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The significant advances in last decade in the research and technology of multi-rotor design, modeling and control, supported by the increasing variety of commercially available platforms, components and manufacturers, have facilitated a rise in the novel applications of aerial robots, capable of not only perceiving, but also interacting with the environment, allowing the realization of diverse operations and tasks in areas and workspaces that are difficult to access by human operators or ground vehicles. The paper illustrates, in laboratory conditions, the deployment of the inspection sensor in a metallic structure subject to vibrations using the aerial robot, including the data acquisition, analysis and interpretation of results. Different sensor technologies for Structural Health Monitoring (SHM) are identified, presenting the design of a sensor unit based on an accelerometer with a docking mechanism that can be attached to metallic structures using the aerial platform. [Extracted from the article]

Featured Application: The main idea of this paper was to deploy a team of unmanned aerial vehicles (UAVs) to attach a sensor to a bridge using a two-component adhesive in order to perform an inspection. Constant pressure must be applied for several minutes to form a bond between two adhesives. Therefore, one UAV sprays the colored component of an adhesive while the aerial manipulator transports the sensor, detects the contact point and attaches the sensor to it. A trajectory planning algorithm was developed around the dynamic model of the UAV and the manipulator attached to it, ensuring that the end-effector is parallel to the wall normal. Finally, the aerial manipulator achieves and maintains contact with a predefined force through an adaptive impedance control approach. Periodic bridge inspections are required every several years to determine the state of a bridge. Most commonly, the inspection is performed using specialized trucks allowing human inspectors to review the conditions underneath the bridge, which requires a road closure. The aim of this paper was to use aerial manipulators to mount sensors on the bridge to collect the necessary data, thus eliminating the need for the road closure. To do so, a two-step approach is proposed: an unmanned aerial vehicle (UAV) equipped with a pressurized canister sprays the first glue component onto the target area; afterward, the aerial manipulator detects the precise location of the sprayed area, and mounts the required sensor coated with the second glue component. The visual detection is based on an Red Green Blue - Depth (RGB-D) sensor and provides the target position and orientation. A trajectory is then planned based on the detected contact point, and it is executed through the adaptive impedance control capable of achieving and maintaining a desired force reference. Such an approach allows for the two glue components to form a solid bond. The described pipeline is validated in a simulation environment while the visual detection is tested in an experimental environment. [ABSTRACT FROM AUTHOR]

The power grid is an essential infrastructure in any country, comprising thousands of kilometers of power lines that require periodic inspection and maintenance, carried out nowadays by human operators in risky conditions. To increase safety and reduce time and cost with respect to conventional solutions involving manned helicopters and heavy vehicles, the AERIAL-CORE project proposes the development of aerial robots capable of performing aerial manipulation operations to assist human operators in power lines inspection and maintenance, allowing the installation of devices, such as bird flight diverters or electrical spacers, and the fast delivery and retrieval of tools. This manuscript describes the goals and functionalities to be developed for safe local aerial manipulation, presenting the preliminary designs and experimental results obtained in the first year of the project. [ABSTRACT FROM AUTHOR]

This paper presents the lane-merging strategy for self-driving cars in dense traffic using the Stackelberg game approach. From the perspective of the self-driving car, in order to make sufficient space to merge into the next lane, a self-driving car should interact with the vehicles in the next lane. In heavy traffic, where the possible actions of the vehicle are pretty limited, it is possible to conjecture the driving intentions of the vehicles from their behaviors. For example, by observing the speed changes of the human-driver in the next lane, the self-driving car can estimate its driving intention in real time, much in the same way of a human driver. We use the principle of Stackelberg competition to make the optimal decision for the self-driving car based on the predicted reaction of the interacting vehicles in the next lane. In this way, according to the traffic circumstances, a self-driving car can decide whether to merge or not. In addition, by limiting the number of interacting vehicles, the computational burden is manageable enough to be implemented in production vehicles. We verify the efficiency of the proposed method through the case studies for different test scenarios, and the test results show that our approach is closer to the human-like decision-making strategy, as compared to the conventional rule-based method. [ABSTRACT FROM AUTHOR]