A three-dimensional (3D) model of a novel 5-DoF type parallel manipulator with a couple-constrained wrench is constructed and its couple-constrained wrench is analyzed. First, the formulas are derived for solving the displacement, velocity, acceleration of the moving platform and moving links, and a workspace is constructed. Second, the formulas are derived for solving the inertial wrenches of the moving links. Third, a dynamics equation is established by considering the inertial wrenches and friction, and the formulas are derived for solving the dynamically active forces and the dynamically couple-constrained wrench. Finally, a numerical example is given to demonstrate the analytic solution of the kinematics and the dynamics, and the analytical solutions are verified by utilizing a simulation mechanism.

A generally accepted helicopter model used for control includes the effect of Small Body Forces (SBF) which couple the vehicle's rotational subsystem inputs to its translational dynamics. SBF result from tail rotor thrust and lateral forces due to main rotor flapping. It is well-known that SBF lead to a theoretically challenging stabilization problem for the tracking error dynamics. Hence, much of the existing work has neglected SBF in order to simplify control design. We design a controller that directly compensates the influence of the tail rotor component of the SBF. The design is validated in simulation and flight tests.

A novel reconfigurable tri-prism mobile robot with eight modes is proposed. The robot is composed of two feet connected by three U-R-U (universal-revolute-universal) limbs. The robot incorporates the kinematic properties of sphere robots, squirming robots, tracked robots, wheeled robots and biped robots. In addition, the somersaulting and turning modes are also explored. After the description of the robot, the DOF (degree-of-freedom) is calculated based on screw theory. The 3D model and simulations indicate that the robot can cross several typical obstacles and can also be folded via two approaches. Finally, the prototype experiments are presented to verify the feasibility of the proposed mobile robot in different motion mode.

We provide necessary and sufficient conditions for all projective transformations of the projectivized dual quaternion model of rigid body displacements that are induced by coordinate changes in moving and/or fixed frame. These transformations fix the quadrics of a pencil and preserve the two families of rulings of an exceptional three-dimensional quadric. Moreover, we fully characterize the constraint varieties of dyads with revolute and prismatic joints in the dual quaternion model. The constraint variety of a dyad with two revolute joints is a regular ruled quadric in a three-space that contains a “null quadrilateral.” If a revolute joint is replaced by a prismatic joint, this quadrilateral collapses into a pair of conjugate complex null lines and a real line but these properties are not sufficient to characterize such dyads. We provide a complete characterization by introducing a new invariant, the “Study fibre projectivity,” and we present examples that demonstrate its potential to explain hitherto not sufficiently well-understood phenomena.

Safe and accurate navigation for autonomous trajectory tracking of quadrotors using monocular vision is addressed in this paper. A second order Sliding Mode (2-SM) control algorithm is used to track desired trajectories, providing robustness against model uncertainties and external perturbations. The time-scale separation of the translational and rotational dynamics allows to design position controllers by giving a desired reference in roll and pitch angles, which is suitable for practical validation in quad-rotors equipped with an internal attitude controller. A Lyapunov based analysis proved the closed-loop stability of the system despite the presence of unknown external perturbations. Monocular vision fused with inertial measurements are used to estimate the vehicle's pose with respect to unstructured scenes. In addition, the distance to potential collisions is detected and computed using the sparse depth map coming also from the vision algorithm. The proposed strategy is successfully tested in real-time experiments, using a low-cost commercial quadrotor.

A robot should be able to estimate an accurate and dense 3D model of its environment (a map), along with its pose relative to it, all of it in real time, in order to be able to navigate autonomously without collisions.

As the robot moves from its starting position and the estimated map grows, the computational and memory footprint of a dense 3D map increases and might exceed the robot capabilities in a short time. However, a global map is still needed to maintain its consistency and plan for distant goals, possibly out of the robot field of view.

In this work, we address such problem by proposing a real-time stereo mapping pipeline, feasible for standard CPUs, which is locally dense and globally sparse and accurate. Our algorithm is based on a graph relating poses and salient visual points, in order to maintain a long-term accuracy with a small cost. Within such framework, we propose an efficient dense fusion of several stereo depths in the locality of the current robot pose.

We evaluate the performance and the accuracy of our algorithm in the public datasets of Tsukuba and KITTI, and demonstrate that it outperforms single-view stereo depth. We release the code as open-source, in order to facilitate the system use and comparisons.

This paper addresses the trajectory tracking control problem for a quadrotor aerial vehicle, equipped with a robotic manipulator (aerial manipulator). The controller is organized in two layers: in the top layer, an inverse kinematics algorithm computes the motion references for the actuated variables; in the bottom layer, a motion control algorithm is in charge of tracking the motion references computed by the upper layer. To the purpose, a model-based control scheme is adopted, where modelling uncertainties are compensated through an adaptive term. The stability of the proposed scheme is proven by resorting to Lyapunov arguments. Finally, a simulation case study is proposed to prove the effectiveness of the approach.

This study deals with the problem of trajectory tracking of wheeled mobile robots (WMR's) under non-holonomic constraints and in the presence of model uncertainties. To solve this problem, the kinematic and dynamic models of a WMR are first derived by applying the recursive Gibbs–Appell method. Then, new kinematics- and dynamics-based multivariable controllers are analytically developed by using the predictive control approach. The control laws are optimally derived by minimizing a pointwise quadratic cost function for the predicted tracking errors of the WMR. The main feature of the obtained closed-form control laws is that online optimization is not needed for their implementation. The prediction time, as a free parameter in the control laws, makes it possible to achieve a compromise between tracking accuracy and implementable control inputs. Finally, the performance of the proposed controller is compared with that of a sliding mode controller, reported in the literature, through simulations of some trajectory tracking maneuvers.

A novel pure-vision egomotion estimation algorithm is presented, with extensions to Unmanned Aerial Systems (UAS) navigation through visual odometry. Our proposed method computes egomotion in two stages using panoramic images segmented into sky and ground regions. Rotations (in 3DOF) are estimated by using a customised algorithm to measure the motion of the sky image, which is affected only by the rotation of the aircraft, and not by its translation. The rotation estimate is then used to derotate the optic flow field generated by the ground, from which the translation of the aircraft (in 3DOF) is estimated by another customised, iterative algorithm. Segmentation of the rotation and translation estimations allows for a partial relaxation of the planar ground assumption, inherently increasing the robustness of the approach. The translation vectors are scaled using stereo-based height to compute the current UAS position through path integration for closed-loop navigation. Outdoor field tests of our approach in a small quadrotor UAS suggest that the technique is comparable to the performance of existing state-of-the-art vision-based navigation algorithms, whilst also removing all dependence on additional sensors, such as an IMU or global positioning system (GPS).