Robert Gash

dblp:341/1661 · DBLP profile ↗
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2ranked-venue papers
0as first author
2since 2021 · last 2025
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 1 · 1 since 2021Systems, architecture and hardware · 1 · 1 since 2021Applied, interdisciplinary, general and emerging computing · 1 · 1 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Artificial intelligence
2 papers
Motion planning and robot control · 89% Robot navigation and mapping · 11%

Topics — the 6 heaviest of 7, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Robotics › Motion planning and robot control
path planning
0.912025
AUTO-IceNav: A Local Navigation Strategy for Autonomous Surface Ships in Broken Ice Fields · IEEE Trans. Robotics 2025
Robotics › Motion planning and robot control
trajectory optimization
0.912025
AUTO-IceNav: A Local Navigation Strategy for Autonomous Surface Ships in Broken Ice Fields · IEEE Trans. Robotics 2025
Robotics › Motion planning and robot control › path planning
collision-free path planning
0.712023
Real-Time Navigation for Autonomous Surface Vehicles In Ice-Covered Waters · ICRA 2023
Robotics › Motion planning and robot control
motion planning
0.712023
Real-Time Navigation for Autonomous Surface Vehicles In Ice-Covered Waters · ICRA 2023
Robotics › Motion planning and robot control › motion planning › online motion planning
receding horizon planning
0.712023
Real-Time Navigation for Autonomous Surface Vehicles In Ice-Covered Waters · ICRA 2023
Robotics › Robot navigation and mapping
SLAM
0.212023
Real-Time Navigation for Autonomous Surface Vehicles In Ice-Covered Waters · ICRA 2023

Methods — techniques the papers use, named apart from their topics

receding horizon planning · 0.9optimization-based path improvement · 0.9lattice-based path planning · 0.9lattice-based planning · 0.7cost-to-go heuristic · 0.7
YearPublicationVenuePosition
2025 AUTO-IceNav: A Local Navigation Strategy for Autonomous Surface Ships in Broken Ice Fields
abstract
Ice conditions often require ships to reduce speed and deviate from their main course to avoid damage to the ship. In addition, broken ice fields are becoming the dominant ice conditions encountered in the Arctic, where the effects of collisions with ice are highly dependent on where contact occurs and on the particular features of the ice floes. In this paper, we present AUTO-IceNav, a framework for the autonomous navigation of ships operating in ice floe fields. Trajectories are computed in a receding-horizon manner, where we frequently replan given updated ice field data. During a planning step, we assume a nominal speed that is safe with respect to the current ice conditions, and compute a reference path. We formulate a novel cost function that minimizes the kinetic energy loss of the ship from ship-ice collisions and incorporate this cost as part of our lattice-based path planner. The solution computed by the lattice planning stage is then used as an initial guess in our proposed optimization-based improvement step, producing a locally optimal path. Extensive experiments were conducted both in simulation and in a physical testbed to validate our approach.
Rodrigue de Schaetzen, Alexander Botros, Ninghan Zhong, Kevin Murrant, Robert Gash, Stephen L. Smith 0001
IEEE Trans. Robotics5
2023 Real-Time Navigation for Autonomous Surface Vehicles In Ice-Covered Waters
abstract
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.
Rodrigue de Schaetzen, Alexander Botros, Robert Gash, Kevin Murrant, Stephen L. Smith 0001
ICRA3