This paper presents a new joint coined as the rT joint and proposes two types of metamorphic parallel mechanisms assembled with this rT joint. In the first type, the mechanism changes its topology by turning the rT joints in all limbs into different configurations. This change in mobility is completed by two cases illustrated by a $3(rT)PS$ metamorphic parallel mechanism having variable mobility from 3 to 6 and a $3(rT)P(rT)$ parallel mechanism having various configurations including pure translations, pure rotations, and mobility 4. In the second type, a central strut with the rT joint is added in a parallel mechanism. The variable mobility of the mechanism results from the topological change of the central $(rT)P(rT)$ strut. This is illustrated in a $3SPS-1(rT)P(rT)$ metamorphic parallel mechanism, which changes its mobility from 4 to 5. It is demonstrated in mobility analysis that the change in local mobility of each limb results in the change in the platform mobility that a metamorphic process can be achieved. This particular analysis leads to advancement of improved Grübler–Kutzbach criterion by introducing the local mobility factor in the mobility analysis.

Achieving precise state estimation is needed for the unmanned aerial vehicle to perform a successful flight with a high degree of stability. Nonetheless, obtaining accurate state estimation is considered challenging due to the inaccuracies associated with the measurements of the onboard commercial-off-the-shelf inertial measurement unit. The immense vibration of the vehicle's rotors makes these measurements suffer from issues like large drifts, biases, and immense unpredictable noise sequences. These issues cannot be significantly tackled using classical estimators, and an accurate sensor fusion technique needs to be developed. In this paper, a deep learning (DL) framework is developed to enhance the performance of the state estimator. A deep neural network (DNN) is trained using a deep-learning-based technique to identify the associated measurement noise models and filter them out. The dropout technique is adopted for training DNN to avoid overfitting and reduce the complexity of nets computations. Compared to the classical estimation results, the proposed DL technique demonstrates capabilities in identifying the measurement's noise characteristics. As an example, an enhancement in estimating the attitude states at near hover is proven using this approach. Furthermore, an actual hover flight was performed to validate the proposed estimation enhancement method.

Advances in soft robotics and material science have enabled rapid progress in soft grippers. The ability to 3D print materials with softer, more elastic materials properties is a recent development and a key enabling technology for the rapid development of soft robots. However, obtaining the desired mechanical properties (e.g., stiffness) of the soft joints and information about the parameters to select in 3D printers is often not straightforward. In this article, we propose the use of interpenetrating phase composites (IPCs) materials with mathematically generated topologies based on triply periodic minimal surfaces for the development of soft grippers with desired mechanical properties. The flexible joints of the gripper can be realized through two or more phases that are topologically interconnected such that each phase represents a standalone cellular structure. As a case study, we present the design and development of a two-finger soft gripper as an example to demonstrate the application scenario of our approach. The flexible parts with desired stiffness values are realized by using IPCs materials in which the reinforcement distribution can be regulated on the basis of mathematical models. We characterized the properties of the material through a set of quantitative experiments on IPCs material specimens, and then we realized qualitative grasping tests with the gripper and a set of objects with different shapes and sizes. We showed that by properly regulating the properties of IPCs material it is possible to design modular grippers with the same structure, but different closure motions. Grippers can be customized for different tasks by easily assembling and disassembling fingers.