Robot Obstacle Avoidance Learning Based on Mixture Models
We briefly surveyed the existing obstacle avoidance algorithms; then a new obstacle avoidance learning framework based on learning from demonstration (LfD) is proposed. The main idea is to imitate the obstacle avoidance mechanism of human beings, in which humans learn to make a decision based on the...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2016-01-01
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| Series: | Journal of Robotics |
| Online Access: | http://dx.doi.org/10.1155/2016/7840580 |
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| _version_ | 1832551008186662912 |
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| author | Huiwen Zhang Xiaoning Han Mingliang Fu Weijia Zhou |
| author_facet | Huiwen Zhang Xiaoning Han Mingliang Fu Weijia Zhou |
| author_sort | Huiwen Zhang |
| collection | DOAJ |
| description | We briefly surveyed the existing obstacle avoidance algorithms; then a new obstacle avoidance learning framework based on learning from demonstration (LfD) is proposed. The main idea is to imitate the obstacle avoidance mechanism of human beings, in which humans learn to make a decision based on the sensor information obtained by interacting with environment. Firstly, we endow robots with obstacle avoidance experience by teaching them to avoid obstacles in different situations. In this process, a lot of data are collected as a training set; then, to encode the training set data, which is equivalent to extracting the constraints of the task, Gaussian mixture model (GMM) is used. Secondly, a smooth obstacle-free path is generated by Gaussian mixture regression (GMR). Thirdly, a metric of imitation performance is constructed to derive a proper control policy. The proposed framework shows excellent generalization performance, which means that the robots can fulfill obstacle avoidance task efficiently in a dynamic environment. More importantly, the framework allows learning a wide variety of skills, such as grasp and manipulation work, which makes it possible to build a robot with versatile functions. Finally, simulation experiments are conducted on a Turtlebot robot to verify the validity of our algorithms. |
| format | Article |
| id | doaj-art-ce4e9d4297244dc38007368369f153ca |
| institution | Kabale University |
| issn | 1687-9600 1687-9619 |
| language | English |
| publishDate | 2016-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Robotics |
| spelling | doaj-art-ce4e9d4297244dc38007368369f153ca2025-02-03T06:05:19ZengWileyJournal of Robotics1687-96001687-96192016-01-01201610.1155/2016/78405807840580Robot Obstacle Avoidance Learning Based on Mixture ModelsHuiwen Zhang0Xiaoning Han1Mingliang Fu2Weijia Zhou3State Key Laboratory of Robotics, Shenyang Institute of Automation, Shenyang 110016, ChinaState Key Laboratory of Robotics, Shenyang Institute of Automation, Shenyang 110016, ChinaState Key Laboratory of Robotics, Shenyang Institute of Automation, Shenyang 110016, ChinaState Key Laboratory of Robotics, Shenyang Institute of Automation, Shenyang 110016, ChinaWe briefly surveyed the existing obstacle avoidance algorithms; then a new obstacle avoidance learning framework based on learning from demonstration (LfD) is proposed. The main idea is to imitate the obstacle avoidance mechanism of human beings, in which humans learn to make a decision based on the sensor information obtained by interacting with environment. Firstly, we endow robots with obstacle avoidance experience by teaching them to avoid obstacles in different situations. In this process, a lot of data are collected as a training set; then, to encode the training set data, which is equivalent to extracting the constraints of the task, Gaussian mixture model (GMM) is used. Secondly, a smooth obstacle-free path is generated by Gaussian mixture regression (GMR). Thirdly, a metric of imitation performance is constructed to derive a proper control policy. The proposed framework shows excellent generalization performance, which means that the robots can fulfill obstacle avoidance task efficiently in a dynamic environment. More importantly, the framework allows learning a wide variety of skills, such as grasp and manipulation work, which makes it possible to build a robot with versatile functions. Finally, simulation experiments are conducted on a Turtlebot robot to verify the validity of our algorithms.http://dx.doi.org/10.1155/2016/7840580 |
| spellingShingle | Huiwen Zhang Xiaoning Han Mingliang Fu Weijia Zhou Robot Obstacle Avoidance Learning Based on Mixture Models Journal of Robotics |
| title | Robot Obstacle Avoidance Learning Based on Mixture Models |
| title_full | Robot Obstacle Avoidance Learning Based on Mixture Models |
| title_fullStr | Robot Obstacle Avoidance Learning Based on Mixture Models |
| title_full_unstemmed | Robot Obstacle Avoidance Learning Based on Mixture Models |
| title_short | Robot Obstacle Avoidance Learning Based on Mixture Models |
| title_sort | robot obstacle avoidance learning based on mixture models |
| url | http://dx.doi.org/10.1155/2016/7840580 |
| work_keys_str_mv | AT huiwenzhang robotobstacleavoidancelearningbasedonmixturemodels AT xiaoninghan robotobstacleavoidancelearningbasedonmixturemodels AT mingliangfu robotobstacleavoidancelearningbasedonmixturemodels AT weijiazhou robotobstacleavoidancelearningbasedonmixturemodels |