Gust disturbance alleviation with Incremental Nonlinear Dynamic Inversion

Micro Aerial Vehicles (MAVs) are limited in their operation outdoors near obstacles by their ability to withstand wind gusts. Currently widespread position control methods such as Proportional Integral Derivative control do not perform well under the influence of gusts. Incremental Nonlinear Dynamic Inversion (INDI) is a sensor-based control technique that can control nonlinear systems subject to disturbances. This method was developed for the attitude control of MAVs, but in this paper we generalize this method to the outer loop control of MAVs under gust loads. Significant improvements over a traditional Proportional Integral Derivative (PID) controller are demonstrated in an experiment where the drone flies in and out of a fan's wake. The control method does not rely on frequent position updates, so it is ready to be applied outside with standard GPS modules

Towards Contextual Action Recognition and Target Localization with Active Allocation of Attention

Exploratory gaze movements are fundamental for gathering the most relevant information regarding the partner during social interactions. We have designed and implemented a system for dynamic attention allocation which is able to actively control gaze movements during a visual action recognition task. During the observation of a partners reaching movement, the robot is able to contextually estimate the goal position of the partner hand and the location in space of the candidate targets, while moving its gaze around with the purpose of optimizing the gathering of information relevant for the task. Experimental results on a simulated environment show that active gaze control provides a relevant advantage with respect to typical passive observation, both in term of estimation precision and of time required for action recognition. © 2012 Springer-Verlag

Monocular distance estimation with optical flow maneuvers and efference copies: a stability based strategy

The visual cue of optical flow plays an important role in the navigation of flying insects, and is increasingly studied for use by small flying robots as well. A major problem is that successful optical flow control seems to require distance estimates, while optical flow is known to provide only the ratio of velocity to distance. In this article, a novel, stability-based strategy is proposed for monocular distance estimation, relying on optical flow maneuvers and knowledge of the control inputs (efference copies). It is shown analytically that given a fixed control gain, the stability of a constant divergence control loop only depends on the distance to the approached surface. At close distances, the control loop starts to exhibit self-induced oscillations. The robot can detect these oscillations and hence be aware of the distance to the surface. The proposed stability-based strategy for estimating distances has two main attractive characteristics. First, self-induced oscillations can be detected robustly by the robot and are hardly influenced by wind. Second, the distance can be estimated during a zero divergence maneuver, i.e., around hover. The stability-based strategy is implemented and tested both in simulation and on board a Parrot AR drone 2.0. It is shown that the strategy can be used to: (1) trigger a final approach response during a constant divergence landing with fixed gain, (2) estimate the distance in hover, and (3) estimate distances during an entire landing if the robot uses adaptive gain control to continuously stay on the 'edge of oscillation.

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

An intermediate form of behavioral control in 'reactive' robots

Contains fulltext : 90689.pdf (publisher's version ) (Open Access

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