The Experts below are selected from a list of 46578 Experts worldwide ranked by ideXlab platform
Andreas Krause - One of the best experts on this subject based on the ideXlab platform.
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Robot Navigation in dense human crowds statistical models and experimental studies of human Robot cooperation
The International Journal of Robotics Research, 2015Co-Authors: Pete Trautman, Richard M Murray, Andreas KrauseAbstract: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 âfreezing Robot problemâ: 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 âmultiple goalâ 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/m2, while a state-of-the-art 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/m2. We also show that our non-cooperative 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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Robot Navigation in dense human crowds the case for cooperation
International Conference on Robotics and Automation, 2013Co-Authors: Pete Trautman, Richard M Murray, Andreas KrauseAbstract:We consider mobile Robot Navigation in dense human crowds. In particular, we explore two questions. Can we design a Navigation algorithm that encourages humans to cooperate with a Robot? Would such cooperation improve Navigation performance? We address the first question by developing a probabilistic predictive model of cooperative collision avoidance and goal-oriented behavior by extending the interacting Gaussian processes approach to include multiple goals and stochastic movement duration. We answer the second question with an extensive quantitative study of Robot Navigation in dense human crowds (488 runs completed), specifically testing how cooperation models effect Navigation performance. We find that the “multiple goal” interacting Gaussian processes algorithm performs comparably with human teleoperators in crowd densities near 1 person/m2, while a state of the art noncooperative planner exhibits unsafe behavior more than 3 times as often as this multiple goal extension, and more than twice as often as the basic interacting Gaussian processes. Furthermore, a reactive planner based on the widely used “dynamic window” approach fails for crowd densities above 0.55 people/m2. Based on these experimental results, and previous theoretical observations, we conclude that a cooperation model is important for safe and efficient Robot Navigation in dense human crowds.
Pete Trautman - One of the best experts on this subject based on the ideXlab platform.
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Robot Navigation in dense human crowds statistical models and experimental studies of human Robot cooperation
The International Journal of Robotics Research, 2015Co-Authors: Pete Trautman, Richard M Murray, Andreas KrauseAbstract: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 âfreezing Robot problemâ: 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 âmultiple goalâ 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/m2, while a state-of-the-art 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/m2. We also show that our non-cooperative 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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Robot Navigation in dense human crowds the case for cooperation
International Conference on Robotics and Automation, 2013Co-Authors: Pete Trautman, Richard M Murray, Andreas KrauseAbstract:We consider mobile Robot Navigation in dense human crowds. In particular, we explore two questions. Can we design a Navigation algorithm that encourages humans to cooperate with a Robot? Would such cooperation improve Navigation performance? We address the first question by developing a probabilistic predictive model of cooperative collision avoidance and goal-oriented behavior by extending the interacting Gaussian processes approach to include multiple goals and stochastic movement duration. We answer the second question with an extensive quantitative study of Robot Navigation in dense human crowds (488 runs completed), specifically testing how cooperation models effect Navigation performance. We find that the “multiple goal” interacting Gaussian processes algorithm performs comparably with human teleoperators in crowd densities near 1 person/m2, while a state of the art noncooperative planner exhibits unsafe behavior more than 3 times as often as this multiple goal extension, and more than twice as often as the basic interacting Gaussian processes. Furthermore, a reactive planner based on the widely used “dynamic window” approach fails for crowd densities above 0.55 people/m2. Based on these experimental results, and previous theoretical observations, we conclude that a cooperation model is important for safe and efficient Robot Navigation in dense human crowds.
Sebastian Thrun - One of the best experts on this subject based on the ideXlab platform.
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Learning metric-topological maps for indoor mobile Robot Navigation
Artificial Intelligence, 1998Co-Authors: Sebastian ThrunAbstract:Autonomous Robots must be able to learn and maintain models of their environments. Research on mobile Robot Navigation has produced two major paradigms for mapping indoor environments: grid-based and topological. While grid-based methods produce accurate metric maps, their complexity often prohibits efficient planning and problem solving in large-scale indoor environments. Topological maps, on the other hand, can be used much more efficiently, yet accurate and consistent topological maps are often difficult to learn and maintain in large-scale environments, particularly if momentary sensor data is highly ambiguous. This paper describes an approach that integrates both paradigms: grid-based and topological. Grid-based maps are learned using artificial neural networks and naive Bayesian integration. Topological maps are generated on top of the grid-based maps, by partitioning the latter into coherent regions. By combining both paradigms, the approach presented here gains advantages from both worlds: accuracy/consistency and efficiency. The paper gives results for autonomous exploration, mapping and operation of a mobile Robot in populated multi-room environments.
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integrating grid based and topological maps for mobile Robot Navigation
National Conference on Artificial Intelligence, 1996Co-Authors: Sebastian Thrun, Arno BuAbstract:Research on mobile Robot Navigation has produced two major paradigms for mapping indoor environments: grid-based and topological. While grid-based methods produce accurate metric maps, their complexity often prohibits efficient planning and problem solving in large-scale indoor environments. Topological maps, on the other hand, can be used much more efficiently, yet accurate and consistent topological maps are considerably difficult to learn in large-scale environments. This paper describes an approach that integrates both paradigms: grid-based and topological. Grid-based maps are learned using artificial neural networks and Bayesian integration. Topological maps are generated on top of the grid-based maps, by partitioning the latter into coherent regions. By combining both paradigms--grid-based and topological--, the approach presented here gains the best of both worlds: accuracy/consistency and efficiency. The paper gives results for autonomously operating a mobile Robot equipped with sonar sensors in populated multi-room environments.
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learning maps for indoor mobile Robot Navigation
1996Co-Authors: Sebastian Thrun, Arno BueckenAbstract:Abstract : Autonomous Robots must be able to learn and maintain models of their environments. Research on mobile Robot Navigation has produced two major paradigms for mapping indoor environments: grid-based and topological. While grid-based methods produce accurate metric maps, their complexity often prohibits efficient planning and problem solving in large-scale indoor environments. Topological maps, on the other hand, can be used much more efficiently, yet accurate and consistent topological maps are considerably difficult to learn in large-scale environments. This paper describes an approach that integrates both paradigms: grid-based and topological. Grid-based maps are learned using artificial neural networks and Bayesian integration. Topological maps are generated on top of the grid- based maps, by partitioning the latter into coherent regions. By combining both paradigms-grid-based and topological-, the approach presented here gains the best of both worlds: accuracy/consistency and efficiency. The paper gives results for autonomously operating a mobile Robot equipped with sonar sensors in populated multi-room environments.
Dayal R Parhi - One of the best experts on this subject based on the ideXlab platform.
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mobile Robot Navigation and obstacle avoidance techniques a review
International Conference on Robotics and Automation, 2017Co-Authors: P Anish, P Shalini, Dayal R ParhiAbstract:Mobile Robot is an autonomous agent capable of navigating intelligently anywhere using sensor actuator control techniques The applications of the autonomous mobile Robot in many fields such as industry space defence and transportation and other social sectors are growing day by day The mobile Robot performs many tasks such as rescue operation patrolling disaster relief planetary exploration and material handling etc Therefore an intelligent mobile Robot is required that could travel autonomously in various static and dynamic environments Several techniques have been applied by the various researchers for mobile Robot Navigation and obstacle avoidance The present article focuses on the study of the intelligent Navigation techniques which are capable of navigating a mobile Robot autonomously in static as well as dynamic environments
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mobile Robot Navigation in unknown static environments using anfis controller
Perspectives on Science, 2016Co-Authors: Anish Pandey, Saroj Kumar, Krishna Kant Pandey, Dayal R ParhiAbstract:Summary Navigation and obstacle avoidance are the most important task for any mobile Robots. This article presents the Adaptive Neuro-Fuzzy Inference System (ANFIS) controller for mobile Robot Navigation and obstacle avoidance in the unknown static environments. The different sensors such as ultrasonic range finder sensor and sharp infrared range sensor are used to detect the forward obstacles in the environments. The inputs of the ANFIS controller are obstacle distances obtained from the sensors, and the controller output is a Robot steering angle. The primary objective of the present work is to use ANFIS controller to guide the mobile Robot in the given environments. Computer simulations are conducted through MATLAB software and implemented in real time by using C/C++ language running Arduino microcontroller based mobile Robot. Moreover, the successful experimental results on the actual mobile Robot demonstrate the effectiveness and efficiency of the proposed controller.
Dinesh Manocha - One of the best experts on this subject based on the ideXlab platform.
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Modelling Multi-Channel Emotions Using Facial Expression and Trajectory Cues for Improving Socially-Aware Robot Navigation
2019 IEEE CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2019Co-Authors: Aniket Bera, Tanmay Randhavane, Dinesh ManochaAbstract:Using facial expressions and trajectory signals, we present an emotion-aware Navigation algorithm for social Robots. Our approach uses a combination of Bayesian-inference, CNN-based learning and the Pleasure-Arousal-Dominance model from psychology to estimate time-varying emotional behaviors of pedestrians from their faces and trajectories. For each pedestrian, these PAD characteristics are used to generate proxemic constraints. We use a multi-channel model to classify pedestrian features into four categories of emotions (happy, sad, angry, neutral). We observe an emotional detection accuracy of 85.33% in our validation results. In low-to medium-density environments, we formulate emotion-based proxemic constraints to perform socially conscious Robot Navigation. With Pepper, a social humanoid Robot, we demonstrate the benefits of our algorithm in simulated environments with tens of pedestrians as well as in a real world setting.
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sociosense Robot Navigation amongst pedestrians with social and psychological constraints
Intelligent Robots and Systems, 2017Co-Authors: Aniket Bera, Tanmay Randhavane, Rohan Prinja, Dinesh ManochaAbstract:We present a real-time algorithm, SocioSense, for socially-aware Navigation of a Robot amongst pedestrians. Our approach computes time-varying behaviors of each pedestrian using Bayesian learning and Personality Trait theory. These psychological characteristics are used for long-term path prediction and generating proxemic characteristics for each pedestrian. We combine these psychological constraints with social constraints to perform human-aware Robot Navigation in low- to medium-density crowds. The estimation of time-varying behaviors and pedestrian personalities can improve the performance of long-term path prediction by 21%, as compared to prior interactive path prediction algorithms. We also demonstrate the benefits of our socially-aware Navigation in simulated environments with tens of pedestrians.
