[1]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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A mobile robot path planning method based on adaptive DWA and an improved bacteria algorithm

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 18 Number of periods: 2023 2 Page number: 314-324 Column: 学术论文—智能系统 Public date: 2023-05-05
Title:
A mobile robot path planning method based on adaptive DWA and an improved bacteria algorithm
Author(s):
PU Xingcheng1; 2;  TAN Ling1
1. School of Automation, Chongqing University of Posts and Telecommunications, Chongqing 400065, China;
2. School of Mathematics and Computer Science, Tongling University, Tongling 244061, China
Keywords:
complex environment; robot; bacterial algorithm; self-adaptive; dynamic window algorithm; referential path; local dynamic avoiding obstacles; path planning
CLC:
TP273
DOI:
10.11992/tis.202112014
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.
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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