activity
20162021
most citedMethods for Stochastic Collection and Replenishment (SCAR) optimisation for persistent autonomy

18 citations · 58 across the 9 of their papers we have counts for

collaborators

9 papers

math.OC2021★ 1 cited

Computationally Efficient Dynamic Traffic Optimization Of Railway Systems

Robin Vujanic, Andrew Hill

In this paper we investigate real-time, dynamic traffic optimization in railway systems. In order to enable practical solution times, we operate the optimizer in a receding horizon…

cs.RO2018

Receding horizon estimation and control with structured noise blocking for mobile robot slip compensation

Nathan Wallace, He Kong, Andrew Hill +1

The control of field robots in varying and uncertain terrain conditions presents a challenge for autonomous navigation. Online estimation of the wheel-terrain slip characteristics…

cs.RO2018

Multi-Vehicle Trajectory Optimisation On Road Networks

Philip Gun, Andrew Hill, Robin Vujanic

This paper addresses the problem of planning time-optimal trajectories for multiple cooperative agents along specified paths through a static road network. Vehicle interactions at…

cs.NE2016★ 1 cited

Weekly maintenance scheduling using exact and genetic methods

Andrew W. Palmer, Robin Vujanic, Andrew J. Hill +1

The weekly maintenance schedule specifies when maintenance activities should be performed on the equipment, taking into account the availability of workers and maintenance bays, an…

cs.MA2016★ 17 cited

Modelling resource contention in multi-robot task allocation problems with uncertain timing

Andrew W. Palmer, Andrew J. Hill, Steven J. Scheding

This paper proposes an analytical framework for modelling resource contention in multi-robot systems, where the travel times and task durations are uncertain. It uses several appro…

eess.SY2016★ 11 cited

Applying Gaussian distributed constraints to Gaussian distributed variables

Andrew W. Palmer, Andrew J. Hill, Steven J. Scheding

This paper develops an analytical method of truncating inequality constrained Gaussian distributed variables where the constraints are themselves described by Gaussian distribution…