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.

/pdf/aerial-manipulation-a-literature-review-2gq2uh3t8a.pdf

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.

/pdf/a-survey-on-aerial-swarm-robotics-htbvw7nqj5.pdf

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.

/pdf/adaptive-fuzzy-control-for-coordinated-multiple-robots-with-30ta2skai1.pdf

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).

/pdf/the-aeroarms-project-aerial-robots-with-advanced-46d7z50yuh.pdf

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 document is a technical attachment to [1] as an extension of the simulation's section.

https://hal.archives-ouvertes.fr/hal-01261251/document

Extended Simulations for the Observer-based Control of Position and Tension for an Aerial Robot Tethered to a Moving Platform

In this chapter we shall present the results obtained by the experimental and numerical campaign, apt to validate the proposed control and estimation methods presented in Chap 4 In particular, we recall that we designed:


two hierarchical controllers for the outputs \(\mathbf {y}^a\), \({\mathbf {y}^{b}}\);


three dynamic feedback linearizing controllers for the output \(\mathbf {y}^a\), \({\mathbf {y}^{b}}\), and \(\mathbf {y}^c\);


a nonlinear observer based on IMU and three encoders readings;


a nonlinear observer based on the IMU readings only, valid for the reduced model

Simulation and Experimental Results

This document is a technical attachment to ”Dynamic Decentralized Control for Protocentric Aerial Manipulators” for explicit 
computations of the nominal states and the inputs of a Pro- 
tocentric Aerial Manipulator (PAM) in 2D, using differential 
flatness property. In ”Dynamic Decentralized Control for Protocentric Aerial Manipulators” these values are used to control a 
PAM in 3D. Furthermore, considering the aerial manipulator 
design used for the experiments in that paper, here we inves- 
tigate the case when the system is non-protocentric; i.e., the 
manipulating arm is not exactly attached to the CoM of the 
flying robot, P0 . We show the effect of the distance between 
this attachment point and P0 on the performance tracking a 
composite trajectory. Finally some additional plots related to 
the experimental results are provided.

Explicit Computations, Simulations and additional Results for the Dynamic Decentralized Control for Protocentric Aerial Manipulators Technical Attachment to: ”Dynamic Decentralized Control for Protocentric Aerial Manipulators” 2017 IEEE International Conference on Robotics and Automation (ICRA), Singapore, May 2017

In this article, we investigate and deploy sampling-based control techniques for the challenging task of the mobile manipulation of articulated objects. By their nature, manipulation tasks necessitate environment interactions, which require the handling of nondifferentiable switching contact dynamics. These dynamics represent a strong limitation for traditional gradient-based optimization methods, such as model-predictive control and differential dynamic programming, which often rely on heuristics for trajectory generation. Sampling-based techniques alleviate these constraints but do not ensure robots' stability and input/state constraints either. On the other hand, real-world applications in human environments require safety and robustness to unexpected events. For this reason, we propose a novel framework for safe robotic manipulation of movable articulated objects. The framework combines sampling-based control together with control barrier functions and passivity theory that, thanks to formal stability guarantees, enhance the safety and robustness of the method. We also provide the practical insights that enable robust deployment of stochastic control using a conventional central processing unit. We deploy the algorithm on a ten-degree-of-freedom mobile manipulator robot. Finally, we open source our generic and multithreaded implementation.

Robust Sampling-Based Control of Mobile Manipulators for Interaction With Articulated Objects

This work studies how parametric uncertainties affect the cooperative manipulation of a cable-suspended beam-shaped load by means of two aerial robots not explicitly communicating with each other. In particular, the work sheds light on the impact of the uncertain knowledge of the model parameters available to an established communication-less force-based controller. First, we find the closed-loop equilibrium configurations in the presence of the aforementioned uncertainties, and then we study their stability. Hence, we show the fundamental role played in the robustness of the load attitude control by the internal force induced in the manipulated object by non-vertical cables. Furthermore, we formally study the sensitivity of the attitude error to such parametric variations, and we provide a method to act on the load position error in the presence of the uncertainties. Eventually, we validate the results through an extensive set of numerical tests in a realistic simulation environment including underactuated aerial vehicles and sagging-prone cables, and through hardware experiments.

Marco Tognon

Papers

Extended Simulations for the Observer-based Control of Position and Tension for an Aerial Robot Tethered to a Moving Platform

Simulation and Experimental Results

Robust Sampling-Based Control of Mobile Manipulators for Interaction With Articulated Objects

Equilibria, Stability, and Sensitivity for the Aerial Suspended Beam Robotic System subject to Parameter Uncertainty