This article presents and studies a recursive information consensus filter for decentralized dynamic state estimation under circumstances in which the communication network is unreliable. Local estimators are assumed to have access only to local information, and no structure is assumed about the topology of the communication network, which need not be connected at all times. The filter is a hybrid approach: it uses iterative covariance intersection to reach consensus over priors, which might become correlated, while consensus over new information is handled using weights based on a Metropolis–Hastings Markov chain. We establish bounds for estimation performance and show that this hybrid method produces unbiased conservative estimates that are better than covariance intersection. The performance of the hybrid method is evaluated extensively, including comparisons with competing algorithms, with a hypothetical “full history” yardstick, and centralized performance. We conduct an assessment on a realistic atmospheric dispersion problem and also on more carefully crafted settings to help characterize particular aspects of the performance.

This paper proposes a new loss function for linear autoencoders (LAEs) and analytically identifies the structure of the associated loss surface. Optimizing the conventional Mean Square Error (MSE) loss results in a decoder matrix that spans the principal subspace of the sample covariance of the data, but, owing to an invariance that cancels out in the global map, it will fail to identify the exact eigenvectors. We show here that our proposed loss function eliminates this issue, so the decoder converges to the exact ordered unnormalized eigenvectors of the sample covariance matrix. We characterize the full structure of the new loss landscape by establishing an analytical expression for the set of all critical points, showing that it is a subset of critical points of MSE, and that all local minima are still global. Specifically, the invariant global minima under MSE are shown to become saddle points under the new loss. Additionally, the computational complexity of the loss and its gradients are the same as MSE and, thus, the new loss is not only of theoretical importance but is of practical value, e.g., for low-rank approximation.

This paper, as a generalization of our previous works, presents a unified time-optimal path-following controller for Wheeled Mobile Robots (WMRs). Unlike other path-following controllers, we solve the path-following problem for all common categories of WMRs such as car-like, differential, omnidirectional, all wheels steerable and others. We show that the insertion of our path-following controller into the kinematic and non-holonomic constraints of the wheels, simplifies the otherwise impenetrable constraints, resulting in explicit monotonic functions between the velocity of the base and that of the wheels. Based on this foundation, we present a closed-form solution that keeps all the wheels' steering and driving velocities within their corresponding pre-specified bounds. Simulation data and experimental results from executing the controller in a real-time environment demonstrate the efficacy of the method.

Mobile robots with independently steerable wheels provide better robustness and efficiency compared to the other types of omnidirectional mobile robots. However, the non-holonomic constraints and singular configurations give rise to several challenging issues in exploiting the high maneuverability features of the robot. Many proposed motion controllers for such robots force the robot to stay outside of bulky regions around its singular points, which in turn limits the robot's dexterity. In this paper, which extends our previous works, we present an online trajectory generation along with a globally stable path following controller that enables the robot to follow any given smooth path and heading function. We show that the control signals extensively simplify the kinematic constraints and are utilized to develop an efficient online “Phase Plane” switching algorithm that bounds the velocities and accelerations of the actuators. Moreover, we show that the algorithm efficiently regulates the velocity of the robot around the singular configurations which allows the robot to realize wide ranges of complex maneuvers. The proposed control algorithm has been tested on iMoro(our four-wheeled independently steerable mobile manipulator), and the presented results show the efficacy of our method.

Mobile robots with independently steerable wheels possess many high maneuverability features of omnidirectional robots while benefiting from better performance and capability of moving on rough terrains. However, motion control of such robots is a challenging task due to presence of singular configurations and unboundedly large steering velocities in the neighborhood of those singularities. Many proposed approaches rely on numerical solutions that keep the robot out of bulky regions around the singular points and hence lose some of the robot maneuverability. Based on a class of traditional path followers we design a new globally stable path following controller that exploits the high maneuverability of the platform. This design allows us to derive a set of closed-form analytical functions that describe the robot base velocity as a function of the wheels driving and steering velocities while abide to the robot non-holonomic constraints. Those functions are then utilized to find the maximum instantaneous velocity of the body that keeps the wheels velocities under the pre-specified bounds no matter how much the robot gets close or far from its singular configurations. The control algorithms developed in this paper have been evaluated on iMoro, a four wheel independently steered mobile manipulator designed and developed at IHA/TUT. Experimental data is also shown that show efficacy of the method.

Newly developed cable driven redundant parallel manipulators (CDRPM) have numerous advantages compared to that of the conventional parallel mechanisms. However, there exist some challenging issues in over-constrained mechanisms like CDRPMs. In contrast to serial manipulators, complexity of parallel manipulator forward kinematics (FK) is one of the main issues being under study in the control of such manipulators. Moreover, using extra sensory data is a common approach in the FK solution of rigid-linked parallel manipulators, which is considered by fewer researchers for CDRPMs. In this paper, tension force sensors of the cables are used as an extra sensor to simplify analytical solution of the FK for a planar CDRPM. To find a suitable solution, geometrical and physical characteristics of the robot are analyzed. It is shown that the proposed method provides the required accuracy and significantly improves the process time compared to the conventional methods.

Dynamic analysis of parallel manipulators plays a vital role in the design and control of such manipulators. Closed-chain kinematic structure affects the dynamics formulations by several constraints. Therefore, especially for higher degrees of freedom manipulators, manipulation of implicit and bulky dynamics formulation looses the tractability of the analysis. In this paper, a methodology and some simplification tools are introduced to achieve explicit dynamics formulation for parallel manipulators. This methodology is applied for the dynamics analysis of the most celebrated parallel manipulator, namely Stewart-Gough platform. By avoiding any recursive or component-wise derivations, the resulting dynamics formulation provides more insight for designers, and can be much easier used in any model-based control of such manipulators. In order to verify the resulting dynamics equations, Lagrange method is used to derive and compare the manipulator mass matrix. This methodology can be further used to formulate the explicit dynamics of other parallel manipulators.