Mobile robot path planning using human prediction model based on massive trajectories

Noguchi, Hiroshi; Yamada, Takaki; Mori, Takahide; Sato, Tomomasa

doi:10.1109/inss.2012.6240547

Cited by 14 publications

(8 citation statements)

References 7 publications

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“…OGM prediction has already been used in path planning of autonomous robots. In [17], Variable Length Markov Model is employed to predict the OGM in an environment whose main occupants are humans. However, in [17], the experiments are restricted to cases where there is only one human exists in the vicinity of the autonomous robot.…”

Section: Related Workmentioning

confidence: 99%

“…In [17], Variable Length Markov Model is employed to predict the OGM in an environment whose main occupants are humans. However, in [17], the experiments are restricted to cases where there is only one human exists in the vicinity of the autonomous robot. Similarly in [19], multi-steps of predicted OGMs are stacked in an XYT space (T stands for time) which is then used for path planning of rescue robots.…”

Section: Related Workmentioning

confidence: 99%

“…The XYT stack of predicted OGMs is also used in other works, such as [9], however, the OGMs are either updated using object models (classic approach) or the main occupant are of the same object type, e.g. human in [17] and [19].…”

Section: Related Workmentioning

confidence: 99%

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Multi-Step Prediction of Occupancy Grid Maps With Recurrent Neural Networks

Mohajerin

Rohani

2019

2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

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We investigate the multi-step prediction of the drivable space, represented by Occupancy Grid Maps (OGMs), for autonomous vehicles. Our motivation is that accurate multi-step prediction of the drivable space can efficiently improve path planning and navigation resulting in safe, comfortable and optimum paths in autonomous driving. We train a variety of Recurrent Neural Network (RNN) based architectures on the OGM sequences from the KITTI dataset. The results demonstrate significant improvement of the prediction accuracy using our proposed difference learning method, incorporating motion related features, over the state of the art. We remove the egomotion from the OGM sequences by transforming them into a common frame. Although in the transformed sequences the KITTI dataset is heavily biased toward static objects, by learning the difference between consecutive OGMs, our proposed method provides accurate prediction over both the static and moving objects. A video of the performance of our method on the KITTI dataset is available at https: //youtu.be/Bskd0Z7eLFE.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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Multi-Step Prediction of Occupancy Grid Maps With Recurrent Neural Networks

Mohajerin

Rohani

2019

2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

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“…Tadokoro et al proposed a method that predicts pedestrian movement by estimating movement tendencies via trial and error when a pedestrian moves in an environmental cell [18]. Noguchi et al proposed a method that predicts pedestrian movement paths by modeling pedestrian movement between cells based on a variable length Markov model [19]. Other researchers have proposed methods that build pedestrian models using machine learning.…”

Section: Related Workmentioning

confidence: 99%

Model-based Pedestrian Trajectory Prediction using Environmental Sensor for Mobile Robots Navigation

Tonoki¹,

Yorozu²,

Takahashi³

2017

ijacsa

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Abstract-Safety is the most important to the mobile robots that coexist with human. There are many studies that investigate obstacle detection and collision avoidance by predicting obstacles' trajectories several seconds into the future using mounted sensors such as cameras and laser range finder (LRF) for the safe behavior control of robots. In environments such as crossing roads where blind areas occur because of visual barriers like walls, obstacle detection might be delayed and collisions might be difficult to avoid. Using environmental sensors to detect obstacles is effective in such environments. When crossing roads, there are several passages pedestrian might move and it is difficult to depict going each passage in the same movement model. Therefore, we hypothesize that a more effective way to predict pedestrian movement is by predicting passages pedestrian might move and estimating the trajectories to the passages. We acquire pedestrian trajectory data using an environmental LRF with an extended Kalman filter (EKF) and construct pedestrian movement models using vector auto regressive (VAR) models, which pedestrian state is consisting of the position, speed and direction. Then, we test the validity of the constructed pedestrian movement models using experimental data. We narrow down the selection of a pedestrian movement model by comparing the prediction error for each path between the estimated pedestrian state using an EKF, and the predicted state using each movement model. We predict the trajectory using the selected movement model. Finally, we confirm that an appropriate path model that a pedestrian can actually move through is selected before the crossing area and that only the appropriate model is selected near the crossing area.

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“…Estimation of human behaviors for mobile robot control and navigation has been applied to many systems, for instance, path planning for obstacle avoidance and future motion prediction. Noguchi [8] introduced a global path planning method for mobile robot. Human presence probabilities are employed to realize flexible and safety mobile robot avoidance.…”

Section: Introductionmentioning

confidence: 99%