Dynamic window based approach to mobile robot motion control in the presence of moving obstacles

Seder, Marija; Petrović, Ivan

doi:10.1109/robot.2007.363613

Cited by 211 publications

(130 citation statements)

References 14 publications

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“…assuming that people keep moving with a constant speed. This is known to be a reasonable approximation for short-term behavior, used for collision avoidance [12,13] and tracking algorithms [14]. The method does not need any knowledge about the environment or the previous trajectory, but it is not reliable for long term prediction ignoring environmental constraints or influence .…”

Section: Related Workmentioning

confidence: 99%

“…We compute the following transition probabilities based on observed trajectories. (12) We also compute transition model with n-1 step histories (n-gram model) We computed these transitions for values of n up to 6, a compromise between computational economy and completeness of description of the environment.…”

Section: ) Computing a Series Of Past Sub-goals From A Fullyobservedmentioning

confidence: 99%

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Modeling and Prediction of Pedestrian Behavior based on the Sub-goal Concept

Ikeda

Chigodo

Rea

et al. 2012

Robotics: Science and Systems VIII

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Abstract-This study addresses a method to predict pedestrians' long term behavior in order to enable a robot to provide them services. In order to do that we want to be able to predict their final goal and the trajectory they will follow to reach it. We attain this task borrowing from human science studies the concept of sub-goals, defined as points and landmarks of the environment towards which pedestrians walk or where they take directional choices before reaching the final destination. We retrieve the position of these sub-goals from the analysis of a large set of pedestrian trajectories in a shopping mall, and model their global behavior through transition probabilities between sub-goals. The method allows us to predict the future position of pedestrians on the basis of the observation of their trajectory up to the moment. 1

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Section: Related Workmentioning

confidence: 99%

Section: ) Computing a Series Of Past Sub-goals From A Fullyobservedmentioning

confidence: 99%

Modeling and Prediction of Pedestrian Behavior based on the Sub-goal Concept

Ikeda

Chigodo

Rea

et al. 2012

Robotics: Science and Systems VIII

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“…The solutions to these two problems provide the base for the appropriate response to the obstacle. Strategies employed for avoiding obstacles in patterns include potential field (Wang et al, 2006), dynamic window (Fox et al, 1997) (Seder & Petrovic, 2007) (Ogren & Leonard, 2003) and flow field method (Shao et al, 2006).…”

Section: Obstacle Avoidancementioning

confidence: 99%

Swarm Patterns: Trends & Transformation Tools

Varghese¹,

McKee²

2011

Multi-Robot Systems, Trends and Development

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“…Nowadays, scholars have put forward many different obstacle avoidance theories according to different navigation environment. The typical methods include Curvature rate method [1] ,Vector field histogram method [2][3] , Dynamic window method [4][5] , Neighborhood histogram method [6] , Limit cycle method [7] , these methods can improve the robustness of obstacle avoidance in different degrees, but they all need time to calculate the optimal path or the optimal solution, it will sacrifice some time. Even deadlock or shock phenomenon, affect the real-time robot obstacle avoidance.…”

Section: Introductionmentioning

confidence: 99%

A Multi-objective Optimization Behavior Fusion Avoidance Method for Snake-like Rescue Robot

Hou

2017

MATEC Web Conf.

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Abstract. The obstacle avoidance path planning of rescue robot has been the key problem of autonomous navigation for rescue robot. Most of the existing path planning algorithms is aimed at the optimization of a particular target, often resulting in deadlock or oscillation in order to obtain the optimal solution or the optimal path. Therefore, using the multi-objective optimization theory, this paper presents a multi-objective optimization dynamic obstacle avoidance algorithm based on interval weights, the fusion process of obstacle avoidance process is divided into three seed action, by giving the behavior function of different weights, the output rate of real-time dynamic change of different action. This is not to obtain the optimal solution or the optimal path at the current moment, but to obtain only the most efficient solution or the most satisfactory path at the current moment. Experiments show that the algorithm can effectively improve the flexibility and security of the obstacle avoidance process under the premise of ensuring the real-time and robustness of the search and rescue robot obstacle avoidance process.

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Dynamic window based approach to mobile robot motion control in the presence of moving obstacles

Cited by 211 publications

References 14 publications

Modeling and Prediction of Pedestrian Behavior based on the Sub-goal Concept

Modeling and Prediction of Pedestrian Behavior based on the Sub-goal Concept

Swarm Patterns: Trends & Transformation Tools

A Multi-objective Optimization Behavior Fusion Avoidance Method for Snake-like Rescue Robot

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