Cooperative Localization for Autonomous Underwater Vehicles

The absence of GPS underwater makes navigation for Autonomous Underwater Vehicles (AUVs) a difficult challenge. Without an external reference in the form of acoustic beacons at known positions, the vehicle has to rely on proprioceptive information obtained through a compass, a Doppler Velocity Logger (DVL) or an Inertial Navigation System (INS). Independent of the quality of the sensors used, the error in the position estimate based on dead-reckoning information grows without bound. Typical navigation errors are 0.5% to 2% of distance traveled for vehicles traveling within a few hundred meters of the sea floor such that their DVL has a lock on the bottom. Errors as low as 0.1% can be obtained with large and expensive INS systems, but for vehicles relying only on a compass and a speed estimate can be as high as 10%. By surfacing the AUV can obtain a position update through its GPS, but this is impossible (under ice) or undesirable for many applications. The use of static beacons in the form of a Long Baseline (LBL) array limits the operation area to a few km2 and requires a substantial deployment effort before operations, especially in deep water. As underwater vehicles become more reliable and affordable the simultaneous use of several AUVs recently became a viable option and multi-vehicle deployments will become standard in the upcoming years. This will not only make entirely new types of missions which rely on cooperation possible, but will also allow each individual member of the group to benefit from navigation information obtained from other members. For optimal cooperative localization a few dedicated Communication and Navigation Aid-AUVs (CNAs), which maintain an accurate estimate of their position through sophisticated DVL and INS sensors, can enable a much larger group of vehicles with less sophisticated navigation suites to maintain an accurate position

Continuous Humanoid Locomotion over Uneven Terrain using Stereo Fusion

Abstract — For humanoid robots to fulfill their mobility po-tential they must demonstrate reliable and efficient locomotion over rugged and irregular terrain. In this paper we present the perception and planning algorithms which have allowed a humanoid robot to use only passive stereo imagery (as opposed to actuating a laser range sensor) to safely plan footsteps to continuously walk over rough and uneven surfaces without stopping. The perception system continuously integrates stereo imagery to build a consistent 3D model of the terrain which is then used by our footstep planner which reasons about obstacle avoidance, kinematic reachability and foot rotation through mixed-integer quadratic optimization to plan the required step positions. We illustrate that our stereo imagery fusion approach can measure the walking terrain with sufficient accuracy that it matches the quality of terrain estimates from LIDAR. To our knowledge this is the first such demonstration of the use of computer vision to carry out general purpose terrain estimation on a locomoting robot — and additionally to do so in continuous motion. A particular integration challenge was ensuring that these two computationally intensive systems oper-ate with minimal latency (below 1 second) to allow re-planning while walking. The results of extensive experimentation and quantitative analysis are also presented. Our results indicate that a laser range sensor is not necessary to achieve locomotion in these challenging situations. I

Cooperative AUV Navigation using a Single Maneuvering Surface Craft

In this paper we describe the experimental implementation of an online algorithm for cooperative localization of submerged autonomous underwater vehicles (AUVs) supported by an autonomous surface craft. Maintaining accurate localization of an AUV is difficult because electronic signals, such as GPS, are highly attenuated by water. The usual solution to the problem is to utilize expensive navigation sensors to slow the rate of dead-reckoning divergence. We investigate an alternative approach that utilizes the position information of a surface vehicle to bound the error and uncertainty of the on-board position estimates of a low-cost AUV. This approach uses the Woods Hole Oceanographic Institution (WHOI) acoustic modem to exchange vehicle location estimates while simultaneously estimating inter-vehicle range. A study of the system observability is presented so as to motivate both the choice of filtering approach and surface vehicle path planning. The first contribution of this paper is to the presentation of an experiment in which an extended Kalman filter (EKF) implementation of the concept ran online on-board an OceanServer Iver2 AUV while supported by an autonomous surface vehicle moving adaptively. The second contribution of this paper is to provide a quantitative performance comparison of three estimators: particle filtering (PF), non-linear least-squares optimization (NLS), and the EKF for a mission using three autonomous surface craft (two operating in the AUV role). Our results indicate that the PF and NLS estimators outperform the EKF, with NLS providing the best performance.United States. Office of Naval Research (Grant N000140711102)United States. Office of Naval Research. Multidisciplinary University Research InitiativeSingapore. National Research FoundationSingapore-MIT Alliance for Research and Technology. Center for Environmental Sensing and Monitorin

Simultaneous Localization and Mapping in Marine Environments

Accurate navigation is a fundamental requirement for robotic systems—marine and terrestrial. For an intelligent autonomous system to interact effectively and safely with its environment, it needs to accurately perceive its surroundings. While traditional dead-reckoning filtering can achieve extremely low drift rates, the localization accuracy decays monotonically with distance traveled. Other approaches (such as external beacons) can help; nonetheless, the typical prerogative is to remain at a safe distance and to avoid engaging with the environment. In this chapter we discuss alternative approaches which utilize onboard sensors so that the robot can estimate the location of sensed objects and use these observations to improve its own navigation as well as its perception of the environment. This approach allows for meaningful interaction and autonomy. Three motivating autonomous underwater vehicle (AUV) applications are outlined herein. The first fuses external range sensing with relative sonar measurements. The second application localizes relative to a prior map so as to revisit a specific feature, while the third builds an accurate model of an underwater structure which is consistent and complete. In particular we demonstrate that each approach can be abstracted to a core problem of incremental estimation within a sparse graph of the AUV’s trajectory and the locations of features of interest which can be updated and optimized in real time on board the AUV.QC 20150326</p