56 citations · 286 across the 24 of their papers we have counts for
25 papers
Sim-to-real transfer of active suspension control using deep reinforcement learning
Viktor Wiberg, Erik Wallin, Arvid Fälldin +4
We explore sim-to-real transfer of deep reinforcement learning controllers for a heavy vehicle with active suspensions designed for traversing rough terrain. While related research…
Learning multiobjective rough terrain traversability
Erik Wallin, Viktor Wiberg, Folke Vesterlund +3
We present a method that uses high-resolution topography data of rough terrain, and ground vehicle simulation, to predict traversability. Traversability is expressed as three indep…
Control of rough terrain vehicles using deep reinforcement learning
Viktor Wiberg, Erik Wallin, Martin Servin +1
We explore the potential to control terrain vehicles using deep reinforcement in scenarios where human operators and traditional control methods are inadequate. This letter present…
Simulation-Based Optimization of High-Performance Wheel Loading
Koji Aoshima, Martin Servin, Eddie Wadbro
Having smart and autonomous earthmoving in mind, we explore high-performance wheel loading in a simulated environment. This paper introduces a wheel loader simulator that combines…
Digital twins with distributed particle simulation for mine-to-mill material tracking
Martin Servin, Folke Vesterlund, Erik Wallin
Systems for transport and processing of granular media are challenging to analyse, operate and optimise. In the mining and mineral processing industries these systems are chains of…
Reinforcement Learning Control of a Forestry Crane Manipulator
Jennifer Andersson, Kenneth Bodin, Daniel Lindmark +2
Forestry machines are heavy vehicles performing complex manipulation tasks in unstructured production forest environments. Together with the complex dynamics of the on-board hydrau…