Arrow Research search

Author name cluster

Lenka Mudrová

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

3 papers
1 author row

Possible papers

3

ECAI Conference 2016 Conference Paper

Partial Order Temporal Plan Merging for Mobile Robot Tasks

  • Lenka Mudrová
  • Bruno Lacerda
  • Nick Hawes

For many mobile service robot applications, planning problems are based on deciding how and when to navigate to certain locations and execute certain tasks. Typically, many of these tasks are independent from one another, and the main objective is to obtain plans that efficiently take into account where these tasks can be executed and when execution is allowed. In this paper, we present an approach, based on merging of partial order plans with durative actions, that can quickly and effectively generate a plan for a set of independent goals. This plan exploits some of the synergies of the plans for each single task, such as common locations where certain actions should be executed. We evaluate our approach in benchmarking domains, comparing it with state-of-the-art planners and showing how it provides a good trade-off between the approach of sequencing the plans for each task (which is fast but produces poor results), and the approach of planning for a conjunction of all the goals (which is slow but produces good results).

ICRA Conference 2015 Conference Paper

Task scheduling for mobile robots using interval algebra

  • Lenka Mudrová
  • Nick Hawes

We present a novel task scheduling algorithm for use on mobile robots in real environments. The scheduling problem is formalised as mixed integer program, which is a standard approach in the scheduling community. Our contribution is the use of Allen's interval algebra to prune the search to be performed by the mixed integer program. This significantly speeds up the algorithm. The proposed algorithm has been used on several mobile robots in long-term autonomy scenarios, where it schedules large sets containing a variety of tasks. The proposed algorithm outperforms the state of the art by at least one order of magnitude on both these real tasks and synthetic datasets.

ICRA Conference 2015 Conference Paper

Where's waldo at time t? using spatio-temporal models for mobile robot search

  • Tomás Krajník
  • Miroslav Kulich
  • Lenka Mudrová
  • Rares Ambrus
  • Tom Duckett

We present a novel approach to mobile robot search for non-stationary objects in partially known environments. We formulate the search as a path planning problem in an environment where the probability of object occurrences at particular locations is a function of time. We propose to explicitly model the dynamics of the object occurrences by their frequency spectra. Using this spectral model, our path planning algorithm can construct plans that reflect the likelihoods of object locations at the time the search is performed. Three datasets collected over several months containing person and object occurrences in residential and office environments were chosen to evaluate the approach. Several types of spatio-temporal models were created for each of these datasets and the efficiency of the search method was assessed by measuring the time it took to locate a particular object. The results indicate that modeling the dynamics of object occurrences reduces the search time by 25% to 65% compared to maps that neglect these dynamics.

v2026.09.13