Arrow Research search

Author name cluster

Jacques Jacot

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.

2 papers
1 author row

Possible papers

2

IROS Conference 2008 Conference Paper

SwisTrack - a flexible open source tracking software for multi-agent systems

  • Thomas Lochmatter
  • Pierre Roduit
  • Christopher M. Cianci
  • Nikolaus Correll
  • Jacques Jacot
  • Alcherio Martinoli

Vision-based tracking is used in nearly all robotic laboratories for monitoring and extracting of agent positions, orientations, and trajectories. However, there is currently no accepted standard software solution available, so many research groups resort to developing and using their own custom software. In this paper, we present Version 4 of SwisTrack, an open source project for simultaneous tracking of multiple agents. While its broad range of pre-implemented algorithmic components allows it to be used in a variety of experimental applications, its novelty stands in its highly modular architecture. Advanced users can therefore also implement additional customized modules which extend the functionality of the existing components within the provided interface. This paper introduces SwisTrack and shows experiments with both marked and marker-less agents.

Details

IROS Conference 2007 Conference Paper

A quantitative method for comparing trajectories of mobile robots using point distribution models

  • Pierre Roduit
  • Alcherio Martinoli
  • Jacques Jacot

In the field of mobile robotics, trajectory details are seldom taken into account to qualify robot performance. Most metrics rely mainly on global results such as the total time needed or distance traveled to accomplish a given navigational task. Indeed, usually mobile roboticists assume that, by using appropriate navigation techniques, they can design controllers so that the error between the actual and the ideal trajectory can be maintained within prescribed bounds. This assumption indirectly implies that there is no interesting information to be extracted by comparing trajectories if their variation is essentially resulting from uncontrolled noisy factors. In this paper, we will instead show that analyzing and comparing resulting trajectories is useful for a number of reasons, including model design, system optimization, system performance, and repeatability. In particular, we will describe a trajectory analysis method based on point distribution models (PDMs). The applicability of this method is demonstrated on the trajectories of a real differential- drive robot, endowed with two different controllers leading to different patterns of motion. Results demonstrate that in the space of the PDM, the difference between the two controllers is easily quantifiable. This method appears also to be extremely useful for comparing real trajectories with simulated ones for the same set-up since it affords an assessment of the simulation faithfulness before and after appropriate tuning of simulation features.

Details
v2026.09.13