Autonomous Navigation Capabilities Research Articles

Mars methane analogue mission: Mission simulation and rover operations at Jeffrey Mine and Norbestos Mine Quebec, Canada

The Canadian Space Agency (CSA), through its Analogue Missions program, supported a microrover-based analogue mission designed to simulate a Mars rover mission geared toward identifying and characterizing methane emissions on Mars. The analogue mission included two, progressively more complex, deployments in open-pit asbestos mines where methane can be generated from the weathering of olivine into serpentine: the Jeffrey mine deployment (June 2011) and the Norbestos mine deployment (June 2012). At the Jeffrey Mine, testing was conducted over 4days using a modified off-the-shelf Pioneer rover and scientific instruments including Raman spectrometer, Picarro methane detector, hyperspectral point spectrometer and electromagnetic induction sounder for testing rock and gas samples. At the Norbestos Mine, we used the research Kapvik microrover which features enhanced autonomous navigation capabilities and a wider array of scientific instruments. This paper describes the rover operations in terms of planning, deployment, communication and equipment setup, rover path parameters and instrument performance. Overall, the deployments suggest that a search strategy of “follow the methane” is not practical given the mechanisms of methane dispersion. Rather, identification of features related to methane sources based on image tone/color and texture from panoramic imagery is more profitable.

An innovative high-precision SINS/CNS deep integrated navigation scheme for the Mars rover

The Mars rovers are the key to Mars exploration missions. In order to achieve long-time autonomous roving and perform science operations, Mars rovers need to have the capability of high-precision autonomous navigation. According to the characteristics of the Martian environment, an innovative scheme of strap-down inertial navigation system/celestial navigation system (SINS/CNS) deep integration for the Mars rover and its implementation are proposed. First, the large field of view (FOV) star sensor is utilized to assist the attitude matrix of SINS by continually observing and correcting the misalignment angles and gyro drifts. Thus, the high-precision mathematic reference is obtained. Then, the star sensor makes use of the reference provided by SINS to obtain the local position vector. The rover's position and attitude can be determined by CNS. On the basis of the mathematics reference, the innovative scheme of SINS/CNS deep integration which takes full advantage of the navigation information of each subsystem is presented. Moreover, the errors of inertial measurement unit (IMU) and the navigation parameters in SINS are estimated and corrected. And the deep integration of SINS/CNS is performed. Compared to the optimal integration mode, this scheme improves navigation accuracy. In the end, the simulation results indicate that this scheme is steady and reliable.

A real-time prognostic method for the drift errors in the inertial navigation system by a nonlinear random-coefficient regression model

Inertial navigation systems have been widely used in both civilian and military systems because of their autonomous navigation capability. Nevertheless, due to its autonomous characteristics, the navigation precision of an inertial navigation system is heavily influenced by its drift errors, which results from the performance degradation of the system in use. One of the most effective means of eliminating such adverse effects is to predict the drift error values in advance, and compensate for them subsequently. It is therefore significantly important to accurately predict the degrading trend of the drift errors of an inertial navigation system. We propose a novel degradation modeling method based on a nonlinear random-coefficient regression model to predict the drift errors. The parameters of the model are dynamically updated by the expectation maximization algorithm, in conjunction with the Bayesian inference method at the time when a new drift error data is observed. In doing this, the degrading trend of the drift errors can be predicted in real time. Finally, a batch of drift error data of an inertial navigation system is used to validate the feasibility and effectiveness of the developed prognostic method.

Detection and Tracking of Human Legs by a Mobile Robot

The autonomous navigation capabilities of mobile robots is a social and technological issue.Many potential applications add to the importance of human following technology.Some of the applications include museum guide robots and service robots at shopping center.In this paper,we propose the detection and tracking schemes for human legs by the use of a camera.The robust tracking of humans is achieved by exploiting a human walking model.The leg detection and tracking schemes are realized by the combined application of support vector data description algorithm and a wheeled mobile robot.The proposed scheme is successfully verified in real time environments. Index term: Human following, Human-robot interaction,Leg detection,Mobile robot navigation,SVDD algorithm.

Development and Flight Test Evaluations of an Autonomous Obstacle Avoidance System for a Rotary-Wing UAV

This paper presents the development and flight-testing of an obstacle avoidance system that can provide a rotary-wing unmanned aerial vehicle (UAV) the autonomous obstacle field navigation capability in uncertain environment. The system is composed of a sensor, an obstacle map generation algorithm from sensor measurements, an online path planning algorithm, and an adaptive vehicle controller. The novel approach of path planning presented in the paper is the integration of a newly developed receding horizon (RH) trajectory optimization scheme with a global path searching algorithm. The developed RH trajectory optimization scheme solves the local nonlinear trajectory optimization problem using approximated vehicle dynamics, maneuverability constraints, and terrain constraints within the finite range of the sensor. The global path searching by dynamic programming algorithm finds the shortest path to the destination to provide the initial guess to the RH trajectory optimization. The spline-based direct solver, Nonlinear Trajectory Generation (NTG), solves the RH trajectory optimization in real time and updates the solution continuously. The developed system is implemented within the Georgia Tech UAV Simulation Tool (GUST) and on the onboard computer of the Georgia Tech UAV test bed. Simulations and flight tests carried out for the benchmark scenarios and with sensor-in-the-loop flight tests demonstrated the viability of the developed system for autonomous obstacle field navigation capability of a UAV.

Integrating Off-Board Cameras and Vehicle On-Board Localization for Pedestrian Safety

Situational awareness for industrial vehicles is crucial to ensure safety of personnel and equipment. While human drivers and onboard sensors are able to detect obstacles and pedestrians within line-of-sight, in complex environments, initially occluded or obscured dynamic objects can unpredictably enter the path of a vehicle. We propose a system that integrates a vision-based offboard pedestrian tracking subsystem with an onboard localization and navigation subsystem. This combination enables warnings to be communicated and effectively extends the vehicle controller's field of view to include areas that would otherwise be blind spots. A simple flashing light interface in the vehicle cabin provides a clear and intuitive interface to alert drivers of potential collisions. Alternatively, the system can be also applied to vehicles that have autonomous navigation capabilities, in which case, instead of alert lights, the vehicle is halted or redirected. We implemented and tested the proposed solution on an automated industrial vehicle under autonomous operation and on a human-driven vehicle in a full-scale production facility, over a period of four months.

Image Processing Algorithms For Deep-Space Autonomous Optical Navigation

As Earth-based radio tracking navigation is severely limited because of communications constraints and low relative navigation accuracy, autonomous optical navigation capabilities are essential for both robotic and manned deep-space exploration missions. Image processing is considered one of the key technologies for autonomous optical navigation to extract high-precision navigation observables from a raw image. New image processing algorithms for deep-space autonomous optical navigation are developed in this paper. First, multiple image pre-processing and the Canny edge detection algorithm are adopted to identify the edges of target celestial bodies and simultaneously remove the potential false edges. Secondly, two new limb profile fitting algorithms are proposed based on the Least Squares method and the Levenberg-Marquardt algorithm, respectively, with the assumption that the perspective projection of a target celestial body on the image plane will form an ellipse. Next, the line-of-sight (LOS) vector from the spacecraft to the centroid of the observed object is obtained. This is taken as the navigation measurement observable and input to the navigation filter algorithm. Finally, the image processing algorithms developed in this paper are validated using both synthetic simulated images and real flight images from theMESSENGERmission.

A New Celestial Navigation Method for Spacecraft on a Gravity Assist Trajectory

A practical and reliable capability for autonomous navigation needs to reduce operation cost, to improve operational efficiency, and to increase mission safety. Celestial navigation is a very attractive autonomous navigation solution for deep space spacecraft. There are mainly two kinds of celestial navigation methods: the direct calculation method and the filter method. The accuracy of the direct calculation method is low and very sensitive to the measurement noise. The filter method can provide a better navigation performance if a high accuracy dynamical model is available. However, the main practical problem existing in the autonomous celestial navigation of spacecraft on a gravity assist trajectory is that the accuracy of trajectory model is not enough to be used in the real navigation sometimes, which may introduce large estimation error and even cause filter divergence. To solve this problem, a new celestial navigation method is proposed in this paper, which effectively combines the direct calculation method and the filter method using an interacting multiple model unscented Kalman filter (IMMUKF). The ground experimental results demonstrate that this method can provide better navigation performance and higher reliability than the traditional direct calculation method and filter method.

Autonomous satellite navigation using starlight refraction angle measurements

An on-board autonomous navigation capability is required to reduce the operation costs and enhance the navigation performance of future satellites. Autonomous navigation by stellar refraction is a type of autonomous celestial navigation method that uses high-accuracy star sensors instead of Earth sensors to provide information regarding Earth’s horizon. In previous studies, the refraction apparent height has typically been used for such navigation. However, the apparent height cannot be measured directly by a star sensor and can only be calculated by the refraction angle and an atmospheric refraction model. Therefore, additional errors are introduced by the uncertainty and nonlinearity of atmospheric refraction models, which result in reduced navigation accuracy and reliability. A new navigation method based on the direct measurement of the refraction angle is proposed to solve this problem. Techniques for the determination of the refraction angle are introduced, and a measurement model for the refraction angle is established. The method is tested and validated by simulations. When the starlight refraction height ranges from 20 to 50km, a positioning accuracy of better than 100m can be achieved for a low-Earth-orbit (LEO) satellite using the refraction angle, while the positioning accuracy of the traditional method using the apparent height is worse than 500m under the same conditions. Furthermore, an analysis of the factors that affect navigation accuracy, including the measurement accuracy of the refraction angle, the number of visible refracted stars per orbit and the installation azimuth of star sensor, is presented. This method is highly recommended for small satellites in particular, as no additional hardware besides two star sensors is required.

A Telepresence Mobile Robot Controlled With a Noninvasive Brain–Computer Interface

This paper reports an electroencephalogram-based brain-actuated telepresence system to provide a user with presence in remote environments through a mobile robot, with access to the Internet. This system relies on a P300-based brain-computer interface (BCI) and a mobile robot with autonomous navigation and camera orientation capabilities. The shared-control strategy is built by the BCI decoding of task-related orders (selection of visible target destinations or exploration areas), which can be autonomously executed by the robot. The system was evaluated using five healthy participants in two consecutive steps: 1) screening and training of participants and 2) preestablished navigation and visual exploration telepresence tasks. On the basis of the results, the following evaluation studies are reported: 1) technical evaluation of the device and its main functionalities and 2) the users' behavior study. The overall result was that all participants were able to complete the designed tasks, reporting no failures, which shows the robustness of the system and its feasibility to solve tasks in real settings where joint navigation and visual exploration were needed. Furthermore, the participants showed great adaptation to the telepresence system.

The intelligent flying eye

Search-and-rescue operations can be very difficult. Consider as a typical scenario a partially collapsed building that may contain trapped survivors. But those people are surrounded by debris that presents a formidable obstacle to searchers. How do you find the survivors, and how do you get them out safely? The most important task in such a situation is to narrow down the search area and guide the rescue workers to the right location. The rescuers also need to know the inner state of the building to assess the damage, anticipate likely risks, and determine the best rescue strategy. Any ground-based robotic device that is sent into the damaged area to obtain this information will very likely get entangled in rubble. So what are the alternatives? What if you could send in a tiny helicopter, a micro air vehicle (MAV), to do the job? A MAV benefits from its vertical degree of freedom: it can evade obstacles on floors by changing altitude, and it can use vertical spaces, such as elevator shafts, to quickly access the most badly damaged levels. Within minutes, it can bring back video images of the wrecked interior. In addition to showing the locations of survivors, the images can help civil engineers assess the stability of the structure to help protect both rescue workers and the victims they are trying to save. The PIXHAWK Cheetah MAV (see Figure 1) is such a device. With onboard intelligence and autonomous-navigation capabilities, the Cheetah can be an extremely useful tool for rescue missions in hazardous environments. This four-rotor MAV is 55cm in diameter, measured across its rotor blades. Powered by lithium polymer batteries, the Cheetah can fly for up to 12 minutes. It features four cameras with flexible mountings and an onboard image-processing computer (see Figure 2). The Cheetah’s computer-vision system enables it to assess and solve navigational problems with no outside help. Some robotic devices send back images by means of wireless communication. Current MAV designs use wireless image transmission and offboard processing1, 2 or global-positioning systems (GPS) for controlling the vehicle.3 A wireless-transmission Figure 1. Operational model of the PIXHAWK Cheetah.

Pulsar/CNS integrated navigation based on federated UKF

In order to improve the autonomous navigation capability of satellite, a pulsar/CNS (celestial navigation system) integrated navigation method based on federated unscented Kalman filter (UKF) is proposed. The celestial navigation is a mature and stable navigation method. However, its position determination performance is not satisfied due to the low accuracy of horizon sensor. Single pulsar navigation is a new navigation method,, which can provide highly accurate range measurements. The major drawback of single pulsar navigation is that the system is completely unobservable. As two methods are complementary to each other, the federated UKF is used here for fusing the navigation data from single pulsar navigation and CNS. Compared to the traditional celestial navigation method and single pulsar navigation, the integrated navigation method can provide better navigation performance. The simulation results demonstrate the feasibility and effectiveness of the navigation method.

Vision-based Target Tracking for Micro Air Vehicles Using Random Set Theory

Micro Air Vehicles (MAV) can deliver their full potential only if they are equipped with autonomous navigation capabilities. Optic flow has emerged as a significant tool for many autonomous navigation tasks. Optic flow fields remain to some extent uncertain because of image noise, lighting changes, low contrast regions, multiple motions in single localized regions and so on. Tracking multiple targets by itself is a challenging problem; it is made more challenging by three factors in the MAV scenario: the uncertainties in optic flow, 6 degrees of freedom (DoF) of movement of the platform and possible 6 DoF of movement of the targets. Finite set statistics provides a unified probabilistic foundation for multisource-multitarget tracking; its computational complexity has been addressed by Probability Hypothesis Density filters and many approximation techniques. We propose a statistical model for tracking multiple objects (targets) from optic flow information obtained from nose-mounted camera. Significant cont...

Autonomous Navigation for Unmanned Aerial Vehicles Based on Chaotic Bionics Theory

In this paper a new reactive mechanism based on perception-action bionics for multi-sensory integration applied to Unmanned Aerial Vehicles (UAVs) navigation is proposed. The strategy is inspired by the olfactory bulb neural activity observed in rabbits subject to external stimuli. The new UAV navigation technique exploits the use of a multiscroll chaotic system which is able to be controlled in real-time towards less complex orbits, like periodic orbits or equilibrium points, considered as perceptive orbits. These are subject to real-time modifications on the basis of environment changes acquired through a Synthetic Aperture Radar (SAR) sensory system. The mathematical details of the approach are given including simulation results in a virtual environment. The results demonstrate the capability of autonomous navigation for UAV based on chaotic bionics theory in complex spatial environments.

6584 A phase III study of CapeOx +/− lapatinib in HER2 positive locally-advanced/metastatic upper gastrointestinal adenocarcinoma: interim safety results

The Canadian Space Agency (CSA), through its Analogue Missions program, supported a microrover-based analogue mission designed to simulate a Mars rover mission geared toward identifying and characterizing methane emissions on Mars. The analogue mission included two, progressively more complex, deployments in open-pit asbestos mines where methane can be generated from the weathering of olivine into serpentine: the Jeffrey mine deployment (June 2011) and the Norbestos mine deployment (June 2012). At the Jeffrey Mine, testing was conducted over 4 days using a modified off-the-shelf Pioneer rover and scientific instruments including Raman spectrometer, Picarro methane detector, hyperspectral point spectrometer and electromagnetic induction sounder for testing rock and gas samples. At the Norbestos Mine, we used the research Kapvik microrover which features enhanced autonomous navigation capabilities and a wider array of scientific instruments. This paper describes the rover operations in terms of planning, deployment, communication and equipment setup, rover path parameters and instrument performance. Overall, the deployments suggest that a search strategy of “follow the methane” is not practical given the mechanisms of methane dispersion. Rather, identification of features related to methane sources based on image tone/color and texture from panoramic imagery is more profitable.

Qualitative Vision-Based Path Following

We present a simple approach for vision-based path following for a mobile robot. Based upon a novel concept called the funnel lane, the coordinates of feature points during the replay phase are compared with those obtained during the teaching phase in order to determine the turning direction. Increased robustness is achieved by coupling the feature coordinates with odometry information. The system requires a single off-the-shelf, forward-looking camera with no calibration (either external or internal, including lens distortion). Implicit calibration of the system is needed only in the form of a single controller gain. The algorithm is qualitative in nature, requiring no map of the environment, no image Jacobian, no homography, no fundamental matrix, and no assumption about a flat ground plane. Experimental results demonstrate the capability of real-time autonomous navigation in both indoor and outdoor environments and on flat, slanted, and rough terrain with dynamic occluding objects for distances of hundreds of meters. We also demonstrate that the same approach works with wide-angle and omnidirectional cameras with only slight modification.

Celestial Navigation Methods for Space Explorers

With the development of the space exploration missions, a practical and reliable capability for autonomous navigation is urgently needed to reduce operation cost, to improve operational efficiency and to increase mission safety by avoiding dangerous delay in ground control. Celestial navigation is a very attractive autonomous navigation solution for the space exploration missions. It suits LEO satellites as well as high earth orbit (HEO) satellites and deep space explorers. In this paper, a comprehensive overview of the current state and future trend of celestial navigation methods for three types of space explorers are introduced, which include satellites, deep space probes and planetary rovers.

Impacting small Near Earth Objects

The design of a low-cost spacecraft to impact on a small, faint Near Earth Object (NEO), poses major challenges. This paper focuses on the terminal phase of such impact mission, analyzing the capability of autonomous Guidance, Navigation and Control (GNC) systems to compensate the deviations in the impact point to achieve a successful collision. The autonomous GNC system employs the information of the optical sensors to estimate the parameters allowing the computation of divert maneuvers to achieve the impact. GMV has developed a simulator, with different levels of sophistication, and a set of different GNC algorithms to help in the design process. This tool is used for different purposes such as: dimensioning the sensors and actuators, verifying mission requirements, computing figures of merit of different SC configurations and evaluating GNC performances. Four GNC algorithms are compared: low-thrust proportional navigation using a fading memory filter, high-thrust predictive guidance using either a Kalman filter or a batch-sequential least-squares filter, and a mid-thrust hybrid predictive-proportional guidance using a fading memory filter. Monte Carlo analysis using global-performances models of the optical sensors for each of these GNC algorithms are presented for two different asteroids (1989 ML and 2002 AT4), showing the mission parameters driving the mission performances. In addition, single-runs with high-fidelity optical sensors models are presented to validate the Monte Carlo simulations.

구조화된 실내 환경에서 초음파센서를 이용한 모바일 로봇 실시간 localization 기법

In order to increase the autonomous navigation capability of a mobile robot, it is very crucial to develop a method for the robot to be able to recognize a priori hon structured environmental characteristics. This paper proposes an ultrasonic sensor based real-time method for recognizing a priori known structured indoor environmental characteristics like a wall and comer Unlike the methods reported in the literature the information obtained from the sensor can be processed in real-time by extended Kalman filter to update estimations of the position and orientation of robot with respect to known environmental characteristics.

A portable, autonomous, urban reconnaissance robot

Portable mobile robots, in the size class of 20 kg or less, could be extremely valuable as autonomous reconnaissance platforms in urban hostage situations and disaster relief. We have developed a prototype urban robot on a novel chassis with articulated tracks that enable stair climbing and scrambling over rubble. Autonomous navigation capabilities of the robot include stereo vision-based obstacle avoidance, visual servoing to user-designated goals, and autonomous vision-guided stair climbing. The system was demonstrated in an urban reconnaissance mission scenario at Fort Sam Houston in October 1999. A two-axis scanning laser rangefinder has been developed and integrated into the robot for indoor mapping and position estimation. This paper describes the robot, its performance in field trials, and some of the technical challenges that remain to enable fieldable urban reconnaissance robots.