An Approach to Socially Compliant Leader Following for Mobile Robots

Kuderer, Markus; Burgard, Wolfram

doi:10.1007/978-3-319-11973-1_24

Cited by 14 publications

(6 citation statements)

References 13 publications

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“…Prassler et al [6] considered side-by-side following within the application of a robotic wheelchair following a human. Also Kuderer and Burgard [7] considered the wheelchair application and developed an approach to predict the human trajectory taking into account information about obstacles in the environment. The authors compute the robot's trajectory so that the distance to the desired relative position along the predicted path is minimized.…”

Section: Related Workmentioning

confidence: 99%

Learning optimal navigation actions for foresighted robot behavior during assistance tasks

Bayoumi

Bennewitz

2016

2016 IEEE International Conference on Robotics and Automation (ICRA)

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We present an approach to learn optimal navigation actions for assistance tasks in which the robot aims at efficiently reaching the final navigation goal of a human where service has to be provided. Always following the human at a close distance might hereby result in inefficient trajectories, since people regularly do not move on the shortest path to their destination (e.g., they move to grab the phone or make a note). Therefore, a service robot should infer the human's intended navigation goal and compute its own motion based on that prediction. We developed an approach that applies reinforcement learning to get a Q-function that determines for each pair of the robot's and human's relative positions the best navigation action for the robot. Our approach applies a prediction of the human's motion based on a softened Markov decision process (MDP). This MDP is independent from the navigation learning framework and is learned beforehand on previously observed trajectories. We thoroughly evaluated our method in simulation and on a real robot. As the experimental results show, our approach leads to foresighted navigation behavior and significantly reduces the path length and completion time compared to naive following strategies.

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Section: Related Workmentioning

confidence: 99%

Learning optimal navigation actions for foresighted robot behavior during assistance tasks

Bayoumi

Bennewitz

2016

2016 IEEE International Conference on Robotics and Automation (ICRA)

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“…Several studies are implementing Social Force Model (SFM) into a mobile robot to navigate (Tamura, et al, 2010) (Yang, et al, 2019). Besides that, several applications of SFM are used for other purposes, such as companion robots (Ferrer, et al, 2013), the human leader following robot (Kuderer & Burgard, 2014), human guide robot (Dewantara & Miura, 2017) (Muallimi, et al, 2020), human-robot collision avoidance (Ratsamee, et al, 2013), social robot navigation (Kivrak, et al, 2021). Other researchers have developed SFM applications in tour-guide robots (Bellarbi, et al, 2017), healthcare robot navigation (Rifqi, et al, 2021), and SFM in quadcopter robots (Gil, et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

Development of an Omni Directional based Mobile Robot Navigation System using Optimized-Fuzzy Social Force Model

2022

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Membangun sebuah sistem navigasi pada mobile robot yang bergerak di ruang sosial perlu memperhatikan beberapa aspek krusial, seperti menghindari rintangan, menjaga arah hadap robot ke tujuan, dan mencapai tujuan dengan cepat. Penelitian ini bertujuan untuk mengembangkan sistem navigasi pada Omnidirectional mobile robot menggunakan Fuzzy-Social Force Model (FSFM). Social Force Model (SFM) mampu menggerakan robot ke tujuan sambil menghindari rintangan. Fuzzy Inference System (FIS) digunakan untuk menghasilkan gain adaptif sebagai salah satu parameter SFM agar respon SFM sesuai dengan masukan dari sensor lidar. Aturan FIS dioptimasi agar mendapatkan nilai optimal menggunakan Particle Swarm Optimization (PSO). Dari hasil percobaan, mobile robot mencapai tujuan lebih cepat dengan selisih 1.59 s dan nilai error heading robot lebih kecil 0.9261 dibandingkan FSFM tanpa optimasi.

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“…The introduction of robotics in our daily activities in the near future will require the navigation of humans and robots in different environments, including public spaces, buildings and private houses. The applications are huge, for example in assistive robotics tasks [1], collaborative searching [2], side-by-side navigation [3], guiding people [4] or other social navigation tasks. In any of these applications, robots have to behave in a social manner, being social-aware (that is, the robot has to plan the trajectory helping the accompaniment or guiding a person while not disturbing other pedestrian trajectories if possible) and avoiding colliding with obstacles or pedestrians.…”

Section: Introductionmentioning

confidence: 99%

Social Robot Navigation Tasks: Combining Machine Learning Techniques and Social Force Model

Gil

Garrell

Sanfeliu

2021

Sensors

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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.

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An Approach to Socially Compliant Leader Following for Mobile Robots

Cited by 14 publications

References 13 publications

Learning optimal navigation actions for foresighted robot behavior during assistance tasks

Learning optimal navigation actions for foresighted robot behavior during assistance tasks

Development of an Omni Directional based Mobile Robot Navigation System using Optimized-Fuzzy Social Force Model

Social Robot Navigation Tasks: Combining Machine Learning Techniques and Social Force Model

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