Guidance system

About: Guidance system is a research topic. Over the lifetime, 4282 publications have been published within this topic receiving 45964 citations.

Guidance of Seekerless Interceptors Using Innovation Covariance Based Tuning of Kalman Filters

Abstract: In this paper, an integrated estimation/guidance algorithm that combines Kalman-filter-based interactive multiple-model estimators with a modified differential game guidance law is developed for seekerless interceptors in three-dimensional space. The target is assumed to perform various types of maneuvers, while the sensor has noisy measurements and the interceptor is subject to acceleration bound. To handle these, the Kalman filters used in the interactive multiple model are pretuned using two recently developed metrics based on innovation covariance. To determine the filter tuning parameters, the metrics are evaluated offline for the Kalman filter using different process models, and the tuned values of the process noise covariance are selected in each case. These tuned Kalman filters are then used in the interactive multiple-model configuration to cater to various maneuvers that are expected during the end game. A numerical study compares the performances of these, and a previously reported interactive ...

Application of guidance laws to quadrotor landing

Abstract: Aerial vehicle landing is an open and challenging problem. This paper focuses on development of simple guidance laws for quadrotor landing with known GPS location - stationary and moving. The guidance laws are based on missile guidance principles, namely pure pursuit, line-of-sight, and pure proportional navigation. Comparison on the performance of these guidance laws is presented through Matlab simulations and ROS implementation.

Video-image-based neural network guidance system with adaptive view-angles for autonomous vehicles

Abstract: This paper describes the guidance function of an autonomous vehicle based on a neural network controller using video images with adaptive view angles for sensory input. The guidance function for an autonomous vehicle provides the low-level control required for maintaining the autonomous vehicle on a prescribed trajectory. Neural networks possess unique properties such as the ability to perform sensor fusion, the ability to learn, and fault tolerant architectures, qualities which are desirable for autonomous vehicle applications. To demonstrate the feasibility of using neural networks in this type of an application, an Intelledex 405 robot fitted with a video camera and vision system was used to model an autonomous vehicle with a limited range of motion. In addition to fixed-angle video images, a set of images using adaptively varied view angles based on speed are used as the input to the neural network controller. It was shown that the neural network was able to control the autonomous vehicle model along a path composed of path segments unlike the exemplars with which it was trained. This system was designed to assess only the guidance system, and it was assumed that other functions employed in autonomous vehicle control systems (mission planning, navigation, and obstacle avoidance) are to be implemented separately and are providing a desired path to the guidance system. The desired path trajectory is presented to the robot in the form of a two-dimensional path, with centerline, that is to be followed. A video camera and associated vision system provides video image data as control feedback to the guidance system. The neural network controller uses Gaussian curves for the output vector to facilitate interpolation and generalization of the output space.

Fitness training guidance system and method thereof

Abstract: A real-time personal fitness training guidance system is described. A camera unit including one or more cameras is provided to take images of a user ready to use or using an exercise equipment. One or more current vital signs and physical activity information are derived from the captured images. Further, a personal fitness training guidance is generated for the user in accordance with the current vital signs, the physical activity information and a workout program the user is doing. To allow the user to see how he or she is doing in his/her workout, an output unit is provided to show the personal fitness training guidance to the user in real-time. An external device is also provided to generate a coaching guidance for the user.