MoMa-Pos: Where Should Mobile Manipulators Stand in Cluttered Environment
Before Task Execution?

Submitted to IROS 2024

Beichen Shao1, Yan Ding2, Xingchen Wang1,
Xuefeng Xie3, Fuqiang Gu1, Jun Luo4, Chao Chen4,
1College of Computer Science, Chongqing University 2Binghamton University 3School of Management Science and Real Estate, Chongqing University 4State Key Laboratory of Mechanical Transmission for Advanced Equipment, Chongqing University
ArXiv Code

Abstract

Mobile manipulators always need to determine feasible base positions prior to carrying out navigation-manipulation tasks. Real-world environments are often clut tered with various furniture, obstacles, and dozens of other objects. Efficiently computing base positions poses a challenge. In this work, we introduce a framework named MoMa-Pos to address this issue. MoMa-Pos first learns to predict a small set of objects that, taken together, would be sufficient for finding base positions using a graph embedding architecture

MoMa-Pos then calculates standing positions by considering furniture structures, robot models, and obstacles comprehensively. We have extensively evaluated the proposed MoMa-Pos across different settings (e.g., environment and algorithm parameters) and with various mobile manipulators. Our empirical results show that MoMa-Pos demonstrates remarkable effectiveness and efficiency in its performance, surpassing the methods in the literature.

Framework of MoMa-Pos

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The MoMa-Pos framework provides an overview that includes three key components: Predicting Object Importance for Modeling, Computing Potential Base Position Area, and Identifying a Feasible Base Position
⁎ The red area represents the planning of the robot's path using candidate grasp points.
⁎ The green area The green area represents the identification of a feasible base position.
⁎ The yellow area represents the simplification of the environment.

Experiments

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In our experiments, we compared the performance of MoMa-Pos (our method) with three baseline methods across six different specific scenarios, recording the time required to complete tasks and the specific distance traveled. In each specific scenario, results were tested at different positions. Ultimately, the method that required the least amount of time and traveled the shortest distance to complete tasks performed the best.

Video Demos

You can view the MoMa-Pos performance under scenarios.

Conclusion and Limitations

In this work, we demonstrated that MoMa-Pos effectively identifies feasible positions for navigation-manipulation tasks. We conclude by acknowledging the limitations of our study, reflecting on the implications of our findings, and highlighting opportunities for future research. A significant limitation we encountered is the lack of discussion on the performance of MoMa-Pos with inaccurate simulation models. In the real world, encountering inaccurate models is common, despite the remarkable advancements in computer vision and 3D modeling technologies. We have not explored whether MoMa-Pos’s performance, particularly in terms of task execution time, would deteriorate under such conditions. Theoretically, MoMa-Pos should still be able to find feasible positions, as we have demonstrated its completeness. Another constraint of the current framework is the inability of certain critical parameters, such as α, to adapt automatically. Moving forward, we aim to address these limitations to enhance MoMa-Pos’s performance further.

We have shown that MoMa-Pos effectively identifies viable positions for navigation-manipulation tasks. Acknowledging the limitations of our research, we reflect on our findings' implications and highlight future research opportunities. One notable limitation is the absence of analysis on MoMa-Pos's performance with imprecise simulation models, a common issue despite advances in computer vision and 3D modeling. We have yet to investigate if MoMa-Pos's task execution time is adversely affected under such conditions. However, our results suggest MoMa-Pos can reliably identify feasible positions. Another limitation is the framework's inability to automatically adjust critical parameters, like α. Future efforts will focus on overcoming these challenges to improve MoMa-Pos's efficacy.

BibTeX

@article{shao2024integrating,
  title={MoMa-Pos: Where Should Mobile Manipulators Stand in Cluttered Environment Before Task Execution?},
  author={Shao, Beichen and Ding, Yan and Wang, Xingchen and Xie, Xuefeng and Gu, Fuqiang and Luo, Jun and Chen, Chao},
  journal={arXiv preprint arXiv:2403.19940},
  year={2024}
}