Real Time Crowd Navigation from First Principles of Probability Theory

Trautman, Peter; Patel, Karankumar

doi:10.1609/icaps.v30i1.6741

Cited by 7 publications

(3 citation statements)

References 41 publications

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“…In [6], an individual's intent is modeled as a Gaussian process and CCA is modeled as a joint decision-making process by coupling Gaussian processes through a collision avoidance-based likelihood function. The statistical optimality of coupled Gaussian processes is further investigated in [38], [39]. In [40], a distribution space crowd navigation model is proposed, with agent intent modeled as a distribution of trajectories.…”

Section: Coupled Prediction and Planningmentioning

confidence: 99%

Move Beyond Trajectories: Distribution Space Coupling for Crowd Navigation

Sun¹,

Baldini²,

Trautman³

et al. 2021

Robotics: Science and Systems XVII

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Cooperatively avoiding collision is a critical functionality for robots navigating in dense human crowds, failure of which could lead to either overaggressive or overcautious behavior. A necessary condition for cooperative collision avoidance is to couple the prediction of the agents' trajectories with the planning of the robot's trajectory. However, it is unclear that trajectory based cooperative collision avoidance captures the correct agent attributes. In this work we migrate from trajectory based coupling to a formalism that couples agent preference distributions. In particular, we show that preference distributions (probability density functions representing agents' intentions) can capture higher order statistics of agent behaviors, such as willingness to cooperate. Thus, coupling in distribution space exploits more information about inter-agent cooperation than coupling in trajectory space. We thus introduce a general objective for coupled prediction and planning in distribution space, and propose an iterative best response optimization method based on variational analysis with guaranteed sufficient decrease. Based on this analysis, we develop a sampling-based motion planning framework called DistNav 1 that runs in real time on a laptop CPU. We evaluate our approach on challenging scenarios from both real world datasets and simulation environments, and benchmark against a wide variety of model based and machine learning based approaches. The safety and efficiency statistics of our approach outperform all other models. Finally, we find that DistNav is competitive with human safety and efficiency performance.

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Section: Coupled Prediction and Planningmentioning

confidence: 99%

Move Beyond Trajectories: Distribution Space Coupling for Crowd Navigation

Sun¹,

Baldini²,

Trautman³

et al. 2021

Robotics: Science and Systems XVII

View full text Add to dashboard Cite

show abstract

“…Mobile service robots offer great societal value, such as transporting personal mobility devices (Segway, USA, Rokuro, Japan), last-mile delivery services (Starship Inc. USA), autonomous cleaning robots (Bluebotics, Switzerland), autonomous wheelchairs (Whill Inc. Japan), telepresence robots and tour-guide robots. Nonetheless, most robots are still limited to navigation in low-density areas, such as pathways or large open areas with the basic safety control system setting the robot to freeze as soon as it perceives a likely contact with pedestrians [1], [2]. Such a reaction would most likely be unexpected by pedestrians and lead to more dangerous collisions with pedestrians stumbling on the robot, a "frozen" robot would become a danger to itself Fig.…”

Section: Introductionmentioning

confidence: 99%

“…As well as, all source code for processing and analyzing interactions II. PROBLEM STATEMENT Obstacle avoidance methods usually consider a bi-state problem with collisions as an absolute negative state which in turn leads to the "freezing robot" problem [1], [2]. Nonetheless, contact might be inescapable when the robot's kinematic and dynamic constraints are below the pedestrians.…”

Section: Introductionmentioning

confidence: 99%

Pedestrian-Robot Interactions on Autonomous Crowd Navigation: Reactive Control Methods and Evaluation Metrics

Paez-Granados

Gonon

et al. 2022

2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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Autonomous navigation in highly populated areas remains a challenging task for robots because of the difficulty in guaranteeing safe interactions with pedestrians in unstructured situations. In this work, we present a crowd navigation control framework that delivers continuous obstacle avoidance and post-contact control evaluated on an autonomous personal mobility vehicle. We propose evaluation metrics for accounting efficiency, controller response and crowd interactions in natural crowds. We report the results of over 110 trials in different crowd types: sparse, flows, and mixed traffic, with low-(< 0.15 ppsm), mid-(< 0.65 ppsm), and high-(< 1 ppsm) pedestrian densities. We present comparative results between two low-level obstacle avoidance methods and a baseline of shared control. Results show a 10% drop in relative time to goal on the highest density tests, and no other efficiency metric decrease. Moreover, autonomous navigation showed to be comparable to shared-control navigation with a lower relative jerk and significantly higher fluency in commands indicating high compatibility with the crowd. We conclude that the reactive controller fulfils a necessary task of fast and continuous adaptation to crowd navigation, and it should be coupled with high-level planners for environmental and situational awareness.

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An integration of enhanced social force and crowd control models for high-density crowd simulation

Kolivand

Rahim

Sunar

et al. 2020

Neural Comput & Applic

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Social force model is one of the well-known approaches that can successfully simulate pedestrians’ movements realistically. However, it is not suitable to simulate high-density crowd movement realistically due to the model having only three basic crowd characteristics which are goal, attraction, and repulsion. Therefore, it does not satisfy the high-density crowd condition which is complex yet unique, due to its capacity, density, and various demographic backgrounds of the agents. Thus, this research proposes a model that improves the social force model by introducing four new characteristics which are gender, walking speed, intention outlook, and grouping to make simulations more realistic. Besides, the high-density crowd introduces irregular behaviours in the crowd flow, which is stopping motion within the crowd. To handle these scenarios, another model has been proposed that controls each agent with two different states: walking and stopping. Furthermore, the stopping behaviour was categorized into a slow stop and sudden stop. Both of these proposed models were integrated to form a high-density crowd simulation framework. The framework has been validated by using the comparison method and fundamental diagram method. Based on the simulation of 45,000 agents, it shows that the proposed framework has a more accurate average walking speed (0.36 m/s) compared to the conventional social force model (0.61 m/s). Both of these results are compared to the real-world data which is 0.3267 m/s. The findings of this research will contribute to the simulation activities of pedestrians in a highly dense population.

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Real Time Crowd Navigation from First Principles of Probability Theory

Cited by 7 publications

References 41 publications

Move Beyond Trajectories: Distribution Space Coupling for Crowd Navigation

Move Beyond Trajectories: Distribution Space Coupling for Crowd Navigation

Pedestrian-Robot Interactions on Autonomous Crowd Navigation: Reactive Control Methods and Evaluation Metrics

An integration of enhanced social force and crowd control models for high-density crowd simulation

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