Collision prediction- based genetic network programming -reinforcement learning for mobile robot adaptive navigation in unknown dynamic environments
The autonomous navigation of a Mobile Robot (MR) in unknown environments populated by abundance of static and dynamic obstacles with a moving target have tremendous importance in real time applications. The ability of an MR to navigate safely, smoothly, and quickly in such environment is cruci...
| 主要作者: | |
|---|---|
| 格式: | Thesis |
| 语言: | English |
| 出版: |
2017
|
| 主题: | |
| 在线阅读: | http://psasir.upm.edu.my/id/eprint/68583/1/FK%202018%2040%20-%20IR.pdf |
| _version_ | 1825933555404898304 |
|---|---|
| author | Mohammed, Ahmed Hassan |
| author_facet | Mohammed, Ahmed Hassan |
| author_sort | Mohammed, Ahmed Hassan |
| collection | UPM |
| description | The autonomous navigation of a Mobile Robot (MR) in unknown environments
populated by abundance of static and dynamic obstacles with a moving target have
tremendous importance in real time applications. The ability of an MR to navigate
safely, smoothly, and quickly in such environment is crucial. Current researches are
focused on investigating these complex features in static or point-to-point dynamic
environments. On the other hand, the salient downside of Q-Learning such as curse of
dimensionality (CoD) is aggravated in complex environments.
The objectives of this thesis is to address the issue of Adaptive Reinforcement
Learning (RL) approaches in order to meet the requirements of MR navigation.
Moreover, it aims to tackle CoD problem of Q-Learning (QL) to be suitable for
complex applications. For this purpose, two genetic network programming with RL
(GNP-RL) designs are proposed. The first design is based on obstacle target
correlation (OTC) environment representation and called OTC-GNP-RL. This
provides a perception of the current environment states. The second design is based
on the proposed collision prediction (CP) environment representation and called CPGNP-
RL. This representation is designed to provide collision prediction between MR
and an obstacle, as well as the perception of current surrounded environment. Besides,
it could represent an environment with compact state space and requires ones to
measure positions only. Furthermore, the combination of CP and QL (CPQL) can
overcome the downside of the CoD problem and improve navigation features.A simulation is used for evaluating the performance of the proposed approaches. The
results show that the superiority of the proposed approaches in terms of the features
of MR navigation, where all these features are taken under the design consideration of
each proposed approach. Through the evaluation, CPQL, CP-GNP-RL, and OTCGNP-
RL provide significant improvements in terms of safety (7.917%), smooth path
(71.776%), and speed (10.89%), respectively, compared with two state-of-arts
approaches, i.e. OTC based Q-learning and artificial potential field. In addition, the
learning analysis of CPQL shows its efficiency and superiority in terms of learning
convergence and safe navigation. Hence, the proposed approaches prove their
authenticity and suitability for navigation in complex and dynamic environments. |
| first_indexed | 2024-03-06T09:59:05Z |
| format | Thesis |
| id | upm.eprints-68583 |
| institution | Universiti Putra Malaysia |
| language | English |
| last_indexed | 2024-03-06T09:59:05Z |
| publishDate | 2017 |
| record_format | dspace |
| spelling | upm.eprints-685832019-05-21T03:01:01Z http://psasir.upm.edu.my/id/eprint/68583/ Collision prediction- based genetic network programming -reinforcement learning for mobile robot adaptive navigation in unknown dynamic environments Mohammed, Ahmed Hassan The autonomous navigation of a Mobile Robot (MR) in unknown environments populated by abundance of static and dynamic obstacles with a moving target have tremendous importance in real time applications. The ability of an MR to navigate safely, smoothly, and quickly in such environment is crucial. Current researches are focused on investigating these complex features in static or point-to-point dynamic environments. On the other hand, the salient downside of Q-Learning such as curse of dimensionality (CoD) is aggravated in complex environments. The objectives of this thesis is to address the issue of Adaptive Reinforcement Learning (RL) approaches in order to meet the requirements of MR navigation. Moreover, it aims to tackle CoD problem of Q-Learning (QL) to be suitable for complex applications. For this purpose, two genetic network programming with RL (GNP-RL) designs are proposed. The first design is based on obstacle target correlation (OTC) environment representation and called OTC-GNP-RL. This provides a perception of the current environment states. The second design is based on the proposed collision prediction (CP) environment representation and called CPGNP- RL. This representation is designed to provide collision prediction between MR and an obstacle, as well as the perception of current surrounded environment. Besides, it could represent an environment with compact state space and requires ones to measure positions only. Furthermore, the combination of CP and QL (CPQL) can overcome the downside of the CoD problem and improve navigation features.A simulation is used for evaluating the performance of the proposed approaches. The results show that the superiority of the proposed approaches in terms of the features of MR navigation, where all these features are taken under the design consideration of each proposed approach. Through the evaluation, CPQL, CP-GNP-RL, and OTCGNP- RL provide significant improvements in terms of safety (7.917%), smooth path (71.776%), and speed (10.89%), respectively, compared with two state-of-arts approaches, i.e. OTC based Q-learning and artificial potential field. In addition, the learning analysis of CPQL shows its efficiency and superiority in terms of learning convergence and safe navigation. Hence, the proposed approaches prove their authenticity and suitability for navigation in complex and dynamic environments. 2017-10 Thesis NonPeerReviewed text en http://psasir.upm.edu.my/id/eprint/68583/1/FK%202018%2040%20-%20IR.pdf Mohammed, Ahmed Hassan (2017) Collision prediction- based genetic network programming -reinforcement learning for mobile robot adaptive navigation in unknown dynamic environments. Doctoral thesis, Universiti Putra Malaysia. Robots - Control Mobile robots |
| spellingShingle | Robots - Control Mobile robots Mohammed, Ahmed Hassan Collision prediction- based genetic network programming -reinforcement learning for mobile robot adaptive navigation in unknown dynamic environments |
| title | Collision prediction- based genetic network programming -reinforcement learning for mobile robot adaptive navigation in unknown dynamic environments |
| title_full | Collision prediction- based genetic network programming -reinforcement learning for mobile robot adaptive navigation in unknown dynamic environments |
| title_fullStr | Collision prediction- based genetic network programming -reinforcement learning for mobile robot adaptive navigation in unknown dynamic environments |
| title_full_unstemmed | Collision prediction- based genetic network programming -reinforcement learning for mobile robot adaptive navigation in unknown dynamic environments |
| title_short | Collision prediction- based genetic network programming -reinforcement learning for mobile robot adaptive navigation in unknown dynamic environments |
| title_sort | collision prediction based genetic network programming reinforcement learning for mobile robot adaptive navigation in unknown dynamic environments |
| topic | Robots - Control Mobile robots |
| url | http://psasir.upm.edu.my/id/eprint/68583/1/FK%202018%2040%20-%20IR.pdf |
| work_keys_str_mv | AT mohammedahmedhassan collisionpredictionbasedgeneticnetworkprogrammingreinforcementlearningformobilerobotadaptivenavigationinunknowndynamicenvironments |