36 research outputs found
Advanced perception, navigation and planning for autonomous in-water ship hull inspection
Inspection of ship hulls and marine structures using autonomous underwater vehicles has emerged as a unique and challenging application of robotics. The problem poses rich questions in physical design and operation, perception and navigation, and planning, driven by difficulties arising from the acoustic environment, poor water quality and the highly complex structures to be inspected. In this paper, we develop and apply algorithms for the central navigation and planning problems on ship hulls. These divide into two classes, suitable for the open, forward parts of a typical monohull, and for the complex areas around the shafting, propellers and rudders. On the open hull, we have integrated acoustic and visual mapping processes to achieve closed-loop control relative to features such as weld-lines and biofouling. In the complex area, we implemented new large-scale planning routines so as to achieve full imaging coverage of all the structures, at a high resolution. We demonstrate our approaches in recent operations on naval ships.United States. Office of Naval Research (Grant N00014-06-10043)United States. Office of Naval Research (Grant N00014-07-1-0791
Actively Mapping Industrial Structures with Information Gain-Based Planning on a Quadruped Robot
In this paper, we develop an online active mapping system to enable a
quadruped robot to autonomously survey large physical structures. We describe
the perception, planning and control modules needed to scan and reconstruct an
object of interest, without requiring a prior model. The system builds a voxel
representation of the object, and iteratively determines the Next-Best-View
(NBV) to extend the representation, according to both the reconstruction itself
and to avoid collisions with the environment. By computing the expected
information gain of a set of candidate scan locations sampled on the as-sensed
terrain map, as well as the cost of reaching these candidates, the robot
decides the NBV for further exploration. The robot plans an optimal path
towards the NBV, avoiding obstacles and un-traversable terrain. Experimental
results on both simulated and real-world environments show the capability and
efficiency of our system. Finally we present a full system demonstration on the
real robot, the ANYbotics ANYmal, autonomously reconstructing a building facade
and an industrial structure
Online Informative Path Planning for Active Information Gathering of a 3D Surface
This paper presents an online informative path planning approach for active
information gathering on three-dimensional surfaces using aerial robots. Most
existing works on surface inspection focus on planning a path offline that can
provide full coverage of the surface, which inherently assumes the surface
information is uniformly distributed hence ignoring potential spatial
correlations of the information field. In this paper, we utilize manifold
Gaussian processes (mGPs) with geodesic kernel functions for mapping surface
information fields and plan informative paths online in a receding horizon
manner. Our approach actively plans information-gathering paths based on recent
observations that respect dynamic constraints of the vehicle and a total flight
time budget. We provide planning results for simulated temperature modeling for
simple and complex 3D surface geometries (a cylinder and an aircraft model). We
demonstrate that our informative planning method outperforms traditional
approaches such as 3D coverage planning and random exploration, both in
reconstruction error and information-theoretic metrics. We also show that by
taking spatial correlations of the information field into planning using mGPs,
the information gathering efficiency is significantly improved.Comment: 7 pages, 7 figures, to be published in 2021 IEEE International
Conference on Robotics and Automation (ICRA
Decentralized cooperative trajectory estimation for autonomous underwater vehicles
Autonomous agents that can communicate and make relative measurements of each other can improve their collective localization accuracies. This is referred to as cooperative localization (CL). Autonomous underwater vehicle (AUV) CL is constrained by the low throughput, high latency, and unreliability of of the acoustic channel used to communicate when submerged. Here we propose a CL algorithm specifically designed for full trajectory, or maximum a posteriori, estimation for AUVs. The method is exact and has the advantage that the broadcast packet sizes increase only linearly with the number of AUVs in the collective and do not grow at all in the case of packet loss. The approach allows for AUV missions to be achieved more efficiently since: 1) vehicles waste less time surfacing for GPS fixes, and 2) payload data is more accurately localized through the smoothing approach.Natural Sciences and Engineering Research Council of CanadaDefense Research and Development CanadaUnited States. Office of Naval Research (Grant N00014-13-1-0588
Autonomous surveillance for biosecurity
The global movement of people and goods has increased the risk of biosecurity
threats and their potential to incur large economic, social, and environmental
costs. Conventional manual biosecurity surveillance methods are limited by
their scalability in space and time. This article focuses on autonomous
surveillance systems, comprising sensor networks, robots, and intelligent
algorithms, and their applicability to biosecurity threats. We discuss the
spatial and temporal attributes of autonomous surveillance technologies and map
them to three broad categories of biosecurity threat: (i) vector-borne
diseases; (ii) plant pests; and (iii) aquatic pests. Our discussion reveals a
broad range of opportunities to serve biosecurity needs through autonomous
surveillance.Comment: 26 pages, Trends in Biotechnology, 3 March 2015, ISSN 0167-7799,
http://dx.doi.org/10.1016/j.tibtech.2015.01.003.
(http://www.sciencedirect.com/science/article/pii/S0167779915000190
Communication-constrained multi-AUV cooperative SLAM
Multi-robot deployments have the potential for completing tasks more efficiently. For example, in simultaneous localization and mapping (SLAM), robots can better localize themselves and the map if they can share measurements of each other (direct encounters) and of commonly observed parts of the map (indirect encounters). However, performance is contingent on the quality of the communications channel. In the underwater scenario, communicating over any appreciable distance is achieved using acoustics which is low-bandwidth, slow, and unreliable, making cooperative operations very challenging. In this paper, we present a framework for cooperative SLAM (C-SLAM) for multiple autonomous underwater vehicles (AUVs) communicating only through acoustics. We develop a novel graph-based C-SLAM algorithm that is able to (optimally) generate communication packets whose size scales linearly with the number of observed features since the last successful transmission, constantly with the number of vehicles in the collective, and does not grow with time even the case of dropped packets, which are common. As a result, AUVs can bound their localization error without the need for pre-installed beacons or surfacing for GPS fixes during navigation, leading to significant reduction in time required to complete missions. The proposed algorithm is validated through realistic marine vehicle and acoustic communication simulations.United States. Office of Naval Research (Grant N00014-13-1-0588)National Science Foundation (U.S.) (Award IIS-1318392)United States. Office of Naval Research Globa