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The purpose of this paper is to facilitate autonomous landing of a multi-rotor unmanned aerial vehicle (UAV) on a moving/tilting platform using a robust vision-based approach.

Autonomous landing of a multi-rotor UAV on a moving or tilting platform of unknown orientation in a GPS-denied and vision-compromised environment presents a challenge to common autopilot systems. The paper proposes a robust visual data processing system based on targets’ Oriented FAST and Rotated BRIEF features to estimate the UAV’s three-dimensional pose in real time.

The system is able to visually locate and identify the unique landing platform based on a cooperative marker with an error rate of 1° or less for all roll, pitch and yaw angles.

The proposed vision-based system aims at on-board use and increased reliability without a significant change to the computational load of the UAV.

The simplicity of the training procedure gives the process the flexibility needed to use a marker of any unknown/irregular shape or dimension. The process can be easily tweaked to respond to different cooperative markers. The on-board computationally inexpensive process can be added to off-the-shelf autopilots.

The paper aims to focus on a vision-based approach to advance the automated process of the manufacturing of an Airbus A350’s pressure bulkhead. The setup enables automated deformation and draping of a fiber textile on a form-variable end-effector.

The proposed method uses the information of infrared (IR) and color-based images in Red, Green and Blue (RGB) representative format, as well as depth measurements to identify the wrinkles and boundary edge of semi-finished dry fiber products on the double-curved surface of a flexible modular gripper used for laying the fabric. The technique implements a simple and practical image processing solution using a sequence of pixel-wise binary masks on an industrial scale setup; it bridges the gap between laboratory experiments and real-world execution, thereby demonstrating practical and applied research.

The efficacy of the technique is demonstrated via experiments in the presented work. The two objectives as follows boundary edge detection and wrinkle detection are accomplished in real time in an industrial setup.

During the draping process, tensions developed in the fibers of the textile cause wrinkles on the surface, which are highly detrimental to the production process, material quality and strength. The proposed method automates the identification and detection of the wrinkles and the textile on the gripper surface. The proposed work aids in alleviating the problems caused by these wrinkles and helps in quality control in the production process.

Describes a dual‐arm mobile manipulator that can autonomously scan natural terrain using a typical handheld landmine detector in a manner similar to a human operator.

Presents a terrain‐scanning robot that consists of two articulated arms mounted on an off‐road remotely operated vehicle. One arm carries a laser and four ultrasonic rangefinders to build a terrain map. The map is used in real time to generate an obstacle‐free path for the second arm that manipulates the landmine detector autonomously. The arms are mounted on the vehicle that is controlled by an operator from a safe distance. Motion planning and control of the robot is carried out using an embedded computer that is linked to a host computer to transmit the detector data and operator commands.

Finds that the terrain‐scanning robot can effectively manipulate a relatively large landmine detector on rugged terrain with undulations and obstacles.

Proposes real‐time motion planning that may be equally applicable to other mobile manipulators.

Provides a technology that together with state‐of‐the‐art landmine sensors will offer a safe solution for detecting hidden landmines and clearing them from the postwar countries.

Introduces the concept of a dual‐arm mobile terrain scanning robot for landmine detection in off‐road missions and civilian areas where truck‐mounted detectors are inefficient.