Robot autonomous navigation

Autonomous vehicle navigation is a very popular research area in the vision and control field. Based on Prof. Dickmanns' philosophy, we implement a navigation algorithm on thc small robot. The robot can rely on its eyes (the camera mounted on thc top of the robot) and control its wheels to walk through the sub-basement hallways of Caltech Moore Lab building. The speed we achieve is robot's mechanical maximum speed 0.5 m/s

Autonomous navigation system

An inertial navigation system utilizing a servo-controlled two degree of freedom pendulum to obtain specific force components in the locally level coordinate system is described. The pendulum includes a leveling gyroscope and an azimuth gyroscope supported on a two gimbal system. The specific force components in the locally level coordinate system are converted to components in the geographical coordinate system by means of a single Euler transformation. The standard navigation equations are solved to determine longitudinal and lateral velocities. Finally, vehicle position is determined by a further integration

Autonomous Spacecraft Navigation With Pulsars

An external reference system suitable for deep space navigation can be defined by fast spinning and strongly magnetized neutron stars, called pulsars. Their beamed periodic signals have timing stabilities comparable to atomic clocks and provide characteristic temporal signatures that can be used as natural navigation beacons, quite similar to the use of GPS satellites for navigation on Earth. By comparing pulse arrival times measured on-board a spacecraft with predicted pulse arrivals at a reference location, the spacecraft position can be determined autonomously and with high accuracy everywhere in the solar system and beyond. The unique properties of pulsars make clear already today that such a navigation system will have its application in future astronautics. In this paper we describe the basic principle of spacecraft navigation using pulsars and report on the current development status of this novel technology.Comment: 22 pages, 12 figures, 2 tables, to be published in the proceedings of the workshop "Relativistic Positioning Systems and their Scientific Applications", held on 19-21 Sept. 2012, Brdo near Kranj, Sloveni

Navigation and attitude reference for autonomous satellite launch and orbital operations

The navigation and attitude reference performance of a strapdown system are investigated for applications to autonomous satellite launch and orbital operations. It is assumed that satellite payloads are integrated into existing missile systems and that the boost, orbit insertion, and in-orbit operation of the satellite are performed autonomously without relying on external support facilities. Autonomous and long term accurate navigation and attitude reference are provided by a strapdown inertial navigation system aided by a star sensor and earth landmark sensor. Sensor measurement geometry and navigation and attitude update mechanizations are discussed. Performance analysis data are presented for following functional elements: (1) prelaunch alignment; (2) boost navigation and attitude reference; (3) post boost stellar attitude and navigation updates; (4) orbital navigation update using sensor landmark measurements; and (5) in-orbit stellar attitude update and gyro calibration. The system performances are shown to satisfy the requirements of a large class of satellite payload applications

Autonomous navigation for artificial satellites

An autonomous navigation system is considered that provides a satellite with sufficient numbers and types of sensors, as well as computational hardware and software, to enable it to track itself. Considered are attitude type sensors, meteorological cameras and scanners, one way Doppler, and image correlator

Correlation Flow: Robust Optical Flow Using Kernel Cross-Correlators

Robust velocity and position estimation is crucial for autonomous robot navigation. The optical flow based methods for autonomous navigation have been receiving increasing attentions in tandem with the development of micro unmanned aerial vehicles. This paper proposes a kernel cross-correlator (KCC) based algorithm to determine optical flow using a monocular camera, which is named as correlation flow (CF). Correlation flow is able to provide reliable and accurate velocity estimation and is robust to motion blur. In addition, it can also estimate the altitude velocity and yaw rate, which are not available by traditional methods. Autonomous flight tests on a quadcopter show that correlation flow can provide robust trajectory estimation with very low processing power. The source codes are released based on the ROS framework.Comment: 2018 International Conference on Robotics and Automation (ICRA 2018