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Chinese Journal of Engineering Design  2026, Vol. 33 Issue (3): 359-369    DOI: 10.3785/j.issn.1006-754X.2026.05.174
Robotic and Mechanism Design     
Research on design of building trash bin handling robot
Zhigang LI(),Junpeng ZOU,Xiang YANG
School of Mechatronics and Vehicle Engineering, East China Jiaotong University, Nanchang 330013, China
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Abstract  

Trash cleaning in modern buildings mainly relies on manual labor, which suffers from high intensity and low efficiency. Meanwhile, general mobile robots face issues such as poor path smoothness and low computational efficiency in complex environments like narrow corridors. To address these issues, a building trash bin handling robot was designed. First, the mechanical structure of the robot was designed, consisting of a two-wheel differential drive chassis module, a lifting module, and a servo-driven clamping module to ensure the stable grasping of trash bins. Then, a hierarchical control system was built based on ROS and μC/OS. Furthermore, an improved A* algorithm was proposed, which adopted a hierarchical directional neighborhood search strategy to optimize search efficiency, introduced an adaptive dual-weight heuristic function to avoid local optimal solutions, and utilized a key node extraction strategy to remove redundant nodes, thereby achieving path optimization. Simulation results showed that search time was reduced by up to 44%, and the number of search nodes was reduced by 50%- 80%. The designed robot successfully completed autonomous navigation, inspection, and trash bin handling tasks in prototype testing, verifying its feasibility and effectiveness in building environments.



Key wordsbuilding robot      control system      A* algorithm      path planning     
Received: 25 August 2025      Published: 27 June 2026
CLC:  TP 242.6  
Cite this article:

Zhigang LI,Junpeng ZOU,Xiang YANG. Research on design of building trash bin handling robot. Chinese Journal of Engineering Design, 2026, 33(3): 359-369.

URL:

https://www.zjujournals.com/gcsjxb/10.3785/j.issn.1006-754X.2026.05.174     OR     https://www.zjujournals.com/gcsjxb/Y2026/V33/I3/359


楼宇垃圾桶搬运机器人设计研究

现代楼宇垃圾清理主要依赖人工,劳动强度大、工作效率低,而通用移动机器人在狭窄走廊等复杂环境中存在路径平滑性差、计算效率低等问题。为此,设计了一种楼宇垃圾桶搬运机器人。首先,设计机器人的机械结构,包括由两轮差速驱动的底盘运动模块、提升模块以及由舵机驱动的夹持模块,以保障垃圾桶的稳定抓取。其次,搭建基于ROS与μC/OS操作系统的分层控制系统。进一步地,提出一种改进A*算法,采用分层定向邻域搜索策略提升搜索效率,引入自适应双权重启发函数以规避局部最优解,并利用关键节点提取策略剔除冗余节点,从而实现路径优化。仿真结果表明,改进算法的搜索时间最大降幅达到44%,搜索节点数量减少了50%~80%。所设计的机器人在样机测试中成功完成了自主导航、巡检与垃圾桶搬运任务,验证了其在楼宇环境中的可行性与有效性。


关键词: 楼宇机器人,  控制系统,  A*算法,  路径规划 
参数数值
最大宽度/mm< 700
轮直径/mm> 60
自身质量/kg60
最大装载质量/kg40
最大运行速度/(m·s-1)0.5
最大加速度/(m·s-2)0.25
续航/h> 2
Table 1 Main parameters of robot
Fig.1 Overall structure of building trash bin handling robot
Fig.2 Schematic of drive wheel assembly
Fig.3 Overall structure of chassis motion module
Fig.4 Schematic of clamping drive mechanism
Fig.5 Overall structure of clamping module
Fig.6 Total deformation diagram of clamping module
Fig.7 Equivalent stress diagram of clamping module
Fig.8 Strain and stress diagrams of trash bin
Fig.9 Workflow of building trash bin handling robot
Fig.10 Control system architecture of building trash bin handling robot
Fig.11 Workflow of lower computer communication
Fig.12 Workflow of inspection tasks
Fig.13 Workflow of target recognition and localization tasks
Fig.14 Schematic diagram of 8-neighborhood search
Fig.15 Schematic diagram of 24-neighborhood search
θ/(°)内层搜索邻域
[337.5, 360.0)∪[0, 22.5)7, 8, 9, 13, 16, 17, 18
[22.5, 67.5)8, 9, 12, 13, 16, 17, 18
[67.5, 112.5)7, 9, 12, 13, 16, 17, 18
[112.5, 157.5)7, 8, 12, 13, 16, 17, 18
[157.5, 202.5)7, 8, 9, 12, 16, 17, 18
[202.5, 247.5)7, 8, 9, 12, 13, 16, 17
[247.5, 292.5)7, 8, 9, 12, 13, 16, 18
[292.5, 337.5)7, 8, 9, 12, 13, 17, 18
Table 2 Search neighborhoods corresponding to inner-layer search guidance vectors
QxQy外层扩展邻域
4, 10, 19, 23
10, 19, 21, 23
15, 19, 21, 23
6, 15, 21, 23
2, 6, 15, 21
2, 4, 6, 15
2, 4, 6, 10
2, 4, 10, 19
Table 3 Extended neighborhoods corresponding to outer-layer guidance vectors
地图大小算法搜索时间/ms

路径长度/

栅格数

20×20传统A*68.4830.38
24邻域A*109.1128.78
11邻域A*78.3030.49
30×30传统A*107.8744.53
24邻域A*221.1843.15
11邻域A*146.3944.68
50×50传统A*264.4574.57
24邻域A*518.1272.07
11邻域A*292.8574.01
Table 4 Performance comparison of traditional A*,24-neighborhood A* and proposed 11-neighborhood A* algorithms
Fig.16 Path planning using 11-neighborhood A* algorithm with optimized heuristic function and comparison algorithms in simple environment
Fig.17 Path planning using 11-neighborhood A* algorithm with optimized heuristic function and comparison algorithms in complex environment
地图大小w2对比算法搜索节点数/%搜索时间/%路径长度/%
20×200.1传统A*-76.49-78.47+6.00
11邻域A*-77.78-81.78+7.29
0.4传统A*-51.07-31.78+8.27
11邻域A*-37.12-42.740
50×500.1传统A*-83.45-88.09+3.96
11邻域A*-85.36-90.31+4.43
0.4传统A*-51.13-38.38+4.32
11邻域A*-55.17-54.47+2.33
Table 5 Performance comparison of 11-neighborhood A* algorithm with optimized heuristic function and comparison algorithms in different environments
Fig.18 Flowchart of key node extraction
Fig.19 Planned paths of traditional A* algorithm before and after applying key node extraction strategy
Fig.20 Prototype of building trash bin handling robot
Fig.21 Experimental site
Fig.22 Map of experimental site
Fig.23 Test result of navigation and obstacle avoidance function
Fig.24 Inspection function test
Fig.25 Trash bin handling function test
 
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