Robot navigation in dense human crowds: the case for cooperation

Trautman, Peter; Ma, Jie; Murray, Richard M.; Krause, Andreas

doi:10.1109/icra.2013.6630866

Cited by 145 publications

(123 citation statements)

References 28 publications

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“…Prior work [7] has demonstrated the importance of this interactive behavior for path planning within crowded environments. Further, a robot can also communicate its intention (such as its prospective path), which can in turn impact the motion of human agents.…”

Section: Resultsmentioning

confidence: 99%

“…Modeling the human-robot interaction is necessary for indoor environments, where the motion of human agents in the environment will be influenced by that of the robot [20]. Trautman [7] models this cooperative navigation using an interaction function, and treats path planning as an inference problem over the joint space. A mixed-observability Markov decision process (MOMDP)-based model is used to reason about the intent and/or goals of human agents and plan around them in [8].…”

Section: A Path Planning For Robots Working Among Humansmentioning

confidence: 99%

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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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Section: Resultsmentioning

confidence: 99%

Section: A Path Planning For Robots Working Among Humansmentioning

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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“…As was proved mathematically, unless crowd navigation is treated as joint decision making, the robot suffers the freezing robot problem (FRP). The FRP has been experimentally observed in independent studies [54], [37]: beyond 0.55 people/m 2 , the robot was unable to move, and a 3x improvement in safety was observed when crowd navigation was treated as joint decision making instead of as path planning.…”

Section: A Robot Crowd Navigation As a Joint Decision Making Problemmentioning

confidence: 98%

“…In [51], the work in [53], [54] was extended to the case of a human sharing control with a robot for navigation through human crowds (e.g., shared control wheelchairs in crowds). Instead of choosing the standard shared control paradigm, the problem was treated as one of optimal joint decision making: how can human, robot and crowd objectives be simultaneously optimized?…”

Section: B Shared Control As a Joint Decision Making Problemmentioning

confidence: 99%

“…We thus introduce generalized shared control (GSC), where instead of arbitrating over predictions from the human and robot models, we arbitrate over the full human and robot distributions themselves (bottom half of Figure 1). Our approach is motivated by the following: distributions represented in a Gaussian process (GP) basis [49], [54], [55], [5], [30] explicitly capture multifaceted intentionality (which we call "intention ambiguity") as well as an agent's willingness to compromise within a given strategy (which we call "flexibility"). In Figure 2, we illustrate intention ambiguity and flexibility, and in Section III we define intention ambiguity and flexibility.…”

Section: Introductionmentioning

confidence: 99%

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Breaking the Human-Robot Deadlock: Surpassing Shared Control Performance Limits with Sparse Human-Robot Interaction

Trautman

2017

Robotics: Science and Systems XIII

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Abstract-Human machine teaming has, for decades, been conceptualized as a function allocation (FA) or levels of autonomy (LOA) process: the human is suited for some tasks, while the machine is suitable for others, and as machines improve they take over duties previously assigned to humans. A wide variety of methods-including adaptive, adjustable, blended, supervisory and mixed initiative control, implemented discretely or continuously, as potential fields, as virtual fixture interfaces, or haptic interfaces-are derivatives of FA/LOA. We formalize FA/LOA (and all their derivatives) under a single mathematical formulation called classical shared control (CSC). Despite the widespread adoption of CSC, we prove that it fails to optimize human and robot agreement and intent if either the human or robot model displays "intention ambiguity" (e.g., the human's intended goal is unclear or the robot finds multiple viable solutions). Practically, this suboptimality can manifest as unnecessary and unresolvable disagreement (an unnecessary deadlock). For instance, if the robot chooses to go left around an obstacle and the human chooses to go right, CSC only provides two solutions: freeze in place or collide with the obstacle (we provide a wide variety of failure examples in [52], https://arxiv.org/abs/1611.09490). We find that CSC suboptimality stems from arbitrating over model samples, rather than over models. Our key insight is thus to arbitrate over human and robot distributions; we prove this method optimizes human and robot agreement and intent and resolves deadlocking. Our key contribution is computationally efficient distribution arbitration: if the human and robot carry N our joint has fewer modes than the individual agent models. We call our approach N min -sparse generalized shared control.

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An improved fuzzy‐controlled local path planning algorithm based on dynamic window approach

Liu,

et al. 2024

Journal of Field Robotics

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With the increasingly complex operating environment of mobile robots, the intelligent requirements of robots are getting higher and higher. Navigation technology is the core of mobile robot intelligent technology research, and path planning is an important function of mobile robot navigation. Dynamic window approach (DWA) is one of the most popular local path planning algorithms nowadays. However, there are also some problems. DWA algorithm is easy to fall into local optimal solution without the guidance of global path. The traditional solution is to use the key nodes of the global path as the temporary target points. However, the guiding ability of the temporary target points will be weakened in some cases, which still leads DWA to fall into local optimal solutions such as being trapped by a “C”‐shaped obstacle or go around outside of a dense obstacle area. In a complex operating environment, if the local path deviates too far from the global path, serious consequences may be caused. Therefore, we proposed a trajectory similarity evaluation function based on dynamic time warping method to provide better guidance. The other problem is poor adaptability to complex environments due to fixed evaluation function weights. And, we designed a fuzzy controller to improve the adaptability of the DWA algorithm in complex environments. Experiment results show that the trajectory similarity evaluation function reduces algorithm execution time by 0.7% and mileage by 2.1%, the fuzzy controller reduces algorithm execution time by 10.8% and improves the average distance between the mobile robot and obstacles at the global path's danger points by 50%, and in simulated complex terrain environment, the finishing rate of experiments improves by 25%.

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Robot navigation in dense human crowds: the case for cooperation

Cited by 145 publications

References 28 publications

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

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

Breaking the Human-Robot Deadlock: Surpassing Shared Control Performance Limits with Sparse Human-Robot Interaction

An improved fuzzy‐controlled local path planning algorithm based on dynamic window approach

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