Project reports

You can find in this category all the public deliverables of the CrowdBot project that have been published so far.

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Deliverable 3.4: Reactive Motion Planning

This report details the reactive navigation techniques developed for the CrowdBot project between months M1 to M30. In this sense, we have investigated three main technical components for achieving reactivity in different types of mobile or service robots when navigating in crowded environments.Each of the three technical components are designed to complement high-level planning techniques

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Deliverable 3.5: Social Navigation

This task brings socially aware navigation strategies on the commercial platform Pepper. Special focus was put on the factors: Safety: No physical harm.  Comfort: Absence of annoyance and stress for humans. Naturalness: Similarity between robots and human’s behaviour patterns. Sociability: Adherence to explicit high-level socio-cultural conventions. This deliverable reports on the developments made by Softbank

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Deliverable 3.2: Robust Localization and Mapping

This report details the robust localization and mapping algorithms developed for the Crowdbot project between months M1 and M30. Our proposed solutions are designed with the explicit goal of achieving robot navigation in crowded environments, where many existing methods struggle due to the high degree of dynamic motion around the robot. This report primarily serves

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Deliverable 3.3: Local Interaction Aware Motion Planning

State-of-the-art approaches for robot navigation among humans are typically restricted to planar movement actions. This work addresses the question of whether it can be beneficial to use interaction actions, such as saying, touching, and gesturing, for the sake of allowing robots to navigate in unstructured, crowded environments. To do so, we first identify challenging scenarios

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Deliverable 7.3: CrowdBot Challenge

This deliverable introduces the CrowdBot Challenge and presents its level of advancement before launching the first round. This document first presents the objective of the challenge, which is, in brief, to allow teams working in the field of crowd robot navigation to evaluate their navigation techniques and to compare their methods for moving robots in

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Deliverable 5.3: 2nd Updated and Extended Robot System

Work package 5 (WP5) is about developing a coherent theoretical and functional system architecture that can accommodate the targeted scenarios and facilitate the integration of the different work packages across all three robotic platforms. However, this process must be iterative and reviewable after each milestone so that the achievement of such objectives is guaranteed during

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Deliverable 1.3: Specification of Scenarios Requirement Update

Deliverable D1.3 is an updated version of D1.1. To enable the reader to have a complete understanding of the document without need to refer to previous ones, some existing portions have been reported here, even though they did not experiment a major update. To clarify things, for each section, a similar orange text box provides

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Deliverable 1.4: 1st Round Test Evaluation Report

In this deliverable we present the results of the first-round evaluations of all our crowdbots (Pepper, the smart wheelchair, cuyBot and Qolo). The evaluations are both quantitative and qualitative. They range from testing the efficacy of core components that are developed within the project and underpin the Crowdbots (e.g. sensing, localisation, simulation, planning), to fully

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Deliverable 2.2: Local Sensing 1st Prototype

In this report we present our first prototype of the perception pipeline developed for the CROWDBOT project. Currently, the focus of the perception pipeline is on detecting and tracking pedestrians in low to medium density scenarios using RGB-D cameras and 2D LiDAR sensors. We begin with reviewing the major detection and tracking methods used in

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Deliverable 4.2: Crowd Simulator – Intermediate Version

The CrowdBot project’s crowd simulators perform two essential roles for the safe navigation of robots in populated environments: Crowd simulation for short-term prediction of the evolution of the situation of people in the vicinity of the robot. Crowd simulation for testing and evaluating the navigation functions of a robot in a densely populated environment. This

Inria
EPFL
ETH Zürich
RWTH
Softbank Robotics
UCL
Locomotec
European Union

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