Probabilistic Autonomous Robot Navigation in Dynamic Environments with Human Motion Prediction

Foka, Amalia F.; Trahanias, Panos

doi:10.1007/s12369-009-0037-z

Cited by 77 publications

(51 citation statements)

References 25 publications

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“…204 Other approaches that can avoid human-like obstacles while also considering the reciprocal effect of the vehicles motion have also been proposed. 81,314 …”

Section: Human-like Obstaclesmentioning

confidence: 99%

Algorithms for collision-free navigation of mobile robots in complex cluttered environments: a survey

2014

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SUMMARYWe review a range of techniques related to navigation of unmanned vehicles through unknown environments with obstacles, especially those that rigorously ensure collision avoidance (given certain assumptions about the system). This topic continues to be an active area of research, and we highlight some directions in which available approaches may be improved. The paper discusses models of the sensors and vehicle kinematics, assumptions about the environment, and performance criteria. Methods applicable to stationary obstacles, moving obstacles and multiple vehicles scenarios are all reviewed. In preference to global approaches based on full knowledge of the environment, particular attention is given to reactive methods based on local sensory data, with a special focus on recently proposed navigation laws based on model predictive and sliding mode control.

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“…204 Other approaches that can avoid human-like obstacles while also considering the reciprocal effect of the vehicles motion have also been proposed. 81,314 …”

Section: Human-like Obstaclesmentioning

confidence: 99%

Algorithms for collision-free navigation of mobile robots in complex cluttered environments: a survey

2014

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“…Therefore, preference should be given to the places where it is more likely to find the human at that particular time. To address the issue, an approach similar to [11], where MDP model has been used to schedule tasks in the office environment, and [12], where a variant of MDP (namely RN-POMDP) is described, is being proposed.…”

Section: Methodsmentioning

confidence: 99%

Use of an Autonomous Mobile Robot for Elderly Care

Berns

Mehdi

2010

2010 Advanced Technologies for Enhancing Quality of Life

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Abstract-In our societies, there is a steady increase in the elderly population. These people need a constant supervision to perform simple tasks at their homes, which is sometimes not possible. This lack of permanent attention sometimes results in the late detection of emergency situations. Therefore, it is judicious to promote technology that helps to detect and react in case of emergency situations. In the last decade, monitoring devices have been installed in the home environments for the surveillance of the inhabitant. However, due to poor privacy there will be an unfavourably low acceptance of such systems. Another approach to the problem is to develop mobile robots for elderly care. In this scenario, a mobile robot has to navigate from place to place to search for the elderly person. Navigating inefficiently in the home environment may delay the process of searching. This paper focus on the aspect of enhancing navigational efficiency of the robot that would speed up the task of finding human being in the unstructured and dynamic household environment. To achieve precision in searching the human, a behaviour based Markov decision process (MDP) along with a detailed representation of the environment as a grid map have been developed on a small sized indoor autonomous mobile robot, ARTOS.

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“…Foka et al proposed a method that predicts a pedestrian's position at the next step using the current and previous step based on a polynomial neural network. They estimated the destination using the tangent vector of the obstacle's positions at times 1, tt  and the predicted position at time 1 t  [27,28]. These methods predict indoor trajectories toward destinations such as the TV and the refrigerator.…”

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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Probabilistic Autonomous Robot Navigation in Dynamic Environments with Human Motion Prediction

Cited by 77 publications

References 25 publications

Algorithms for collision-free navigation of mobile robots in complex cluttered environments: a survey

Algorithms for collision-free navigation of mobile robots in complex cluttered environments: a survey

Use of an Autonomous Mobile Robot for Elderly Care

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

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