Efficient Dynamic Object Search in Home Environment by Mobile Robot: A Priori Knowledge-Based Approach
Efficient Dynamic Object Search in Home Environment by Mobile Robot: A Priori Knowledge-Based Approach
复制标题
移动机器人在家庭环境中高效动态对象搜索：基于先验知识的方法
DOI：
10.1109/tvt.2019.2934509
复制
发表时间：
2019-08
影响因子：
6.8
通讯作者：
Zhang Senyan
中科院分区：
文献类型：
--
作者：
Zhang Ying;Tian Guohui;Lu Jiaxing;Zhang Mengyang;Zhang Senyan
目标搜索能力是移动机器人在家庭环境中执行日常任务的前提。由于目标的动态性和家庭环境的特殊性，如何以较低的总代价寻找动态目标仍然是移动机器人面临的一个具有挑战性的问题。针对这一问题，提出了一种基于先验知识的动态对象搜索方法。受人类搜索过程的启发，将关于典型空间位置和对象之间关系的常识性知识建模为先验知识。在此基础上，提出了一种新颖的搜索策略，以提高移动机器人在整个家庭环境中的搜索效率。与现有方法不同的是，该方法考虑了推断的空间位置知识和机器人位置与候选房间的距离之间的权衡，允许机器人优先搜索最有可能找到目标对象的候选房间。此外，通过使用先验知识制导的搜索启发式算法，进一步减少了房间内的搜索空间。同时，还对知识的更新进行了研究，以保持其可靠性。该方法在仿真和真实环境中都得到了实现，并通过大量的实验进行了评估。实验结果验证了该方法的可行性和有效性。
The capability of object search is a prerequisite for mobile robot to perform everyday tasks in the home environment. Due to the dynamic nature of object and the particularity of home environment, it still remains a challenging problem for mobile robot to find the dynamic target object with the low total cost. To address this problem, a priori knowledge-based approach for dynamic object search is presented in this paper. Inspired by human search process, the common sense knowledge about typical spatial location and the relationship between objects is modeled as a priori knowledge. On this basis, we propose a novel search strategy to improve the search efficiency of mobile robot at the scale of the entire home environment. Unlike the existing methods, a trade-off between the inferred spatial location knowledge and the distance of robot position and candidate room is considered, which permits robot to prioritize the search effort for the candidate room in which the target object is most likely to be found. Also, the search space inside the room is further reduced by using a priori knowledge-guided search heuristics. Meanwhile, the update of knowledge is also investigated to maintain its reliability. The proposed approach is implemented in both simulation and real environments, and evaluated through extensive experiments. The experimental results are provided to demonstrate the feasibility and efficiency of our proposal.
登录
查看更多内容
DOI：
10.1109/iros.2014.6942962
发表时间：
2014-10
期刊：
2014 IEEE/RSJ International Conference on Intelligent Robots and Systems
影响因子：
--
作者：
M. Lorbach;S. Höfer;O. Brock
通讯作者：
M. Lorbach;S. Höfer;O. Brock
影响因子：
7.8
作者：
Nils Bore;Johan Ekekrantz;P. Jensfelt;J. Folkesson
通讯作者：
Nils Bore;Johan Ekekrantz;P. Jensfelt;J. Folkesson
DOI：
10.1177/0278364911410090
发表时间：
2011-09
期刊：
The International Journal of Robotics Research
影响因子：
--
作者：
Jeremy Ma;Timothy H. Chung;J. Burdick
通讯作者：
Jeremy Ma;Timothy H. Chung;J. Burdick
DOI：
--
发表时间：
2011
期刊：
--
影响因子：
--
作者：
A. Aydemir;M. Göbelbecker;Andrzej Pronobis;K. Sjöö;P. Jensfelt
通讯作者：
A. Aydemir;M. Göbelbecker;Andrzej Pronobis;K. Sjöö;P. Jensfelt
DOI：
10.1109/iros.2017.8202319
发表时间：
2017-09
期刊：
2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
影响因子：
--
作者：
Hassan Umari;S. Mukhopadhyay
通讯作者：
Hassan Umari;S. Mukhopadhyay