Computer Science ›› 2019, Vol. 46 ›› Issue (4): 210-215.doi: 10.11896/j.issn.1002-137X.2019.04.033

• Artificial Intelligence • Previous Articles     Next Articles

Local Path Planning of Mobile Robot Based on Situation Assessment Technology

CHAI Hui-min, FANG Min, LV Shao-nan   

  1. School of Computer Science and Technology,Xidian University,Xi’an 710071,China
  • Received:2018-03-09 Online:2019-04-15 Published:2019-04-23

Abstract: From the cognitive perspective,an approach by situation assessment technology was proposed for local path planning of mobile robot.First,the angle rang [10°,170°] in the front of mobile robot is divided into five parts under the robot coordinate system.The environmental situation factors of the five parts can be extracted for mobile robot from the fusion results of laser data and image data.Then,Bayesian networks model for robot action choosing is constructed.The environmental situation factors are regarded as evidences for the Bayesian networks mode.The inference can be made and the action in which the posterior probability is maximum is chosen.The action can be straight line walking,obstacle avoidance,escaping from U trap.Furthermore,the chosen action is processed to let robot move to the next grid.The next grid cell is chosen according to sonar data and the direction of robot is adjusted at the same time.Experiments including eleven typical simulation scenes were given.In these experiments,one scene test fails and the rest ten scenes are successful.In the ten scenes,mobile robot can reach the destination with the shortest route or secondary shortest route.The results show that the approach about situation assessment technology is effective and available for local path planning of mobile robot.

Key words: Mobile robot, Local path planning, Situation assessment, Bayesian networks model, Grid cell choosing

CLC Number: 

  • TP242
[1]MCFETRIDGE L,IBRAHIM M Y.A new methodology of mobile robot navigation:the agoraphilic algorithm[J].Robotics and Computer-Integrated Manufacturing,2009,25(3):545-551.
[2]YU Z Z,YAN J H,ZHAO J,et al.Mobile robot path planning based on improved artificial potential field method[J].Journal of Harbin Institute of Technology,2011,43(1):50-55.(in Chinese) 于振中,阎继宏,赵杰,等.改进人工势场法的移动机器人路径规划[J].哈尔滨工业大学学报,2011,43(1):50-55.
[3]ZHANG Q S,CHEN D D,CHEN T.An Obstacle Avoidance Method of Soccer Robot Based on Evolutionary Artificial Potential Field[J].Energy Procedia,2012,16(5):1792-1798.
[4]TOIBERO J M,ROBERTI F,CARELLI R,et al.Switching control approach for stable navigation of stable navigation of mobile robots in unknown environments[J].Robotics and Computer-Integrated Manufacturing.2011,27(3):558-568.
[5]ZHANG Q.Path Planning and Location for Mobile Robot[D].Harbin:Harbin Institute of Technology,2014.(in Chinese) 张琦.移动机器人的路径规划与定位技术研究[D].哈尔滨:哈尔滨工业大学,2014.
[6]BU X P,SU H,ZOU W,et al.Ant Colony Path Planning Based on Non-uniform Modeling of Complex Environment[J].Robot,2016,38(3):276-284.(in Chinese) 卜新苹,苏虎,邹伟,等.基于复杂环境非均匀建模的蚁群路径规划[J].机器人,2016,38(3):276-284.
[7]WANG L,LI M.Application of improved adaptive genetic algorithm in mobile robot path planning[J].Journal of Nanjing University of Science and Technology,2017,41(5):627-632.(in Chinese) 王雷,李明.改进自适应遗传算法在移动机器人路径规划中的应用[J].南京理工大学学报,2017,41(5):627-632.
[8]ZHAO Z S,FENG X,WEI F,et al.Learning representative features for robot topological localization[J].International Journal of Advanced Robotic Systems,2013,10(4):1-12.
[9]LIU Jie,ZHAO H F,ZHOU D L.Improved Quantum Behaved Particle Swarm Optimization Algorithm for Mobile Robot Path Planning[J].Computer Science,2017,44(S2):123-128.(in Chinese) 刘洁,赵海芳,周德廉.一种改进量子行为粒子群优化算法的移动机器人路径规划[J].计算机科学,2017,44(S2):123-128.
[10]DAS S.High-Level Data Fusion[C]∥International Conference on Infromation Fusion.2008:1-6.
[11]LI C,DUANMU J S,LEI Y J,et al.Situation assessment based on cognitive maps and intuitionistic fuzzy reasoning[J].Systems Engineering and Electronics,2012,34(10):2064-2068.(in Chinese) 李闯,端木京顺,雷英杰,等.基于认知图和直觉模糊推理的态势评估方法[J].系统工程与电子技术,2012,34(10):2064-2068.
[12]HUANG Z Q,SHEN C C,DOSHI S,et al.Fuzzy Sets Based Team Decision-Making for Cyber Situation Awareness[C]∥2016 IEEE Military Communications Conference.2016:1077-1082.
[13]CHAI H M,WANG B S.Research on the Bayesian networks model in situation assessment[J].Journal of Xidian University,2009,36(3):140-142.(in Chinese) 柴慧敏,王宝树.态势估计中的贝叶斯网络模型研究[J].西安电子科技大学学报,2009,36(3):140-142.
[14]MENG G L,MA X Y,LIU X,et al.Situation Assessment for Unmanned Aerial Vehicles Air Combat Based on Hybrid Dynamic Bayesian Network[J].Command Control & Simulation,2017,39(4):1-6.(in Chinese) 孟光磊,马晓玉,刘昕,等.基于混合动态贝叶斯网络的无人机空战态势评估[J].指挥控制与仿真,2017,39(4):1-6.
[15]CHAI H N.A Novel Approach to Evidence Combination in Battlefield Situation Assessment Using Dezert-Smarandache Theory[C]∥International Conference on Machine Learning and Cybernetics.2013:720-727.
[16]TANG Y L,LI W J,DING J X,et al.Network security situational assessment method based on improved D-S evidence theory[J].Journal of Nanjing University of Science and Technology,2015,39(4):405-411.(in Chinese) 汤永利,李伟杰,丁金霞,等.基于改进D-S证据理论的网络安全态势评估方法[J].南京理工大学学报,2015,39(4):405-411.
[17]PEARL J.Fusion,propagation and structuring in belief net- works [J].Artificial Intelligence,1986,29(3):241-288.
[18]POURRET O,NAIM P,MARCOT B.Bayesian Networks:A Practical Guide to Applications[J].Treatise on Geochemistry,2008,37(4):281-304.
[19]ZHANG N L ,POOLE D.A simple approach to Bayesian network computations[C]∥Proceedings of the Tenth Canadian Conference on Artificial Intelligence.1994:171-178.
[20]SMARANDACHE F,DEZERT J.An introduction to DSm theory of plausible,paradoxist,uncertain,and imprecise reasoning for information fusion[J].Octogon Mathenatical Magazine,2007,15(2):681-722.
[1] LI Xin, DUAN Yong-cheng. Network Security Situation Assessment Method Based on Improved Hidden Markov Model [J]. Computer Science, 2020, 47(7): 287-291.
[2] MA Hong. Fusion Localization Algorithm of Visual Aided BDS Mobile Robot Based on 5G [J]. Computer Science, 2020, 47(6A): 631-633.
[3] CHEN Jun-ling, QIN Xiao-lin, LI Xing-luo, ZHOU Yang-hao, BAO Bin-guo. Multi-robot Collaborative Obstacle Avoidance Based on Artificial Potential Field Method [J]. Computer Science, 2020, 47(11): 220-225.
[4] HENG Hong-jun, WANG Rui. Long-term Operational Situation Assessment System for Terminal Buildings [J]. Computer Science, 2019, 46(5): 310-314.
[5] LIU Jie, ZHAO Hai-fang and ZHOU De-lian. Improved Quantum Behaved Particle Swarm Optimization Algorithm for Mobile Robot Path Planning [J]. Computer Science, 2017, 44(Z11): 123-128.
[6] WU Chao-xiong, WANG Xiao-cheng, WANG Hong-yan and SHI Bo. Research on Real-time Network Security Situation Assessment Based on Rough Set [J]. Computer Science, 2015, 42(Z6): 435-437.
[7] XU Teng-fei, LUO Qi and WANG Hai. Dynamic Path Planning for Mobile Robot Based on Vector Field [J]. Computer Science, 2015, 42(5): 237-244.
[8] TANG Yi-ping, HU Da-wei, CAI Ying-mei, HUANG Ke and JIANG Rong-jian. Moving Object Detection in Omnidirectional Vision-based Mobile Robot [J]. Computer Science, 2015, 42(11): 314-319.
[9] TANG Cheng-hua,TANG Shen-sheng and QIANG Bao-hua. Assessment and Validation of Network Security Situation Based on DS and Knowledge Fusion [J]. Computer Science, 2014, 41(4): 107-110.
[10] ZHANG He,LIU Guo-liang,LI Nan-jun and HOU Zi-feng. Submap and Adaptive Covariance Based Method for 2D Localization [J]. Computer Science, 2014, 41(10): 23-26.
[11] . Prediction Method for Network Security Situation Based on Elman Neural Network [J]. Computer Science, 2012, 39(6): 61-63.
[12] . Research on Dynamic Obstacle Avoidance and Path [J]. Computer Science, 2012, 39(3): 223-227.
[13] . Mobile Robot Middleware Supporting Self-adaptive Programming [J]. Computer Science, 2012, 39(10): 119-124.
[14] LIAO Zhuo-fan,WANG Jian-xin,LIANG Jun-bin. Dynamic Deployment of Nodes in Wireless Sensor Networks [J]. Computer Science, 2011, 38(10): 45-50.
[15] ZHOU Jing,DAI Guan-zhong,CAI Xiao-yan. Research and Simulating of Global Optimal Path Planning of Mobile Robot Based on Ant Colony System [J]. Computer Science, 2010, 37(5): 171-174.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
[1] LEI Li-hui and WANG Jing. Parallelization of LTL Model Checking Based on Possibility Measure[J]. Computer Science, 2018, 45(4): 71 -75 .
[2] SUN Qi, JIN Yan, HE Kun and XU Ling-xuan. Hybrid Evolutionary Algorithm for Solving Mixed Capacitated General Routing Problem[J]. Computer Science, 2018, 45(4): 76 -82 .
[3] ZHANG Jia-nan and XIAO Ming-yu. Approximation Algorithm for Weighted Mixed Domination Problem[J]. Computer Science, 2018, 45(4): 83 -88 .
[4] WU Jian-hui, HUANG Zhong-xiang, LI Wu, WU Jian-hui, PENG Xin and ZHANG Sheng. Robustness Optimization of Sequence Decision in Urban Road Construction[J]. Computer Science, 2018, 45(4): 89 -93 .
[5] SHI Wen-jun, WU Ji-gang and LUO Yu-chun. Fast and Efficient Scheduling Algorithms for Mobile Cloud Offloading[J]. Computer Science, 2018, 45(4): 94 -99 .
[6] ZHOU Yan-ping and YE Qiao-lin. L1-norm Distance Based Least Squares Twin Support Vector Machine[J]. Computer Science, 2018, 45(4): 100 -105 .
[7] LIU Bo-yi, TANG Xiang-yan and CHENG Jie-ren. Recognition Method for Corn Borer Based on Templates Matching in Muliple Growth Periods[J]. Computer Science, 2018, 45(4): 106 -111 .
[8] GENG Hai-jun, SHI Xin-gang, WANG Zhi-liang, YIN Xia and YIN Shao-ping. Energy-efficient Intra-domain Routing Algorithm Based on Directed Acyclic Graph[J]. Computer Science, 2018, 45(4): 112 -116 .
[9] CUI Qiong, LI Jian-hua, WANG Hong and NAN Ming-li. Resilience Analysis Model of Networked Command Information System Based on Node Repairability[J]. Computer Science, 2018, 45(4): 117 -121 .
[10] WANG Zhen-chao, HOU Huan-huan and LIAN Rui. Path Optimization Scheme for Restraining Degree of Disorder in CMT[J]. Computer Science, 2018, 45(4): 122 -125 .