Planning for a Tight Squeeze: Navigation of Morphing Soft Robots in Congested Environments

Edward Gough*, Andrew T. Conn, Jonathan Rossiter

*Corresponding author for this work

Research output: Contribution to journalArticle (Academic Journal)peer-review

6 Citations (Scopus)
152 Downloads (Pure)


Autonomous navigation methods can prevent robots becoming trapped between obstacles and ensure that they take the most efficient route. As mobile robots have a limited power supply, selecting the most efficient route is crucial. This letter presents a path-planning method for morphing soft robots in congested environments. The proposed method is suitable for all scales of robots and environments, from intra-organ biomedical navigation to search-and-rescue operations in cave networks. The method utilizes 3D Voronoi diagrams and Dijkstra's algorithm to calculate an optimal path that balances cost between the size and shape change of the robot and the length of the path. The Voronoi method is particularly suitable for circumferentially expanding robots because the waypoints generated lay where a device with a circular cross-section would naturally sit. The method is demonstrated by simulation in procedurally generated environments with either spherical or continuous obstacles to illustrate the effectiveness of the method for in-situ planning and as an aid to design. This letter provides a generic approach that has the potential to be easily adapted for many applications across healthcare, industry and space exploration.
Original languageEnglish
Pages (from-to)4752-4757
Number of pages6
JournalIEEE Robotics and Automation Letters
Issue number3
Publication statusPublished - 19 Mar 2021

Bibliographical note

Funding Information:
Manuscript received October 15, 2020; accepted February 20, 2021. Date of publication March 19, 2021; date of current version April 13, 2021. This letter was recommended for publication by Associate Editor C. Onal and Editor C. Laschi upon evaluation of the reviewers. comments. The work of Edward Gough was supported in part by the EPSRC Centre for Doctoral Training in Future Autonomous, and Robotic Systems (FARSCOPE), and in part by BT Applied Research through the EPSRC iCASE scheme. The work of Jonathan Rossiter was supported in part by through EPSRC research under Grants EP/S026096/1, EP/R02961X/1, and EP/M020460/1, and by in part by the Royal Academy of Engineering as a Chair in Emerging Technologies. The work of Andrew T. Conn was supported by EPSRC under Grant EP/R02961X/1. (Corresponding author: Edward Gough.) Edward Gough is with the FARSCOPE CDT, Bristol Robotics Laboratory, Bristol BS34 8QZ, U.K. (e-mail:

Publisher Copyright:
© 2016 IEEE.


  • Learning for soft robots
  • control
  • modeling
  • motion and path planning


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