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A decentralized controller for target tracking and obstacle avoidance for a multi-agent system is proposed. The controller is based on a navigation function which is composed of a goal function, as the D-optimality criterion of the Fisher information matrix, and a constraint function, which generates repulsive force to the agents near the obstacle. The multi-agent system is modeled as a graph and...
This paper presents results of developing and operating a team of multiple unmanned aircraft system. It is a challenging task since the system complexity increases as the number of the unmanned aircraft increases. Given the autonomy of each vehicle in navigation, guidance, control, and vision-based missions, a system of multiple unmanned aircraft is constructed which is expected to be applied to complicated...
This paper proposed a framework for airborne multisensor management for multitarget tracking, considering each of multiple unmanned aircraft as a sensor. The purpose of the algorithm is to quantify the performance of the multitarget tracking and to determine the time and location of the sensor deployment. Utilizing the posterior Cramer-Rao lower bound, the inverse of the Fisher information matrix,...
This paper focuses on unmanned aircraft guidance laws for a straight path and a circular orbit following using the vector field approach. The vector fields introduced in this paper can be applied to not only path following, but also other purposes such as arrival position, angle, and time control. Therefore, they could be applied to various missions providing advantages over other previous vector...
In this paper, we improve the accuracy and robustness of nonlinear least squares algorithm in pose estimation problem for UAV. To improve accuracy and robustness, first we reduced the noise of feature position of beacon. We apply Kalman Filter to feature position. After the Kalman Filter, the accuracy is improved approximately 40% in simulation study. Second, We organized the Relative Navigation Filter...
This paper presents a guidance law to operate intelligence, surveillance and reconnaissance missions using cooperative UAVs under known static threats. The guidance law is classified into three levels. In level 1 as the highest level, waypoints of UAVs are selected using different cost functions. In level 2, preliminary paths are generated using the A* algorithm for avoiding threats, and approaching...
A collision avoidance algorithm for unmanned aerial vehicles (UAVs) based on the conventional proportional navigation (PN) guidance law is investigated. The proportional navigation guidance law being applied to a wide range of missile guidance problems is tailored to the collision avoidance of UAVs. This can be accomplished by guiding the relative velocity vector of the aircraft to a vector connecting...
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