Machine Vision Techniques for Situational Awareness and Path Planning in Model Test Basin Ice-Covered Waters

Gash, Robert; Murrant, Kevin; Mills, Jason; Millan, D.

doi:10.1109/ieeeconf38699.2020.9389165

Cited by 5 publications

(6 citation statements)

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“…Two baseline planning algorithms were considered which we refer to as straight and skeleton. The former is simply a planner that returns a constant straight path from the ship's current position to the goal G and the latter refers to the shortest open-water path routing approach described in [16] and [4]. Their approach constructs morphological skeletons based on the ice environment to generate paths.…”

Section: B Simulation Resultsmentioning

confidence: 99%

“…They then employ A* to compute a path in the resulting graph. The techniques proposed in [15], [16] empirically work well in low-concentration ice fields. However, planned paths are piece-wise straight lines and do not take into account the ASVs' dynamics.…”

Section: Introductionmentioning

confidence: 93%

“…We validated our proposed approach by integrating our navigation framework with the NRC ice tank facility shown in Figure 1, which is complete with a model vessel and realtime overhead vision system [16].…”

Section: Details On Experimental Platformmentioning

confidence: 99%

“…Vision System: The facility contains 20 ceiling cameras sending ice information at a frequency of 1 Hz. The ASV configuration is computed at 50 Hz using the tracking system and an on-board inertial measurement unit (IMU) [4], [16]. Given the two update rates, we set ∆t = 1 in Algorithm 1.…”

Section: Details On Experimental Platformmentioning

confidence: 99%

“…Local path planning is considered in [15] where the authors propose a motion planner using bidirectional RRT and data gathered from marine radar imaging. Similarly, in [16] the authors construct a graph using a morphological skeleton from a post processed overhead snapshot of an ice field. They then employ A* to compute a path in the resulting graph.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Real-Time Navigation for Autonomous Surface Vehicles In Ice-Covered Waters

Botros¹,

Gash²,

Murrant³

et al. 2023

Preprint

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Vessel transit in ice-covered waters poses unique challenges in safe and efficient motion planning. When the concentration of ice is high, it may not be possible to find collision-free trajectories. Instead, ice can be pushed out of the way if it is small or if contact occurs near the edge of the ice. In this work, we propose a real-time navigation framework that minimizes collisions with ice and distance travelled by the vessel. We exploit a lattice-based planner with a cost that captures the ship interaction with ice. To address the dynamic nature of the environment, we plan motion in a receding horizon manner based on updated vessel and ice state information. Further, we present a novel planning heuristic for evaluating the cost-to-go, which is applicable to navigation in a channel without a fixed goal location. The performance of our planner is evaluated across several levels of ice concentration both in simulated and in real-world experiments.

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Section: B Simulation Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 93%

Section: Details On Experimental Platformmentioning

confidence: 99%

Section: Details On Experimental Platformmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Real-Time Navigation for Autonomous Surface Vehicles In Ice-Covered Waters

Botros¹,

Gash²,

Murrant³

et al. 2023

Preprint

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Real-Time Navigation for Autonomous Surface Vehicles In Ice-Covered Waters

Botros

Gash

Murrant

et al. 2023

2023 IEEE International Conference on Robotics and Automation (ICRA)

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Exploring the Impact of Immersion on Situational Awareness and Trust in Remotely Monitored Maritime Autonomous Surface Ships

Gregor,

Allison,

Heffner

2023

OCEANS 2023 - Limerick

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This thesis examines how Situational Awareness (SA) and Trust, along with some exploratory variables, were affected by different immersion levels in maritime remote monitoring. To examine this a simulated Shore Control Centre (SCC) interface for Maritime Autonomous Surface Ships (MASS) was constructed, which had an autonomous container ship traversing the arctic with robotic aids. Three query sets were asked per simulation run, which facilitated tracking how SA, Trust, and Motion Sickness (MS) evolved over time. Three different virtual reality (VR) interfaces were used; Non-Immersive VR (NVR), Semi-immersive VR (SVR), and Immersive VR (IVR). The simulation and query sets were performed on a counterbalanced within-subjects user study with 39 participants. The results illustrated various trade-offs -with NVR showing higher user preference, SVR showing signs of higher SA, and IVR showing moderate Trust but increased MS. Understanding these trade-offs between immersion levels is a requisite step for designing future SCCs.

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Machine Vision Techniques for Situational Awareness and Path Planning in Model Test Basin Ice-Covered Waters

Cited by 5 publications

References 0 publications

Real-Time Navigation for Autonomous Surface Vehicles In Ice-Covered Waters

Real-Time Navigation for Autonomous Surface Vehicles In Ice-Covered Waters

Real-Time Navigation for Autonomous Surface Vehicles In Ice-Covered Waters

Exploring the Impact of Immersion on Situational Awareness and Trust in Remotely Monitored Maritime Autonomous Surface Ships

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