board the relative location of their neighbors. The challenge lies in limiting the mass and processing burden needed to enable this. We developed a relative localization method that only requires the MAVs to communicate via their wireless transceiver. Communication allows the exchange of on-board states (velocity, height, and orientation), while the signal-strength provides range data. These quantities are fused to provide a full relative location estimate. We used our method to tackle the problem of collision avoidance in tight areas. The system was tested with a team of AR.Drones flying in a 4mx4m area and with miniature drones of ~50g in a 2mx2m area. The MAVs were able to track their relative positions and fly several minutes without collisions. Our implementation used Bluetooth to communicate between the drones. This featured significant noise and disturbances in signal-strength, which worsened as more drones were added. Simulation analysis suggests that results can improve with a more suitable transceiver module.

cs.RO↗

arXiv2016

First free-flight flow visualisation of a flapping-wing robot

Flow visualisations are essential to better understand the unsteady aerodynamics of flapping wing flight. The issues inherent to animal experiments, such as poor controllability and unnatural flapping when tethered, can be avoided by using robotic flyers. Such an approach holds a promise for a more systematic and repeatable methodology for flow visualisation, through a better controlled flight. Such experiments require high precision position control, however, and until now this was not possible due to the challenging flight dynamics and payload restrictions of flapping wing Micro Air Vehicles (FWMAV). Here, we present a new FWMAV-specific control approach that, by employing an external motion tracking system, achieved autonomous wind tunnel flight with a maximum root-mean-square position error of 28 mm at low speeds (0.8 - 1.2 m/s) and 75 mm at high speeds (2 - 2.4 m/s). This allowed the first free-flight flow visualisation experiments to be conducted with an FWMAV. Time-resolved stereoscopic Particle Image Velocimetry (PIV) was used to reconstruct the 3D flow patterns of the FWMAV wake. A good qualitative match was found in comparison to a tethered configuration at similar conditions, suggesting that the obtained free-flight measurements are reliable and meaningful.

physics.bio-ph↗

arXiv2015

Behaviour Trees for Evolutionary Robotics

Evolutionary Robotics allows robots with limited sensors and processing to tackle complex tasks by means of sensory-motor coordination. In this paper we show the first application of the Behaviour Tree framework to a real robotic platform using the Evolutionary Robotics methodology. This framework is used to improve the intelligibility of the emergent robotic behaviour as compared to the traditional Neural Network formulation. As a result, the behaviour is easier to comprehend and manually adapt when crossing the reality gap from simulation to reality. This functionality is shown by performing real-world flight tests with the 20-gram DelFly Explorer flapping wing Micro Air Vehicle equipped with a 4-gram onboard stereo vision system. The experiments show that the DelFly can fully autonomously search for and fly through a window with only its onboard sensors and processing. The success rate of the optimised behaviour in simulation is 88% and the corresponding real-world performance is 54% after user adaptation. Although this leaves room for improvement, it is higher than the 46% success rate from a tuned user-defined controller.

cs.RO↗