Avoiding cars and pedestrians using velocity obstacles and motion prediction

Large, F.; Vasquez, Dizan; Fraichard, Thierry; Laugier, Christian

doi:10.1109/ivs.2004.1336412

Cited by 62 publications

(36 citation statements)

References 10 publications

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“…Some research has thus focused on limiting this predictive uncertainty. For instance, in Thompson et al (2009), Bennewitz et al (2005), Helble and Cameron (2007) and Large et al (2004), high-fidelity independent human motion models were developed, in the hope that controlling the predictive uncertainty would lead to improved navigation performance. The work of Du Toit and Burdick (2012) and Du Toit (2009) improves navigation performance by directly limiting individual agent predictive uncertainty.…”

Section: Related Workmentioning

confidence: 99%

Robot navigation in dense human crowds: Statistical models and experimental studies of human–robot cooperation

Trautman

Murray

et al. 2015

The International Journal of Robotics Research

225

170

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We consider the problem of navigating a mobile robot through dense human crowds. We begin by exploring a fundamental impediment to classical motion planning algorithms called the ''freezing robot problem'': once the environment surpasses a certain level of dynamic complexity, the planner decides that all forward paths are unsafe, and the robot freezes in place (or performs unnecessary maneuvers) to avoid collisions. We argue that this problem can be avoided if the robot anticipates human cooperation, and accordingly we develop interacting Gaussian processes, a prediction density that captures cooperative collision avoidance, and a ''multiple goal'' extension that models the goal-driven nature of human decision making. We validate this model with an empirical study of robot navigation in dense human crowds (488 runs), specifically testing how cooperation models effect navigation performance. The multiple goal interacting Gaussian processes algorithm performs comparably with human teleoperators in crowd densities nearing 0.8 humans/m 2 , while a state-of-theart non-cooperative planner exhibits unsafe behavior more than three times as often as the multiple goal extension, and twice as often as the basic interacting Gaussian process approach. Furthermore, a reactive planner based on the widely used dynamic window approach proves insufficient for crowd densities above 0.55 people/m 2 . We also show that our noncooperative planner or our reactive planner capture the salient characteristics of nearly any dynamic navigation algorithm. Based on these experimental results and theoretical observations, we conclude that a cooperation model is critical for safe and efficient robot navigation in dense human crowds.

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Section: Related Workmentioning

confidence: 99%

Robot navigation in dense human crowds: Statistical models and experimental studies of human–robot cooperation

Trautman

Murray

et al. 2015

The International Journal of Robotics Research

225

170

View full text Add to dashboard Cite

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“…One study [4] uses velocity obstacles to plan around humans. Gaussian processes for prediction and a probabilistic extension of RRT are used to plan paths for dynamic environments in [15].…”

Section: A Path Planning For Robots Working Among Humansmentioning

confidence: 99%

“…Existing approaches that address the problem of robot path planning in proximity to humans have incorporated both of these aspects at different levels of detail [4]- [8]. Here, we focus on improving human motion prediction through the use of human walking motion features that have been identified as statistically significant in prior biomechanical studies.…”

Section: Introductionmentioning

confidence: 99%

Human-robot co-navigation using anticipatory indicators of human walking motion

Unhelkar

Pérez-D’Arpino

Stirling

et al. 2015

2015 IEEE International Conference on Robotics and Automation (ICRA)

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Abstract-Mobile, interactive robots that operate in humancentric environments need the capability to safely and efficiently navigate around humans. This requires the ability to sense and predict human motion trajectories and to plan around them. In this paper, we present a study that supports the existence of statistically significant biomechanical turn indicators of human walking motions. Further, we demonstrate the effectiveness of these turn indicators as features in the prediction of human motion trajectories. Human motion capture data is collected with predefined goals to train and test a prediction algorithm. Use of anticipatory features results in improved performance of the prediction algorithm. Lastly, we demonstrate the closedloop performance of the prediction algorithm using an existing algorithm for motion planning within dynamic environments. The anticipatory indicators of human walking motion can be used with different prediction and/or planning algorithms for robotics; the chosen planning and prediction algorithm demonstrates one such implementation for human-robot conavigation.

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“…The concept of a velocity obstacle, as detailed by [4,5,7,13], was used in the development of VOS. Both traditional velocity obstacles and VOS require a vehicle to be able to instantaneously change velocities in order for the velocity obstacles to remain valid.…”

Section: Velocity Obstaclesmentioning

confidence: 99%