[1]蒲兴成,谭令.基于自适应动态窗口改进细菌算法与移动机器人路径规划[J].智能系统学报,2023,18(2):314-324.[doi:10.11992/tis.202112014]
PU Xingcheng,TAN Ling.A mobile robot path planning method based on adaptive DWA and an improved bacteria algorithm[J].CAAI Transactions on Intelligent Systems,2023,18(2):314-324.[doi:10.11992/tis.202112014]
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PU Xingcheng,TAN Ling.A mobile robot path planning method based on adaptive DWA and an improved bacteria algorithm[J].CAAI Transactions on Intelligent Systems,2023,18(2):314-324.[doi:10.11992/tis.202112014]
基于自适应动态窗口改进细菌算法与移动机器人路径规划
《智能系统学报》[ISSN 1673-4785/CN 23-1538/TP] 卷:
18
期数:
2023年第2期
页码:
314-324
栏目:
学术论文—智能系统
出版日期:
2023-05-05
- Title:
- A mobile robot path planning method based on adaptive DWA and an improved bacteria algorithm
- Keywords:
- complex environment; robot; bacterial algorithm; self-adaptive; dynamic window algorithm; referential path; local dynamic avoiding obstacles; path planning
- 分类号:
- TP273
- 摘要:
- 针对移动机器人在复杂环境下的路径规划问题，提出一种新的自适应动态窗口改进细菌算法，并将新算法应用于移动机器人路径规划。改进细菌算法继承了细菌算法与动态窗口算法（dynamic window algorithm, DWA）在避障时的优点，能较好实现复杂环境中移动机器人静态和动态避障。该改进算法主要分三步完成移动机器人路径规划。首先，利用改进细菌趋化算法在静态环境中得到初始参考规划路径。接着，基于参考路径，机器人通过自身携带的传感器感知动态障碍物进行动态避障并利用自适应DWA完成局部动态避障路径规划。最后，根据移动机器人局部动态避障完成情况选择算法执行步骤，如果移动机器人能达到最终目标点，结束该算法，否则移动机器人再重回初始路径，直至到达最终目标点。仿真比较实验证明，改进算法无论在收敛速度还是路径规划精确度方面都有明显提升。
- Abstract:
- To solve the problem of mobile robot path planning in complex environments, a novel path planning method based on enhanced bacterial chemotaxis and an adaptive dynamic window algorithm is proposed. In addition to inheriting the benefits of bacterial chemotaxis and the dynamic window algorithm in avoiding obstacles, the novel method can also be used to avoid static or dynamic obstacles for mobile robots. It is typically divided into three steps when employed for mobile robot path planning. First, an initial referential planning path in a static environment is determined using the enhanced bacterial chemotaxis algorithm. Next, based on the reference path, the robot detects and avoids dynamic obstacles using its own sensors and completes path planning for local dynamic obstacle avoidance using adaptive DWA. Ultimately, the mobile robot chooses the next execution step based on the outcome of avoiding dynamic obstacles. If the robot is able to reach the final object point, the algorithm stops. Otherwise, the robot will return to the initial path until the final object point is reached. The comparison of simulation and experiment results demonstrates that the algorithm’s convergence speed and accuracy of path planning have been significantly enhanced.
- 参考文献/References:
-
[1] 杜永浩, 邢立宁, 蔡昭权. 无人飞行器集群智能调度技术综述[J]. 自动化学报, 2020, 46(2): 222–241
DU Yonghao, XING Lining, CAI Zhaoquan. Survey on intelligent scheduling technologies for unmanned flying craft clusters[J]. Acta automatica sinica, 2020, 46(2): 222–241
[2] 盛亮, 包磊, 吴鹏飞. 启发式方法在机器人路径规划优化中的应用综述[J]. 电光与控制, 2018, 25(9): 58–64
SHENG Liang, BAO Lei, WU Pengfei. Application of heuristic approaches in the robot path planning and optimization: a review[J]. Electronics optics & control, 2018, 25(9): 58–64
[3] LUO Qiang, WANG Haibao, ZHENG Yan, et al. Research on path planning of mobile robot based on improved ant colony algorithm[J]. Neural computing and applications, 2020, 32(6): 1555–1566.
[4] 孙功武, 苏义鑫, 顾轶超, 等. 基于改进蚁群算法的水面无人艇路径规划[J]. 控制与决策, 2021, 36(4): 847–856
SUN Gongwu, SU Yixin, GU Yichao, et al. Path planning for unmanned surface vehicle based on improved ant colony algorithm[J]. Control and decision, 2021, 36(4): 847–856
[5] GAO Wenxiang, TANG Qing, YE Beifa, et al. An enhanced heuristic ant colony optimization for mobile robot path planning[J]. Soft computing, 2020, 24(8): 6139–6150.
[6] MIAO Changwei, CHEN Guangzhu, YAN Chengliang, et al. Path planning optimization of indoor mobile robot based on adaptive ant colony algorithm[J]. Computers & industrial engineering, 2021, 156: 107230.
[7] 曹新亮, 王智文, 冯晶, 等. 基于改进蚁群算法的机器人全局路径规划研究[J]. 计算机工程与科学, 2020, 42(3): 564–570
CAO Xinliang, WANG Zhiwen, FENG Jing, et al. Global path planning of robots based on improved ant colony algorithm[J]. Computer engineering & science, 2020, 42(3): 564–570
[8] LI Peng, ZHU Hua. Parameter selection for ant colony algorithm based on bacterial foraging algorithm[J]. Mathematical problems in engineering, 2016, 2016: 6469721.
[9] 蒲兴成, 李俊杰, 吴慧超, 等. 基于改进粒子群算法的移动机器人多目标点路径规划[J]. 智能系统学报, 2017, 12(3): 301–309
PU Xingcheng, LI Junjie, WU Huichao, et al. Mobile robot multi-goal path planning using improved particle swarm optimization[J]. CAAI transactions on intelligent systems, 2017, 12(3): 301–309
[10] ZHANG Yi, GUAN Guolun, PU Xingchen. The robot path planning based on improved artificial fish swarm algorithm[J]. Mathematical problems in engineering, 2016, 2016: 3297585.
[11] MUNI M K, PARHI D R, KUMAR P B. Improved motion planning of humanoid robots using bacterial foraging optimization[J]. Robotica, 2021, 39(1): 123–136.
[12] LIANG Xiaodan, LI Liangyu, WU Jigang, et al. Mobile robot path planning based on adaptive bacterial foraging algorithm[J]. Journal of Central South University, 2013, 20(12): 3391–3400.
[13] PANG Bao, SONG Yong, ZHANG Chengjin, et al. Bacterial foraging optimization based on improved chemotaxis process and novel swarming strategy[J]. Applied intelligence, 2019, 49(4): 1283–1305.
[14] LIAN Jianfang, YU Wentao, XIAO Kui, et al. Cubic spline interpolation-based robot path planning using a chaotic adaptive particle swarm optimization algorithm[J]. Mathematical problems in engineering, 2020, 2020: 1849240.
[15] 李元, 王石荣, 于宁波. 基于RGB-D信息的移动机器人SLAM和路径规划方法研究与实现[J]. 智能系统学报, 2018, 13(3): 445–451
LI Yuan, WANG Shirong, YU Ningbo. RGB-D-based SLAM and path planning for mobile robots[J]. CAAI transactions on intelligent systems, 2018, 13(3): 445–451
[16] CHANG Lu, SHAN Liang, JIANG Chao, et al. Reinforcement based mobile robot path planning with improved dynamic window approach in unknown environment[J]. Autonomous robots, 2021, 45(1): 51–76.
[17] HENKEL C, BUBECK A, XU Weiliang. Energy efficient dynamic window approach for local path planning in mobile service robotics[J]. IFAC-papersonline, 2016, 49(15): 32–37.
[18] JIN Qibing, TANG Chuning, CAI Wu. Research on dynamic path planning based on the fusion algorithm of improved ant colony optimization and rolling window method[J]. IEEE access, 2021, 10: 28322–28332.
[19] CHEN Zheng, ZHANG Youming, ZHANG Yougong, et al. A hybrid path planning algorithm for unmanned surface vehicles in complex environment with dynamic obstacles[J]. IEEE access, 2019, 7: 126439–126449.
[20] 黄辰. 基于智能优化算法的移动机器人路径规划与定位方法研究[D]. 大连: 大连交通大学, 2018: 1?123.
HUANG Chen. Study on path planning and location of mobile robot based on intelligent optimization algorithm[D]. Dalian: Dalian Jiaotong University, 2018: 1?123.
[21] 蒲兴成, 赵红全, 张毅. 细菌趋化行为的移动机器人路径规划[J]. 智能系统学报, 2014, 9(1): 69–75
PU Xingcheng, ZHAO Hongquan, ZHANG Yi. Mobile robot path planning research based on bacterial chemotaxis[J]. CAAI transactions on intelligent systems, 2014, 9(1): 69–75
[22] 封硕, 吉现友, 程博, 等. 融合动态障碍物运动信息的路径规划算法[J]. 计算机工程与应用, 2021, 1(1): 1–8
FENG shuo, JI xianyou, CHENG bo, et al. Path planning algorithm based on dynamic obstacle movement information[J]. Computer engineering and application., 2021, 1(1): 1–8
[23] AJEIL F H, IBRAHEEM I K, AZAR A T, et al. Grid-based mobile robot path planning using aging-based ant colony optimization algorithm in static and dynamic environments[J]. Sensors (Basel, Switzerland), 2020, 20(7): 1880.
[24] WANG Lanfei, KAN Jiangming, GUO Jun, et al. 3D path planning for the ground robot with improved ant colony optimization[J]. Sensors (Basel, Switzerland), 2019, 19(4): 815.
[25] TULLU A, ENDALE B, WONDOSEN A, et al. Machine learning approach to real-time 3D path planning for autonomous navigation of unmanned aerial vehicle[J]. Applied sciences, 2021, 11(10): 4706.
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[1]方勇纯.机器人视觉伺服研究综述[J].智能系统学报,2008,3(2):109.
FANG Yong-chun.A survey of robot visual servoing[J].CAAI Transactions on Intelligent Systems,2008,3():109.
[2]王立权,刘秉昊,吴健荣,等.6R关节型机器人运动学建模[J].智能系统学报,2010,5(2):156.
WANG Li-quan,LIU Bing-hao,WU Jian-rong,et al.Modeling and implementing the inverse kinematics ofa six revolute joint robot[J].CAAI Transactions on Intelligent Systems,2010,5():156.
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PU Xingcheng,ZHANG Jun,ZHANG Yi.Modified behavior coordination for intelligent wheelchair path planning based on a neural network[J].CAAI Transactions on Intelligent Systems,2011,6():456.
[5]李晓飞,刘宏.机器人听觉声源定位研究综述[J].智能系统学报,2012,7(1):9.
LI Xiaofei,LIU Hong.A survey of sound source localization for robot audition[J].CAAI Transactions on Intelligent Systems,2012,7():9.
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SUN Fengchi,SONG Meng,LIU Guang.An adaptive ranging model based on energy distance loss of wireless sensors[J].CAAI Transactions on Intelligent Systems,2012,7():214.
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SUN Yuxiang,CAO Huibin,FENG Yong,et al.Structure design on a legwheeledintegration robotwith an earthmoving function[J].CAAI Transactions on Intelligent Systems,2012,7():409.
[8]伍明,李琳琳,李承剑.基于协方差交集的多机器人协作目标跟踪算法[J].智能系统学报,2013,8(1):66.[doi:10.3969/j.issn.1673-4785.201204022]
WU Ming,LI Linlin,LI Chengjian.An algorithm of multi robot cooperative object tracking based on covariance intersection[J].CAAI Transactions on Intelligent Systems,2013,8():66.[doi:10.3969/j.issn.1673-4785.201204022]
[9]肖国宝,严宣辉.一种基于改进Theta *的机器人路径规划算法[J].智能系统学报,2013,8(1):58.[doi:10.3969/j.issn.1673-4785.201208032]
XIAO Guobao,YAN Xuanhui.A path planning algorithm based on improved Theta * for mobile robot[J].CAAI Transactions on Intelligent Systems,2013,8():58.[doi:10.3969/j.issn.1673-4785.201208032]
[10]李大伟,贾鹏飞,李卫国,等.一种基于卡尔曼滤波与模糊算法的变电站机器人组合导航及控制系统设计[J].智能系统学报,2013,8(3):226.
LI Dawei,JIA Pengfei,LI Weiguo,et al.A kind of integrated navigation and control system design for substation robot based on the Kalman filtering and fuzzy algorithm[J].CAAI Transactions on Intelligent Systems,2013,8():226.
备注/Memo
收稿日期:2021-12-07。
基金项目:国家自然科学基金项目（61876200）；重庆市教委科研项目（KJZD-M202001901）
作者简介:蒲兴成,教授,博士,主要研究方向为多智能体系统、群智能算法和随机系统。主持和参与市级以上科研项目10余项,出版学术著作和教材各2部,发表学术论文50余篇;谭令,硕士研究生,主要研究方向为不确定环境下移动机器人路径规划
通讯作者:蒲兴成. E-mail: 1662598286@qq.com
更新日期/Last Update:
1900-01-01