Stereo vision–based navigation for autonomous surface vessels

Huntsberger, Terrance L.; Aghazarian, Hrand; Howard, Andrew; Trotz, David C.

doi:10.1002/rob.20380

Cited by 109 publications

(52 citation statements)

References 16 publications

(15 reference statements)

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“…Larger ships are expected to carry an automatic identification system (AIS) transmitting radio signals containing position and other information about the ship, that can be received by other ships and authorities. In order to be able to detect the wide range of potential obstacles, onboard sensors such as radar, LIDAR and camera can be used to scan the environment of the ship, [5], [6], [7].…”

Section: A Backgroundmentioning

confidence: 99%

See 1 more Smart Citation

Ship Collision Avoidance and COLREGS Compliance Using Simulation-Based Control Behavior Selection With Predictive Hazard Assessment

Johansen

Pérez

Cristofaro

2016

IEEE Trans. Intell. Transport. Syst.

259

224

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Abstract-This paper describes a concept for a collision avoidance system for ships, based on model predictive control. A finite set of alternative control behaviors are generated by varying two parameters: offsets to the guidance course angle commanded to the autopilot, and changes to the propulsion command ranging from nominal speed to full reverse. Using simulated predictions of the trajectories of the obstacles and ship, the compliance with COLREGS and collision hazards associated with each of the alternative control behaviors are evaluated on a finite prediction horizon, and the optimal control behavior is selected. Robustness to sensing error, predicted obstacle behavior, and environmental conditions can be ensured by evaluating multiple scenarios for each control behavior. The method is conceptually and computationally simple and yet quite versatile as it can account for the dynamics of the ship, the dynamics of the steering and propulsion system, forces due to wind and ocean current, and any number of obstacles. Simulations show that the method is effective and can manage complex scenarios with multiple dynamic obstacles and uncertainty associated with sensors and predictions.

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Section: A Backgroundmentioning

confidence: 99%

“…A detailed description and survey of such systems is beyond the scope of the paper, and we refer to [26], [5], [6], [7] as well as recent results from automotive industry [27], [28].…”

Section: Contributionsmentioning

confidence: 99%

Ship Collision Avoidance and COLREGS Compliance Using Simulation-Based Control Behavior Selection With Predictive Hazard Assessment

Johansen

Pérez

Cristofaro

2016

IEEE Trans. Intell. Transport. Syst.

259

224

View full text Add to dashboard Cite

show abstract

“…Some outstanding examples include environmental and remote monitoring [10], support to search & rescue operations [26], humanitarian demining [31], patrol and reconnaissance [17], and agriculture [18].…”

Section: Related Workmentioning

confidence: 99%

On Exploiting Haptic Cues for Self-Supervised Learning of Depth-Based Robot Navigation Affordances

Baleia

Santana

Barata

2015

J Intell Robot Syst

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This article presents a method for online learning of robot navigation a↵or-dances from spatiotemporally correlated haptic and depth cues. The method allows the robot to incrementally learn which objects present in the environment are actually traversable. This is a critical requirement for any wheeled robot performing in natural environments, in which the inability to discern vegetation from non-traversable obstacles frequently hampers terrain progression. A wheeled robot prototype was developed in order to experimentally validate the proposed method. The robot prototype obtains haptic and depth sensory feedback from a pan-tilt telescopic antenna and from a structured light sensor, respectively. With the presented method, the robot learns a mapping between objects' descriptors, given the range data provided by the sensor, and objects' sti↵ness, as estimated from the interaction between the antenna and the object. Learning confidence estimation is considered in order to progressively reduce the number of required physical interactions with acquainted objects. To raise the number of meaningful interactions per object under time pressure, the several segments of the object under analysis are prioritised according to a set of morphological criteria. Field trials show the ability of the robot to progressively learn which elements of the environment are traversable.

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“…Therefore, a system for detecting and keeping track of obstacles near the planned path of the ship is required. On-board sensors such as a radar, LIDAR and camera can be used to detect objects in the environment of the ship [13][14][15]. However, in order to have a robust system it might not be sufficient to only place sensors on-board the ship.…”

Section: Introductionmentioning

confidence: 99%

Tracking of Ocean Surface Objects from Unmanned Aerial Vehicles with a Pan/Tilt Unit using a Thermal Camera

Helgesen

Leira

Fossen

et al. 2017

J Intell Robot Syst

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This paper presents four vision-based tracking system architectures for marine surface objects using a fixed-wing unmanned aerial vehicle (UAV) with a thermal camera mounted in a pan/tilt gimbal. The tracking systems estimate the position and velocity of an object in the North-East (NE) plane, and differ in how the measurement models are defined. The first tracking system measures the position and velocity of the target with georeferencing and optical flow. The states are estimated in a Kalman filter. A Kalman filter is also utilized in the second architecture, but only the georeferenced position is used as a measurement. A bearing-only measurement model is the foundation for the third tracking system, and because the measurement model is nonlinear, an extended Kalman filter is used for state estimation. The fourth tracking system extends the bearing-only tracking system to let navigation uncertainty in the UAV position affect the target estimates in a Schmidt-Kalman filter. All tracking architectures are evaluated on data gathered at a flight experiment near the Azores islands outside of Portugal. The results show that various marine vessels can be tracked quite accurately.

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Stereo vision–based navigation for autonomous surface vessels

Cited by 109 publications

References 16 publications

Ship Collision Avoidance and COLREGS Compliance Using Simulation-Based Control Behavior Selection With Predictive Hazard Assessment

Ship Collision Avoidance and COLREGS Compliance Using Simulation-Based Control Behavior Selection With Predictive Hazard Assessment

On Exploiting Haptic Cues for Self-Supervised Learning of Depth-Based Robot Navigation Affordances

Tracking of Ocean Surface Objects from Unmanned Aerial Vehicles with a Pan/Tilt Unit using a Thermal Camera

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