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Journal of ZheJiang University (Engineering Science)  2026, Vol. 60 Issue (4): 702-711    DOI: 10.3785/j.issn.1008-973X.2026.04.003
    
Local path planning based on an improved time elastic band algorithm
Xin HU1(),Jiazhong ZHANG1,Shuai HU1,Jian XIAO2,*(),Shiwei LUO3,Liang MA3
1. School of Energy and Electrical Engineering, Chang’an University, Xi’an 710064, China
2. School of Electronics and Control Engineering, Chang’an University, Xi’an 710064, China
3. Shaanxi 210 Research Institute Co. Ltd. of China Shaanxi Nuclear Industry Group, Xianyang 712000, China
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Abstract  

Aiming at the problems of sudden changes in the acceleration rate and unsmooth control instructions of the time elastic band (TEB) algorithm in complex environments, an improved TEB algorithm was proposed. Firstly, this algorithm introduced the jerk constraint on the basis of the original TEB algorithm to smooth the velocity and acceleration curves and avoid oscillation and jitter phenomena of the robot during the movement process. Secondly, the method of adaptively adjusting the number of elastic band nodes was adopted to adaptively adjust the interpolation points, which improved the safety and stability of the robot’s movement process. To verify the effectiveness of the improved TEB algorithm, simulation and comparative experiments of the APF algorithm, DWA algorithm, traditional TEB algorithm and improved TEB algorithm were conducted in a long-distance long narrow corridor environment and a multi-turn environment including reverse parking. The results demonstrated that the improved TEB algorithm generated smoother paths. In corridor environments, the variance of linear velocity, the mean angular velocity, and the variance of angular velocity were reduced by 16.67%, 7.38%, and 12.84%, respectively, compared with the traditional TEB algorithm. In multi-turn environments, the corresponding reductions were 8.61%, 4.34%, and 8.58%, respectively. This led to smoother linear and angular velocities with reduced oscillations. Furthermore, the effectiveness of the algorithm has been validated in a real-world experimental environment.



Key wordspath planning      TEB algorithm      mobile robot      dynamic obstacle avoidance      jerk constraint     
Received: 21 May 2025      Published: 19 March 2026
CLC:  TP 242  
Fund:  陕西省秦创原“科学家+工程师”队伍建设项目(2024QCY-KXJ-161);咸阳市重点研发计划资助项目(L2024-ZDYF-ZDYF-GY-0004).
Corresponding Authors: Jian XIAO     E-mail: huxin@chd.edu.cn;xiaojian@chd.edu.cn
Cite this article:

Xin HU,Jiazhong ZHANG,Shuai HU,Jian XIAO,Shiwei LUO,Liang MA. Local path planning based on an improved time elastic band algorithm. Journal of ZheJiang University (Engineering Science), 2026, 60(4): 702-711.

URL:

https://www.zjujournals.com/eng/10.3785/j.issn.1008-973X.2026.04.003     OR     https://www.zjujournals.com/eng/Y2026/V60/I4/702


基于改进时间弹性带算法的局部路径规划

针对时间弹性带(TEB)算法在复杂环境下出现加速度变化率突变、控制指令不平滑的问题,提出改进的TEB算法. 在原始TEB算法基础上引入加加速度(jerk)约束来平滑速度和加速度曲线,避免机器人在运动过程中发生震荡、抖动现象. 采用自适应调整弹性带节点数量的方法来自适应调整插值点,提高机器人移动过程中的安全稳定性. 为了验证改进TEB算法的有效性,选取远距离长狭窄走廊环境和多转弯包含反向停车环境对APF算法、DWA算法、传统TEB算法和改进TEB算法进行仿真对比实验. 结果表明,改进TEB算法能生成更平滑路径. 在走廊环境中,其线速度方差、平均角速度、角速度方差分别较传统TEB降低了16.67%、7.38%、12.84%;在多转弯环境中,则分别降低了8.61%、4.34%、8.58%,速度与角速度更加平滑. 另外,在真实实验环境下验证了算法的有效性.


关键词: 路径规划,  TEB算法,  移动机器人,  动态避障,  加加速度约束 
Fig.1 Motion model of a two-wheel differential robot
Fig.2 Sequence of pose points and time differences
Fig.3 Nonholonomic constraints
Fig.4 Hyper-graph structure of TEB algorithm
Fig.5 Flowchart of TEB algorithm
Fig.6 Schematic diagram of hyper-graph structure with jerk constraint added
参数数值参数数值
$ {v}_{\max } $/ (m?s?1)0.4$ {v}_{\text{bw},\max } $/(m·s?1)0.2
$ {\omega }_{\max } $/(rad·s?1)0.3$ {a}_{\max } $/(m·s?2)0.5
$ {\alpha }_{\max } $/(rad·s?2)0.5$ {j}_{\max } $/(m·s?3)0.5
$ {d}_{\min } $/ m0.5$ {r}_{\text{infl}} $/ m0.6
Tab.1 Simulation parameter settings
Fig.7 Paths and local enlarged views of four algorithms for route 1
Fig.8 Comparison of linear velocities of four algorithms for route 1
Fig.9 Comparison of angular velocities of four algorithms for route 1
算法$ \overline{v} $/(m·s?1)$ \sigma _{v}^{2} $/(m2·s?2)$ \overline{\omega } $/(rad·s?1)$ \sigma _{\omega }^{2} $/(rad2·s?2)
APF0.391 60.003 30.306 10.226 4
DWA0.375 00.004 70.138 40.049 1
传统TEB0.366 00.005 40.171 90.036 6
改进TEB0.382 00.004 50.159 20.031 9
Tab.2 Comparison of linear speed and angular velocity of route 1
Fig.10 Paths and local enlarged views of four algorithms for route 2
Fig.11 Comparison of linear velocities of different algorithms for route 2
Fig.12 Comparison of angular velocities of different algorithms for route 2
算法$ \overline{v} $/(m·s?1)$ \sigma _{v}^{2} $/(m2·s?2)$ \overline{\omega } $/(rad·s?1)$ \sigma _{\omega }^{2} $/(rad2·s?2)
APF0.390 90.003 50.237 10.064 7
DWA0.306 30.021 80.203 40.075 2
传统TEB0.339 80.015 10.188 70.044 3
改进TEB0.332 90.013 80.179 90.040 5
Tab.3 Comparison of linear speed and angular velocity of route 2
Fig.13 Physical testing environment
Fig.14 Comparison of TEB and improved TEB algorithms in a simple environment
Fig.15 Comparison of TEB and improved TEB algorithms in a complex environment
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