Aerial manipulation aims at combining the versatility and the agility of some aerial platforms with the manipulation capabilities of robotic arms. This letter tries to collect the results reached by the research community so far within the field of aerial manipulation, especially from the technological and control point of view. A brief literature review of general aerial robotics and space manipulation is carried out as well.

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Aerial Manipulation: A Literature Review

The use of aerial swarms to solve real-world problems has been increasing steadily, accompanied by falling prices and improving performance of communication, sensing, and processing hardware. The commoditization of hardware has reduced unit costs, thereby lowering the barriers to entry to the field of aerial swarm robotics. A key enabling technology for swarms is the family of algorithms that allow the individual members of the swarm to communicate and allocate tasks amongst themselves, plan their trajectories, and coordinate their flight in such a way that the overall objectives of the swarm are achieved efficiently. These algorithms, often organized in a hierarchical fashion, endow the swarm with autonomy at every level, and the role of a human operator can be reduced, in principle, to interactions at a higher level without direct intervention. This technology depends on the clever and innovative application of theoretical tools from control and estimation. This paper reviews the state of the art of these theoretical tools, specifically focusing on how they have been developed for, and applied to, aerial swarms. Aerial swarms differ from swarms of ground-based vehicles in two respects: they operate in a three-dimensional space and the dynamics of individual vehicles adds an extra layer of complexity. We review dynamic modeling and conditions for stability and controllability that are essential in order to achieve cooperative flight and distributed sensing. The main sections of this paper focus on major results covering trajectory generation, task allocation, adversarial control, distributed sensing, monitoring, and mapping. Wherever possible, we indicate how the physics and subsystem technologies of aerial robots are brought to bear on these individual areas.

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A Survey on Aerial Swarm Robotics

In this paper, we investigate fuzzy neural network (FNN) control using impedance learning for coordinated multiple constrained robots carrying a common object in the presence of the unknown robotic dynamics and the unknown environment with which the robot comes into contact. First, an FNN learning algorithm is developed to identify the unknown plant model. Second, impedance learning is introduced to regulate the control input in order to improve the environment–robot interaction, and the robot can track the desired trajectory generated by impedance learning. Third, in light of the condition requiring the robot to move in a finite space or to move at a limited velocity in a finite space, the algorithm based on the position constraint and the velocity constraint are proposed, respectively. To guarantee the position constraint and the velocity constraint, an integral barrier Lyapunov function is introduced to avoid the violation of the constraint. According to Lyapunov’s stability theory, it can be proved that the tracking errors are uniformly bounded ultimately. At last, some simulation examples are carried out to verify the effectiveness of the designed control.

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Adaptive Fuzzy Control for Coordinated Multiple Robots With Constraint Using Impedance Learning

This article summarizes new aerial robotic manipulation technologies and methods-aerial robotic manipulators with dual arms and multidirectional thrusters-developed in the AEROARMS project for outdoor industrial inspection and maintenance (IaM).

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The AEROARMS Project: Aerial Robots with Advanced Manipulation Capabilities for Inspection and Maintenance

Nowadays, Unmanned Aerial Vehicles (UAVs) are used in many different applications. Using systems of multiple UAVs is the next obvious step in the process of applying this technology for variety of ...

https://www.worldscientific.com/doi/pdf/10.1142/S2301385020500090

Multiple UAV Systems: A Survey

This technical paper summarizes the recent experimental results concerning the challenging problem of landing and takeoff on/from a sloped surface with an aerial vehicle exploiting the force provided by an anchored taut tether. A special regard is given to the practical aspects concerning the experimental part. In this manuscript we show extreme landing and takeoff maneuvers on slopes with at least 50deg inclination and non flat surfaces, such as, e.g., on industrial pipes.

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Landing and Take-off on/from Sloped and Non-planar Surfaces with more than 50 Degrees of Inclination

As the performance of autonomous systems increases, safety concerns arise, especially when operating in non-structured environments. To deal with these concerns, this work presents a safety layer for mechanical systems that detects and responds to unstable dynamics caused by external disturbances. The safety layer is implemented independently and on top of already present nominal controllers, like pose or wrench tracking, and limits power flow when the system’s response would lead to instability. This approach is based on the computation of the Largest Lyapunov Exponent (LLE) of the system’s error dynamics, which represent a measure of the dynamics’ divergence or convergence rate. By actively computing this metric, divergent and possibly dangerous system behaviors can be promptly detected. The LLE is then used in combination with Control Barrier Functions (CBFs) to impose power limit constraints on a jerk controlled system. The proposed architecture is experimentally validated on an Omnidirectional Micro Aerial Vehicle (OMAV) both in free flight and interaction tasks.

Power-Based Safety Layer for Aerial Vehicles in Physical Interaction Using Lyapunov Exponents

Bilateral teleoperation offers an intriguing solution towards shared autonomy with aerial vehicles in contact-based inspection and manipulation tasks. Omnidirectional aerial robots allow for full pose operations, making them particularly attractive in such tasks. Naturally, the question arises whether standard bilateral teleoperation methodologies are suitable for use with these vehicles. In this work, a fully decoupled 6DoF bilateral teleoperation framework for aerial physical interaction is designed and tested for the first time. The method is based on the well established rate control, recentering and interaction force feedback policy. However, practical experiments evince the difficulty of performing de-coupled motions in a single axis only. As such, this work shows that the trivial extension of standard methods is insufficient for omnidirectional teleoperation, due to the operator's physical inability to properly decouple all input DoFs. This suggests that further studies on enhanced haptic feedback are necessary.

Towards 6DoF Bilateral Teleoperation of an Omnidirectional Aerial Vehicle for Aerial Physical Interaction

This document is a technical attachment to the paper: M. Tognon and A. Franchi, “Nonlinear observer-based tracking control of link stress and elevation for a tethered aerial robot using inertial-only measurements,” in 2015 IEEE Int. Conf. on Robotics and Automation, Seattle, WA, 2015; as an extension of the simulation’s section.

https://hal.archives-ouvertes.fr/hal-01118868/document

Extended Simulations for the Link Stress and Elevation Control of a Tethered Aerial Robot

The recent development of novel aerial vehicles capable of physically interacting with the environment leads to new applications such as contact-based inspection. These tasks require the robotic system to exchange forces with partially-known environments, which may contain uncertainties including unknown spatially-varying friction properties and discontinuous variations of the surface geometry. Finding a solution that senses, adapts, and remains robust against these environmental uncertainties remains an open challenge. This letter presents a learning-based adaptive control strategy for aerial sliding tasks. In particular, the gains of a standard impedance controller are adjusted in real-time by a neural network policy based on proprioceptive and tactile sensing. This policy is trained in simulation with simplified actuator dynamics in a student-teacher learning setup. The real-world performance of the proposed approach is verified using a tilt-arm omnidirectional flying vehicle. The proposed controller structure combines data-driven and model-based control methods, enabling our approach to successfully transfer directly and without adaptation from simulation to the real platform. We attribute the success of the sim-to-real transfer to the inclusion of feedback control in the training and deployment. We achieved tracking performance and disturbance rejection that cannot be achieved using fine-tuned state of the art interaction control method.

Marco Tognon

Papers

Landing and Take-off on/from Sloped and Non-planar Surfaces with more than 50 Degrees of Inclination

Power-Based Safety Layer for Aerial Vehicles in Physical Interaction Using Lyapunov Exponents

Towards 6DoF Bilateral Teleoperation of an Omnidirectional Aerial Vehicle for Aerial Physical Interaction

Extended Simulations for the Link Stress and Elevation Control of a Tethered Aerial Robot

Learning Variable Impedance Control for Aerial Sliding on Uneven Heterogeneous Surfaces by Proprioceptive and Tactile Sensing