This work addresses mission planning for autonomous underwater gliders based on predictions of an uncertain, time-varying current field. Glider submersibles are highly sensitive to prevailing currents so mission planners must account for ocean tides and eddies. Previous work in variable-current path planning assumes that current predictions are perfect, but in practice these forecasts may be inaccurate. Here we evaluate plan fragility using empirical tests on historical ocean forecasts for which followup data is available. We present methods for glider path planning and control in a time-varying current field. A case study scenario in the Southern California Bight uses current predictions drawn from the Regional Ocean Monitoring System (ROMS).

This paper is about the autonomous control of an autonomous underwater vehicle (AUV), and the particular considerations required to allow proper control while towing a 100-meter vector sensor array. Mission related objectives are tempered by the need to consider the effect of a sequence of maneuvers on the motion of the towed array which is thought not to tolerate sharp bends or twists in sensitive material. We describe and motivate an architecture for autonomy structured on the behavior-based control model augmented with a novel approach for performing behavior coordination using multi-objective optimization. We provide detailed in-field experimental results from recent exercises with two 21-inch AUVs in Monterey Bay California.

In this paper a Bayesian fusion based template matching algorithm is proposed for the target tracking problem. Two different template matching methods (sum of the squared errors (SSE) and Gaussian mixture models (GMMs)) are weighted by their matching accuracies and then combined through the Bayesian theory to give a final robust template updating and matching. With the fusion of different template matching methods, the algorithm in this paper can deal with the problem such as the template drifting, shape deformation or occluded object matching.

This paper proposes an approach for object identification and tracking for autonomous vehicle application. In this scheme, data from the vehicle’s onboard vision and motion sensors are fused to identify the target 3D dynamic features in the world coordinate. Here several simple and basic linear dynamic models are combined to make the approximation of the target’s unpredicted or complex motion properties. With these basic linear dynamic models a detailed description of the 3D target tracking system with the interacting multiple models (IMM) for Extended Kalman Filtering is presented. The target’s final state estimates are obtained as a weighted combination of the outputs from each different model. Performance of the proposed interacting multiple dynamic model tracking algorithm is demonstrated through experimental results.

In this paper, an algorithm is proposed for vision-based object identification and tracking by autonomous vehicles. In order to estimate the speed of the tracking object, this algorithm fuses information captured by on-board sensors such as camera and inertial sensors. To formulate the tracking algorithm it is necessary to use a proper model which describes the dynamics of the tracking object. However due to complex nature of the moving object, it is necessary to have different dynamic models. Here, several simple and basic linear dynamic models are combined to approximate unpredictable, complex dynamics of the moving target. With these basic linear dynamic models, a detailed description of the three dimensional (3D) target tracking scheme using an interacting multiple model (IMM)alongwithanExtendedKalmanFilteringispresented. Thefinal state of the target is estimated as a weighted combination of the outputs from each different dynamic model. Performance of the proposed interacting multiple dynamic model tracking algorithm is demonstrated through experimental results. 1

This paper proposes a data fusion scheme for visual object identification and tracking by autonomous vehicles. In this scheme, image motion vectors fields, color features, visual disparity depth information and camera motion parameters are fused together to identify the target 3D visual and dynamic features. This paper also presents a detailed description of the 3D target tracking algorithm using an extended Kalman filter with a constant velocity dynamic model. Performance of the proposed scheme is discussed through experimental results.

In this paper, autonomous underwater vehicle (AUV) navigation schemes are proposed using forward looking sonar (FLS) and charged coupled device (CCD) camera for near bottom applications. Scans obtained from the onboard FLS are processed using a feature extraction technique discussed in the paper to extract stable point features in the environment. A technique is also proposed for AUVs to track optical features. Through field trials conducted using the test-bed vehicles NTU-UAV and the Twin-Burger2, the performance of the algorithms proposed are analyzed. The paper discusses the above-mentioned individual sensor capabilities and drawbacks in AUV navigation.

Road-boundary detection is an integral and important function in advanced driver-assistance systems and autonomous vehicle navigation systems. A prominent feature of roads in urban, semi-urban, and similar environments, such as in theme parks, campus sites, industrial estates, science parks, and the like, is curbs on either side defining the road's boundary. Although vision is the most common and popular sensing modality used by researchers and automotive manufacturers for road-lane detection, it can pose formidable challenges in detecting road curbs under poor illumination, bad weather, and complex driving environments. This paper proposes a novel method based on extended Kalman filtering for fast detection and tracking of road curbs using successive range/bearing readings obtained from a scanning two-dimensional ladar measurement system. As compared with millimeter wave radar methods reported in the literature, the proposed technique is simpler and computationally more efficient. This is the first of its kind reported in the literature. Qualitative experimental results are presented from the application of the technique to a campus site environment to demonstrate the viability, effectiveness, and robustness.

In most urban roads, and similar environments such as in the theme parks, campus sites, industrial estates, science parks and the like, the painted lane markings that exist may not be easily discernible by CCD cameras due to poor lighting, bad weather conditions and inadequate maintenance. An important feature of roads in such environments is the existence of pavements or curbs on either side defining the road boundaries. These curbs, which are mostly parallel to the road, can be harnessed to extract useful features of the road for implementing autonomous navigation or driver assistance systems. However, extraction of the curb or road edge feature using vision image data is a difficult task as curbs are not conspicuous in the vision image. To extract the curb from a camera image requires extensive image processing, heuristics and very favorable lighting. In our approach, road curbs are extracted speedily using range data provided by a 2D laser measurement system (LMS). Experimental results are presented to demonstrate the viability, and effectiveness, of the proposed methodology and its robustness to different obstacle, weather and lighting conditions.

In most urban roads, and similar environments such as in theme parks, campus sites, industrial estates, science parks and the like, the painted lane markings that exist may not be easily discernible by CCD cameras due to poor lighting, bad weather conditions, and inadequate maintenance. An important feature of roads in such environments is the existence of pavements or curbs on either side defining the road boundaries. These curbs, which are mostly parallel to the road, can be harnessed to extract useful features of the road for implementing autonomous navigation or driver assistance systems. However, extraction of the curb or road edge feature using vision image data is a very formidable task as the curb is not conspicuous in the vision image. To extract the curb using vision data requires extensive image processing, heuristics and very favourable ambient lighting. In our approach, the curb data is extracted speedily using range data provided by a 2D laser range measurement device. This information is then used to extract the mid-line(s) in the vision image using an extended Kalman filtering (EKF) approach. Subsequently midline data is used for the prediction of the road boundaries. Experimental results are presented to demonstrate the viability, and effectiveness, of the proposed methodology.

In most urban roads and similar environments, such as in theme parks, campus sites, industrial estates, science parks and the like, the painted lane markings that exist may not be easily discernible by CCD cameras due to poor lighting, bad weather conditions, and inadequate maintenance. An important feature of roads in such environments is the existence of pavements or curbs on either side defining the road boundaries. These curbs, which are mostly parallel to the road, can be harnessed to extract useful features of the road for implementing autonomous navigation or driver assistance systems. However, extraction of the curb or road edge feature using vision image data is a very formidable task as the curb is not conspicuous in the vision image. To extract the curb using vision data requires extensive image processing, heuristics and very favorable ambient lighting. In our approach, road curbs are extracted speedily using the range data provided by a 2D laser range measurement system (LMS). Experimental results are presented to demonstrate the viability and effectiveness of the proposed methodology and its robustness to different road configurations including road intersections.

In this paper, a sensor fusion technique is proposed for autonomous underwater vehicles (AUV) to track underwater cables. The paper discusses the navigation of AUV when cable is invisible in the image, and the selection of the correct cable (interested feature) when there are many similar features appearing in the image. The proposed sensor fusion scheme uses deadreckoning position uncertainty with a 2D position model of the cable to predict the region of interest in the image. This reduces the processing data increasing processing speed and avoids tracking other similar features appearing in the image. The proposed method uses a 2D position model of the cable for AUV navigation when the cable features are invisible in the predicted region. An experiment is conducted to test the performance of the proposed system using the AUV "Twin-Burger 2". The experimental results presented in this paper shows how the proposed method handles the above mentioned practical problems.

A sensor fusion technique is proposed for autonomous underwater vehicles (AUVs) to track underwater cables. The focus of this paper is to solve the two practical problems encountered in optical vision based systems in underwater environments: (1) navigation of AUV when cable is invisible in the image; and (2) selection of the correct cable when there are many similar features appearing in the image. The proposed sensor fusion scheme uses the dead reckoning position uncertainty with a 2D position model of the cable to predict the region of interest in the image. This reduces the processing data, increases the processing speed and avoids tracking other similar features appearing in the optical image. The proposed method uses a priori map of the cable for AUV navigation when the cable features are invisible in the predicted region in the image. An experiment was conducted to test the performance of the proposed algorithm using the AUV "Twin-Burger 2". The experimental results obtained show how the proposed method handles the above-mentioned practical problems.

This paper presents a methodology for extracting road edge and lane information for smart and intelligent navigation of vehicles. The range information provided by a fast laser range-measuring device is processed by an extended Kalman filter to extract the road edge or curb information. The resultant road edge information is used to aid in the extraction of the lane boundary from a CCD camera image. The Hough transform is used to extract the candidate of lane boundary edges, and the most probable lane boundary is determined by using an active line model and minimizing an appropriate energy function. Experimental results are presented to demonstrate the effectiveness of the combined laser and vision strategy for road-edge and lane boundary detection.

In this paper, a sensor fusion technique is proposed for autonomous underwater vehicles (AUV) to track underwater cables. The focus of this paper is to solve the two practical problems encountered in optical vision based systems in underwater environments; namely 1) navigation of AUV when cable is invisible in the image, and 2) selection of the correct cable (interested feature) when there are many similar features appearing in the image. The proposed sensor fusion scheme uses dead reckoning position uncertainty with a 2D position model of the cable to predict the region of interest in the image. This reduces the processing data increasing the processing speed and avoids tracking other similar features appearing in the optical image. The proposed method uses a priori map of the cable for AUV navigation when the cable features are invisible in the predicted region in the image. An experiment is conducted to test the performance of the proposed algorithm using the AUV "Twin-Burger 2". The experimental results presented in this paper show how the proposed method handles the above-mentioned practical problems.

In this paper, a sensor fusion technique is proposed for autonomous underwater vehicles (AUV) to track underwater cables. The work presented here is an extension of the authors' previously proposed vision based cable tracking system (1997, 1998). The focus of this paper is to solve the two practical problems encountered in vision based systems; namely (1) navigation of AUV when cable is invisible in the image, and (2) selection of the correct cable (interested feature) when there are many similar features appearing in the image. The proposed sensor fusion scheme uses deadreckoning position uncertainty with a 2D position model of the cable to predict the region of interest in the image. This reduces the processing data increasing processing speed and avoids tracking other similar features appearing in the image. The proposed method uses a 2D position model of the cable for AUV navigation when the cable features are invisible in the predicted region. An experiment is conducted to test the performance of the proposed system using the AUV "Twin-Burger 2". The experimental results presented in this paper shows how the proposed method handles the above mentioned practical problems.

A vision based system is developed for interaction between the man-robot unit and robot-robot unit in underwater environments. Vision is the communicating medium between the agents. The image consists of an ON/OFF light pattern produced by a set of electro-luminescent panels, representing a particular command. The vision system introduced in this paper recognizes these patterns under the defined environmental conditions. In the image processing system the Hough transform is performed with the Sobel operator to extract important features of the image. The decision making time of this system is approximately 1 sec on a transputer based hardware, which is equivalent to the CPU system on the testbed robot, "The Twin-Burger". This decision making time is acceptable for slowly moving underwater robots. In order to track the region of interest, the vision system calculates the pan and tilt angles of the CCD camera. The system is tested for different situations underwater as well as in air.