Being motivated by ceiling inspection applications via unmanned aerial vehicles (UAVs) which require close proximity flight to surfaces, a systematic control approach enabling safe and accurate close proximity flight is proposed in this work. There are two main challenges for close proximity flights: (i) the trust characteristics varies drastically for the different distance from the ceiling which results in a complex nonlinear dynamics; (ii) the system needs to consider physical and environmental constraints to safely fly in close proximity. To address these challenges, a novel framework consisting of a constrained optimization-based force estimation and an optimization-based nonlinear controller is proposed. Experimental results illustrate that the performance of the proposed control approach can stabilize UAV down to 1 cm distance to the ceiling. Furthermore, we report that the UAV consumes up to 12.5 less power when it is operated 1 cm distance to ceiling, which is promising potential for more battery-efficient inspection flights.
The available approaches can handle the control of the flying robot when it does not engage with an interaction. However, the challenges associated with the aerodynamic interaction require the system to be more responsive, adaptive and resilient @cite_12 @cite_29 @cite_31 @cite_21 . This operation also brings system and environment based constraints including the level of the interaction. The available approaches that consider the constraints leverage individual multi-models for generic interaction problems which bring additional complexity @cite_5 . Moreover, nominal optimization-based approaches are considered in the UAV control for the interaction tasks, wherein the system lacks the ability to take external forces, changing parameters and unmodeled dynamics into account @cite_17 @cite_19 @cite_25 .
@cite_29 @cite_21 @cite_19 @cite_5 @cite_31 @cite_25 @cite_12 @cite_17
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The challenge of aerial robotic contact-based inspection is the driving motivation of this paper. The problem is approached on both levels of control and path-planning by introducing algorithms and control laws that ensure optimal inspection through contact and controlled aerial robotic physical interaction. Regarding the flight and physical interaction stabilization, a hybrid model predictive control framework is proposed, based on which a typical quadrotor becomes capable of stable and active interaction, accurate trajectory tracking on environmental surfaces as well as force control. Convex optimization techniques enabled the explicit computation of such a controller which accounts for the dynamics in free-flight as well as during physical interaction, ensures the global stability of the hybrid system and provides optimal responses while respecting the physical limitations of the vehicle. Further augmentation of this scheme, allowed the incorporation of a last-resort obstacle avoidance mechanism at the control level. Relying on such a control law, a contact-based inspection planner was developed which computes the optimal route within a given set of inspection points while avoiding any obstacles or other no-fly zones on the environmental surface. Extensive experimental studies that included complex "aerial-writing" tasks, interaction with non-planar and textured surfaces, execution of multiple inspection operations and obstacle avoidance maneuvers, indicate the efficiency of the proposed methods and the potential capabilities of aerial robotic inspection through contact. In this paper, we consider the problem of multirotor flying robots physically interacting with the environment under wind influence. The result are the first algorithms for simultaneous online estimation of contact and aerodynamic wrenches acting on the robot based on real-world data, without the need for dedicated sensors. For this purpose, we investigate two model-based techniques for discriminating between aerodynamic and interaction forces. The first technique is based on aerodynamic and contact torque models, and uses the external force to estimate wind speed. Contacts are then detected based on the residual between estimated external torque and expected (modeled) aerodynamic torque. Upon detecting contact, wind speed is assumed to change very slowly. From the estimated interaction wrench, we are also able to determine the contact location. This is embedded into a particle filter framework to further improve contact location estimation. The second algorithm uses the propeller aerodynamic power and angular speed as measured by the speed controllers to obtain an estimate of the airspeed. An aerodynamics model is then used to determine the aerodynamic wrench. Both methods rely on accurate aerodynamics models. Therefore, we evaluate data-driven and physics based models as well as offline system identification for flying robots. For obtaining ground truth data we performed autonomous flights in a 3D wind tunnel. Using this data, aerodynamic model selection, parameter identification, and discrimination between aerodynamic and contact forces could be done. Finally, the developed methods could serve as useful estimators for interaction control schemes with simultaneous compensation of wind disturbances. Robotic systems need to be able to plan control actions that are robust to the inherent uncertainty in the real world. This uncertainty arises due to uncertain state estimation, disturbances, and modeling errors, as well as stochastic mode transitions such as component failures. Chance-constrained control takes into account uncertainty to ensure that the probability of failure, due to collision with obstacles, for example, is below a given threshold. In this paper, we present a novel method for chance-constrained predictive stochastic control of dynamic systems. The method approximates the distribution of the system state using a finite number of particles. By expressing these particles in terms of the control variables, we are able to approximate the original stochastic control problem as a deterministic one; furthermore, the approximation becomes exact as the number of particles tends to infinity. This method applies to arbitrary noise distributions, and for systems with linear or jump Markov linear dynamics, we show that the approximate problem can be solved using efficient mixed-integer linear-programming techniques. We also introduce an important weighting extension that enables the method to deal with low-probability mode transitions such as failures. We demonstrate in simulation that the new method is able to control an aircraft in turbulence and can control a ground vehicle while being robust to brake failures. Safe autonomous navigation of microair vehicles in cluttered dynamic environments is challenging due to the uncertainties arising from robot localization, sensing, and motion disturbances. This letter presents a probabilistic collision avoidance method for navigation among other robots and moving obstacles, such as humans. The approach explicitly considers the collision probability between each robot and obstacle and formulates a chance constrained nonlinear model predictive control problem (CCNMPC). A tight bound for approximation of collision probability is developed, which makes the CCNMPC formulation tractable and solvable in real time. For multirobot coordination, we describe three approaches, one distributed without communication (constant velocity assumption), one distributed with communication (of previous plans), and one centralized (sequential planning). We evaluate the proposed method in experiments with two quadrotors sharing the space with two humans and verify the multirobot coordination strategy in simulation with up to sixteen quadrotors.