The capability of a quadruped robot to negotiate obstacles is tightly connected to its leg workspace and joint torque limits. When facing terrain where the height of obstacles is close to the leg length, the locomotion robustness and safety are reduced since more dynamic motions are required to traverse it. In this paper, we introduce a new mechanism called the Carpal-Claw, which enables quadruped robots to negotiate higher obstacles and adds safety to the locomotion by allowing the robot to negotiate obstacles under static and quasi-static locomotion and regular joint torque demands. The design of the mechanism is detailed, as well as the methodology to exploit the mechanism in the locomotion control framework. The Carpal-Claw functionality is validated through various experiments on a very high obstacle and stairs-like terrains using an Aliengo robot. We demonstrate how Aliengo can safely descend a step height of 40cm, which is 80% of its leg length. To the best knowledge of the authors, this is the first time a mechanism like the C-Claw is proposed for improving quadruped robot locomotion over high obstacles.

In this paper, we introduce the concept of using passive arm structures with intrinsic impedance for robot-robot and human-robot collaborative carrying with quadruped robots. The concept is meant for a leader-follower task and takes a minimalist approach that focuses on exploiting the robots’ payload capabilities and reducing energy consumption, without compromising the robot locomotion capabilities. We introduce a preliminary arm mechanical design and describe how to use its joint displacements to guide the robot’s motion. To control the robot’s locomotion, we propose a decentralized Model Predictive Controller that incorporates an approximation of the arm dynamics and the estimation of the external forces from the collaborative carrying. We validate the overall system experimentally by performing both robot-robot and human-robot collaborative carrying on a stair-like obstacle and on rough terrain.

Thickness measurement is a crucial matter in many applications, such as pipeline inspection by means of ultrasound. In this regard, an automated system must rely on an algorithm able to identify properly the ultrasonic echoes originated from the pipe's walls, which can be disturbed by noise and other sources of ultrasonic reflections. This paper describes the application of genetic algorithms for processing and analysis of signals obtained from thickness measurement using ultrasonic transducers. The main application for this algorithm is the processing and analysis of ultrasound signals obtained from oil duct inspections using ultrasonic pipeline inspection gauges (PIGs). The objective of the proposed algorithm is to identify correctly the ultrasound echoes in order to obtain an accurate thickness measurement, allowing the identification of cracks and corrosions in pipeline inspections. The algorithm was applied to several signals obtained from laboratory experiments with different distances between the transducer and a test plate with known thickness. Its efficiency was measured in terms of error percentage and computational cost.