We present a closed-loop control strategy for a delta-wing unmanned aerial aquatic-vehicle (UAAV) that enables autonomous swim, fly, and water-to-air transition. Our control system consists of a hybrid state estimator and a closed-loop feedback policy which is capable of trajectory following through the water, air and transition domains. To test our estimator and control approach in hardware, we instrument the vehicle with a minimalistic set of commercial off-the-shelf sensors. Finally, we demonstrate a successful autonomous water-to-air transition with our prototype UAAV system and discuss the implications of these results with regards to robustness.
Few of the approaches mentioned above consider the closed-loop control or estimation strategies necessary for enabling multi-domain locomotion. In @cite_16 and @cite_1 the authors present modeling, simulation and control strategies for a multi-domain quadcopter. Their approach focuses on applying robust control techniques to develop a globally stable switching attitude controller which relies on two different linear models. In @cite_7 , the authors explore hybrid control for the quadrotor UAAV experimentally.
@cite_16 @cite_1 @cite_7
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Abstract Modeling and control of a multi-medium unmanned vehicle capable of seamless operation in air or underwater is introduced in this paper. The multi-medium system is treated as a hybrid system with continuous dynamics while performing in both air and underwater, and discrete jumps in the medium density during the transitions. The continuous dynamics are modeled by the Newton–Euler formalism, taking into account the effects of the buoyancy and drag phenomena, normally neglected in aerial vehicles. A hybrid controller is designed for trajectory tracking considering the full system, including a transition strategy to assure the switching between mediums. Stability analysis for the full system is provided using hybrid Lyapunov and invariance principles. The performance of the control strategy is validated through simulations. Finally, an experimental platform consisting of a multirotor in an octo-quadcopter configuration was developed and some preliminary experimental results are introduced, showing the vehicle performing in air, underwater and through the transition. Aiming to real easy application in several quadruped robot platforms, this paper introduces a new method of modeling central pattern generators (CPG) to control quadruped locomotion. Not only can this new model generate all the primary gaits of quadrupeds stably with limit cycle effect, but it also has the ability of tuning the periodic outputs with arbitrary waveforms. The core idea is to combine strong points of two mathematical tools: Fourier series and Recurrent neural networks. In addition, a new biomimetic controller is also introduced using the proposed CPG model and several reflex modules. Finally, dynamic simulations are performed to validate the efficiency of the proposed controller. Potential-field-based control strategy for path planning and formation control of multi Quadrotor systems is proposed in this work. The potential field is used to attract the Quadrotor to the goal location as well as avoiding the obstacle. The algorithm to solve the so called local minima problem by utilizing the wall-following behavior is also proposed. The resulted path planning via potential function strategy is then used to design formation control algorithm. Using the virtual leader approach, the formation control strategy by means of potential function is formulated. Each Quadrotor is assigned attractive potential to reach its desired position in formation. Each Quadrotor is also treated as moving obstacle which induces repulsive potential to the others in order to prevent inter-robot collision. The desired position for each Quadrotor in certain configuration is calculated based on the virtual leaderâ��s current position. To get smooth movement, the virtual leader is set as unicycle robot. The repulsive potential is also attached to each agent in case it moves in the region inside the obstacleâ��s influence. The overall strategy has been successfully applied to the Quadrotorâ��s model of Parrot AR Drone 2.0 in Gazebo simulator programmed using Robot Operating System. This work addresses the problem of motion planning among obstacles for quadrotor platforms under external disturbances and with model uncertainty. A novel Nonlinear Model Predictive Control (NMPC) optimization technique is proposed which incorporates specified uncertainties into the planned trajectories. At the core of the procedure lies the propagation of model parameter uncertainty and initial state uncertainty as high-confidence ellipsoids in pose space. The quadrotor trajectories are then computed to avoid obstacles by a required safety margin, expressed as ellipsoid penetration while minimizing control effort and achieving a user-specified goal location. Combining this technique with online model identification results in robust obstacle avoidance behavior. Experiments in outdoor scenarios with virtual obstacles show that the quadrotor can avoid obstacles robustly, even under the influence of external disturbances.