Social robot navigation tasks: combining machine learning techniques and social force model
| Autor: | Anais Garrell, Oscar Serrano Gil, Alberto Sanfeliu |
|---|---|
| Přispěvatelé: | Ministerio de Economía y Competitividad (España), Ministerio de Ciencia, Innovación y Universidades (España), Agencia Estatal de Investigación (España), European Commission, Universitat Politècnica de Catalunya. Doctorat en Automàtica, Robòtica i Visió, Universitat Politècnica de Catalunya. Departament d'Enginyeria de Sistemes, Automàtica i Informàtica Industrial, Universitat Politècnica de Catalunya. VIS - Visió Artificial i Sistemes Intel·ligents |
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Social Force Model
Informàtica::Automàtica i control [Àrees temàtiques de la UPC] Computer science social robot navigation Social Interaction Social navigation TP1-1185 Machine learning computer.software_genre Biochemistry Article Analytical Chemistry Machine Learning Automation Reinforcement learning Humans Electrical and Electronic Engineering Set (psychology) Instrumentation Pedestrians Social robot business.industry Chemical technology Deep learning Robotics Social robot navigation Models Theoretical Reinforcement Learning Atomic and Molecular Physics and Optics Social force model Robot Artificial intelligence Automation [Classificació INSPEC] business computer Humanoid robot |
| Zdroj: | UPCommons. Portal del coneixement obert de la UPC Universitat Politècnica de Catalunya (UPC) Sensors Volume 21 Issue 21 Sensors (Basel, Switzerland) Digital.CSIC. Repositorio Institucional del CSIC instname Sensors, Vol 21, Iss 7087, p 7087 (2021) |
| Popis: | Social robot navigation in public spaces, buildings or private houses is a difficult problem that is not well solved due to environmental constraints (buildings, static objects etc.), pedestrians and other mobile vehicles. Moreover, robots have to move in a human-aware manner—that is, robots have to navigate in such a way that people feel safe and comfortable. In this work, we present two navigation tasks, social robot navigation and robot accompaniment, which combine machine learning techniques with the Social Force Model (SFM) allowing human-aware social navigation. The robots in both approaches use data from different sensors to capture the environment knowledge as well as information from pedestrian motion. The two navigation tasks make use of the SFM, which is a general framework in which human motion behaviors can be expressed through a set of functions depending on the pedestrians’ relative and absolute positions and velocities. Additionally, in both social navigation tasks, the robot’s motion behavior is learned using machine learning techniques: in the first case using supervised deep learning techniques and, in the second case, using Reinforcement Learning (RL). The machine learning techniques are combined with the SFM to create navigation models that behave in a social manner when the robot is navigating in an environment with pedestrians or accompanying a person. The validation of the systems was performed with a large set of simulations and real-life experiments with a new humanoid robot denominated IVO and with an aerial robot. The experiments show that the combination of SFM and machine learning can solve human-aware robot navigation in complex dynamic environments. This research was supported by the grant MDM-2016-0656 funded by MCIN/AEI/ 10.13039/501100011033, the grant ROCOTRANSP PID2019-106702RB-C21 funded by MCIN/AEI/ 10.13039/501100011033 and the grant CANOPIES H2020-ICT-2020-2-101016906 funded by the European Union. |
| Databáze: | OpenAIRE |
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