Cascaded Kalman and particle filters for photogrammetry based gyroscope drift and robot attitude estimation

Based on a cascaded Kalman–Particle Filtering, gyroscope drift and robot attitude estimation method is proposed in this paper. Due to noisy and erroneous measurements of MEMS gyroscope, it is combined with Photogrammetry based vision navigation scenario. Quaternions kinematics and robot angular velocity dynamics with augmented drift dynamics of gyroscope are employed as system state space model. Nonlinear attitude kinematics, drift and robot angular movement dynamics each in 3 dimensions result in a nonlinear high dimensional system. To reduce the complexity, we propose a decomposition of system to cascaded subsystems and then design separate cascaded observers. This design leads to an easier tuning and more precise debugging from the perspective of programming and such a setting is well suited for a cooperative modular system with noticeably reduced computation time. Kalman Filtering (KF) is employed for the linear and Gaussian subsystem consisting of angular velocity and drift dynamics together with gyroscope measurement. The estimated angular velocity is utilized as input of the second Particle Filtering (PF) based observer in two scenarios of stochastic and deterministic inputs. Simulation results are provided to show the efficiency of the proposed method. Moreover, the experimental results based on data from a 3D MEMS IMU and a 3D camera system are used to demonstrate the efficiency of the method.     

Inverse Dynamic Model and a Control Application of a Novel 6-DOF Hybrid Kinematics Manipulator

Kinematics with six degrees of freedom can be of several types. This paper describes the inverse dynamic model of a novel hybrid kinematics manipulator. The so-called Epizactor consists of two planar disk systems that together move a connecting element in 6 DOF. To do so each of the disk systems has a linkage point equipped with a homokinetic joint. Each disk system can be described as a serial 3-link planar manipulator with unlimited angles of rotation. To compensate singularities, a kinematic redundancy is introduced via a fourth link. The kinematic concept leads to several technical advantages for compact 6-DOF-manipulators when compared to established parallel kinematics: The ratio of workspace volume and installation space is beneficial, the number of kinematic elements is smaller, and rotating drives are used exclusively. For a singularity-robust control-approach, the inverse dynamic model is derived using the iterative Newton–Euler-method. Feasibility is shown by the application of the model to an example where excessive actuator velocities and torques are avoided.     

Distributed observers for pose estimation in the presence of inertial sensory soft faults

Distributed Particle-Kalman Filter based observers are designed in this paper for inertial sensors (gyroscope and accelerometer) soft faults (biases and drifts) and rigid body pose estimation. The observers fuse inertial sensors with Photogrammetric camera. Linear and angular accelerations as unknown inputs of velocity and attitude rate dynamics, respectively, along with sensory biases and drifts are modeled and augmented to the moving body state parameters. To reduce the complexity of the high dimensional and nonlinear model, the graph theoretic tearing technique (structural decomposition) is employed to decompose the system to smaller observable subsystems. Separate interacting observers are designed for the subsystems which are interacted through well-defined interfaces. Kalman Filters are employed for linear ones and a Modified Particle Filter for a nonlinear non-Gaussian subsystem which includes imperfect attitude rate dynamics is proposed. The main idea behind the proposed Modified Particle Filtering approach is to engage both system and measurement models in the particle generation process. Experimental results based on data from a 3D MEMS IMU and a 3D camera system are used to demonstrate the efficiency of the method.