Distributed Control of Unmanned Surface Vessels Using the Derivative-free Nonlinear Kalman Filter

Abstract

Intelligence and autonomy is becoming a prerequisite for maritime transportation systems. In this paper a distributed control problem for unmanned surface vessels (USVs) is formulated as follows: there are N USVs which pursue another vessel (moving target). Each USV can be equipped with various sensors, such as IMU, cameras and non-imaging sensors such as sonar, radar and thermal signature sensors. At each time instant each USV can obtain measurements of the target's cartesian coordinates. Additionally, each USV is aware of the target's distance from a reference monitoring station (coastal or satellite monitoring units). The objective is to make the USVs converge in a synchronized manner towards the target, while avoiding collisions between them and avoiding collisions with obstacles in their motion plane. A distributed control law is developed for the USVs which enables not only convergence of the USVs to the goal position, but also makes possible to maintain the cohesion of the multi-USV system. Moreover, distributed filtering is performed, so as to obtain an estimate of the target vessel's state vector. This provides the desirable state vector to be tracked by each one of the USVs. To this end, a new distributed nonlinear filtering method of improved accuracy and computation speed is introduced. This filtering approach, under the name Derivative-free distributed nonlinear Kalman filter is based on differential flatness theory and on an exact linearization of the target vessel's dynamic/kinematic model. The performance of the proposed distributed filtering scheme is compared against the Extended and the Unscented Information filter.

An Overview to Visual Odometry and Visual SLAM: Applications to Mobile Robotics

“…Their simulation results showed that the distributed SLAM system has a similar estimation accuracy and requires only one-fifth of the computational time when compared to the centralized particle filter. Rigatos et al [95] outline the problem of multiple autonomous systems (unmanned surface vessels) tracking and perusing a target (another vessel). They proposed a solution for the problem of distributed con-trol of cooperating the unmanned surface vessels (USVs).…”

“…Complete mapping of the environment becomes very difficult when environment is dynamic or stochastic as in the case of moving obstacles. For such problems the secure autonomous navigation of the robot can be assured with the use of motion planning algorithms such as the one described above [95], in which the collisions risk is minimized.…”

Optimum Coordination of Directional Overcurrent Relays Using Modified Adaptive Teaching Learning Based Optimization Algorithm

“…Hybrid protection scheme using adaptive relaying and small size FCL is presented in [22]. The algorithm step and application of stochastic disturbance factor to bring the search to global minimum by escaping from local minima is explained in [23]. The run time of algorithm with respect to change in population size is presented in [24].…”