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This paper aims to present a fully actuated aerial manipulator (AM) with a robust motion/force hybrid controller for conducting contact-typed inspection tasks in industrial plants.

An AM is designed based on a hexarotor with tilted rotors and a rigidly attached end effector. By tilting the rotors, the position and attitude of the AM can be controlled independently, and the AM can actively exert forces on industrial facilities through the rigidly attached end effector. A motion/force hybrid controller is proposed to perform contact-typed inspection tasks. The contact-typed inspection task is divided into the approach phase and the contact phase. In the approach phase, the AM automatically approaches the contact surface. In the contact phase, a motion/force hybrid controller is used for contact-typed inspection. Finally, a disturbance observer (DOB) is used to estimate external disturbances and used as feedforward compensation.

The proposed AM can slowly approach the contact surface without significant impact in the contact phase. It can realize constant force control in the direction normal to the contact surface in the contact phase, whereas the motion of the remaining directions can be controlled by the operator. The use of the DOB ensures the robustness of the AM in the presence of external wind disturbances.

A fully actuated AM system with a robust motion/force hybrid controller is proposed. The effectiveness of the proposed AM system for conducting contact-typed industrial inspection tasks is validated by practical experiments.

The prerequisite for most traditional visual simultaneous localization and mapping (V-SLAM) algorithms is that most objects in the environment should be static or in low-speed locomotion. These algorithms rely on geometric information of the environment and restrict the application scenarios with dynamic objects. Semantic segmentation can be used to extract deep features from images to identify dynamic objects in the real world. Therefore, V-SLAM fused with semantic information can reduce the influence from dynamic objects and achieve higher accuracy. This paper aims to present a new semantic stereo V-SLAM method toward outdoor dynamic environments for more accurate pose estimation.

First, the Deeplabv3+ semantic segmentation model is adopted to recognize semantic information about dynamic objects in the outdoor scenes. Second, an approach that combines prior knowledge to determine the dynamic hierarchy of moveable objects is proposed, which depends on the pixel movement between frames. Finally, a semantic stereo V-SLAM based on ORB-SLAM2 to calculate accurate trajectory in dynamic environments is presented, which selects corresponding feature points on static regions and eliminates useless feature points on dynamic regions.

The proposed method is successfully verified on the public data set KITTI and ZED2 self-collected data set in the real world. The proposed V-SLAM system can extract the semantic information and track feature points steadily in dynamic environments. Absolute pose error and relative pose error are used to evaluate the feasibility of the proposed method. Experimental results show significant improvements in root mean square error and standard deviation error on both the KITTI data set and an unmanned aerial vehicle. That indicates this method can be effectively applied to outdoor environments.

The main contribution of this study is that a new semantic stereo V-SLAM method is proposed with greater robustness and stability, which reduces the impact of moving objects in dynamic scenes.