A rapid maneuver path planning method with complex sensor pointing constraints in the attitude space

Abstract

Large-scale and high-resolution perception is easy to achieve for the physical world, if satellite technology was used in Internet of Things (IoT) in the future. Remote sensing satellite is superior to original method for ground target detection and environmental perception, which could be completed through onboard perception sensors. In the process of detection and perception, satellite needs to frequently perform attitude maneuver in order to meet a variety of task requirements. We have to face with multi-object, multi-sensors constrained maneuver problem. Not only the kinematic and dynamics constraints should been taken into account, but the engineering bounded constraints need to be considered. Moreover, sensor pointing constraints should be elaborated and analyzed reasonably. It is increasingly important how to achieve attitude maneuver in these complex constraints safely and rapidly. Firstly, sensor pointing constraints are translated to quadratic form in order to simplify the representation and computation in the attitude quaternion space. Secondly, we propose an improved RRT planning algorithm for spacecraft, which is able to address a variety of sensor pointing constraints. This algorithm will be used as a global planner, in which the uniformly distributed nodes in the expansion space are randomly sampled and the expanded nodes are screened out based on the comparative evaluation function. Finally, simulation results validate the advantages of the proposed algorithm.

Table Tennis Technical and Tactical Ability Based on Secure Internet of Things and BP Neural Network

“…In the process of moving target detection, the relatively static part is regarded as the background image, common background modeling methods include statistical average method and mixed Gaussian background modeling method [12,13].…”

“…According to the basic assumption that the instantaneous gray value does not change, there are I(x, y, t) � I(x + dx, y + dy, t + dt). (12) u and v are the velocity vectors along the horizontal and vertical axis, respectively:…”

Application of the Improved Grey Wolf Algorithm in Spacecraft Maneuvering Path Planning

“…Only when some constraints [38,39] are met can the spacecraft work better in space. That is, only under these conditions can the mission be carried out.…”

Attitude-constrained reorientation control for spacecraft based on extended state observer

“…Some image search algorithms have been introduced for attitude maneuver control. Such as the A* path planning method 3,4 the distortion-minimizing attitude parameterizations 5,6 and the random search tree method 7 with a smaller computational complexity. Based on the previous literature, 8 the pseudo-spectral method 9 has been used to generate the paths without any constraint violations between local nodes, which has achieved a better performance.…”