Performance of a low-cost, human-inspired perception approach for dense moving crowd navigation

Chatterjee, Ishani; Steinfeld, Aaron

doi:10.1109/roman.2016.7745176

Cited by 10 publications

(6 citation statements)

References 10 publications

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“…Other works leverage insights about the structure of selected real-world navigation tasks. For example, Chatterjee and Steinfeld [28] amd Wang and Steinfeld [248] propose a "Gestalt" approach using frameworks that cluster navigating pedestrians into groups. Nanavati et al [163] focus on leader-follower tasks involving a guiding robot and a following human and extract an empirical model of human following behavior that enables a robot to plan effective maneuvers leading humans to their destinations.…”

Section: Explicit Approachesmentioning

confidence: 99%

Core Challenges of Social Robot Navigation: A Survey

Mavrogiannis¹,

Baldini²,

Wang³

et al. 2021

Preprint

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Robot navigation in crowded public spaces is a complex task that requires addressing a variety of engineering and human factors challenges. These challenges have motivated a great amount of research resulting in important developments for the fields of robotics and human-robot interaction over the past three decades. Despite the significant progress and the massive recent interest, we observe a number of significant remaining challenges that prohibit the seamless deployment of autonomous robots in public pedestrian environments. In this survey article, we organize existing challenges into a set of categories related to broader open problems in motion planning, behavior design, and evaluation methodologies. Within these categories, we review past work, and offer directions for future research. Our work builds upon and extends earlier survey efforts by a) taking a critical perspective and diagnosing fundamental limitations of adopted practices in the field and b) offering constructive feedback and ideas that we aspire will drive research in the field over the coming decade.

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Section: Explicit Approachesmentioning

confidence: 99%

Core Challenges of Social Robot Navigation: A Survey

Mavrogiannis¹,

Baldini²,

Wang³

et al. 2021

Preprint

Self Cite

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“…The social force model [30] also inspires Mazzon et al [31], Šochman and Hogg [20] to develop features that indicate groups. Clustering is another common group of technique to group pedestrians with similar features into groups [32,33,34,35]. In this paper, we do not intend to explore the state-ofthe-art grouping practice.…”

Section: Related Workmentioning

confidence: 99%

“…In this paper, we do not intend to explore the state-ofthe-art grouping practice. For our formulation, it is sufficient to employ a simple clustering based grouping method proposed by Chatterjee and Steinfeld [35].…”

Section: Related Workmentioning

confidence: 99%

Group-based Motion Prediction for Navigation in Crowded Environments

Wang¹,

Mavrogiannis²,

Steinfeld³

2021

Preprint

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We focus on the problem of planning the motion of a robot in a dynamic multiagent environment such as a pedestrian scene. Enabling the robot to navigate safely and in a socially compliant fashion in such scenes requires a representation that accounts for the unfolding multiagent dynamics. Existing approaches to this problem tend to employ microscopic models of motion prediction that reason about the individual behavior of other agents. While such models may achieve high tracking accuracy in trajectory prediction benchmarks, they often lack an understanding of the group structures unfolding in crowded scenes. Inspired by the Gestalt theory from psychology, we build a Model Predictive Control framework (G-MPC) that leverages group-based prediction for robot motion planning. We conduct an extensive simulation study involving a series of challenging navigation tasks in scenes extracted from two real-world pedestrian datasets. We illustrate that G-MPC enables a robot to achieve statistically significantly higher safety and lower number of group intrusions than a series of baselines featuring individual pedestrian motion prediction models. Finally, we show that G-MPC can handle noisy lidar-scan estimates without significant performance losses.

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“…A human intent-aware strategy for safe and efficient robotic crowd navigation were reported by Park et al [23]. A Low-cost approach is shown by Chatterjee that employs a vision system based on Microsoft's Kinect platform [24]. A considerably more complex approach is presented by Bohorquez, that considers a bipedal robot [25].…”

Section: Introduction and Related Workmentioning

confidence: 99%

Crowded Environment Navigation with NEAT: Impact of Perception Resolution on Controller Optimization

Seriani

Marcini

Caruso

et al. 2021

J Intell Robot Syst

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Crowd navigation with autonomous systems is a topic which has seen a rapid increase in interest recently. While it appears natural to humans, being able to reach a target can prove difficult or impossible to a mobile robot because of the safety issues related to collisions with people. In this work we propose an approach to control a robot in a crowded environment; the method employs an Artificial Neural Network (ANN) that is trained with the NeuroEvolution of Augmented Topologies (NEAT) method. Models for the kinematics, perception, and cognition of the robot are presented. In particular, perception is based on a raycasting model which is tailored on the ANN. An in-depth analysis of a number of parameters of the environment and the robot is performed and a comparative analysis is presented; finally, results of the performance of the controller trained with NEAT are compared to those of a human driver who takes over the controller itself. Results show that the intelligent controller is able to perform on par with the human, within the simulated environment.

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Performance of a low-cost, human-inspired perception approach for dense moving crowd navigation

Cited by 10 publications

References 10 publications

Core Challenges of Social Robot Navigation: A Survey

Core Challenges of Social Robot Navigation: A Survey

Group-based Motion Prediction for Navigation in Crowded Environments

Crowded Environment Navigation with NEAT: Impact of Perception Resolution on Controller Optimization

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