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Collaborative Continuum Robots: A Survey

Xinyu Li, Qian Tang, Guoxin Yin, Gang Zheng, Jessica Burgner-Kahrs, Cesare Stefanini, Ke Wu

TL;DR

This survey defines three collaboration modes for collaborative continuum robots (CCR): separated, assistance, and parallel, and synthesizes advances across structural design, modeling, motion planning, and control. It highlights the trade-offs between task versatility, load capacity, and system complexity, and discusses how coupling constraints evolve to improve coordination while managing collisions. The authors call for intelligent structural design, physics-informed data-driven modeling, and reinforcement-learning–based planning/control to address current challenges and accelerate deployment in rehabilitation, emergency response, and agriculture. Overall, CCRs promise expanded workspace, higher dexterity, and improved stability over single CRs, aiming for practical real-world impact in complex, dynamic environments.

Abstract

Continuum robots (CRs), owing to their compact structure, inherent compliance, and flexible deformation, have been widely applied in various fields. By coordinating multiple CRs to form collaborative continuum robots (CCRs), task adaptability, workspace, flexibility, load capacity, and operational stability can be further improved, thus offering significant advantages. In recent years, interest in this emerging field has grown steadily within the continuum-robotics community, accompanied by a consistent rise in related publications. By presenting a comprehensive overview of recent progress from different system-architecture levels, this survey provides a clear framework for research on CCRs. First, CCRs are classified into the three collaboration modes of separated collaboration, assistance collaboration, and parallel collaboration, with definitions provided. Next, advances in structural design, modeling, motion planning, and control for each mode are systematically summarized. Finally, current challenges and future opportunities for CCRs are discussed.

Collaborative Continuum Robots: A Survey

TL;DR

This survey defines three collaboration modes for collaborative continuum robots (CCR): separated, assistance, and parallel, and synthesizes advances across structural design, modeling, motion planning, and control. It highlights the trade-offs between task versatility, load capacity, and system complexity, and discusses how coupling constraints evolve to improve coordination while managing collisions. The authors call for intelligent structural design, physics-informed data-driven modeling, and reinforcement-learning–based planning/control to address current challenges and accelerate deployment in rehabilitation, emergency response, and agriculture. Overall, CCRs promise expanded workspace, higher dexterity, and improved stability over single CRs, aiming for practical real-world impact in complex, dynamic environments.

Abstract

Continuum robots (CRs), owing to their compact structure, inherent compliance, and flexible deformation, have been widely applied in various fields. By coordinating multiple CRs to form collaborative continuum robots (CCRs), task adaptability, workspace, flexibility, load capacity, and operational stability can be further improved, thus offering significant advantages. In recent years, interest in this emerging field has grown steadily within the continuum-robotics community, accompanied by a consistent rise in related publications. By presenting a comprehensive overview of recent progress from different system-architecture levels, this survey provides a clear framework for research on CCRs. First, CCRs are classified into the three collaboration modes of separated collaboration, assistance collaboration, and parallel collaboration, with definitions provided. Next, advances in structural design, modeling, motion planning, and control for each mode are systematically summarized. Finally, current challenges and future opportunities for CCRs are discussed.
Paper Structure (26 sections, 12 equations, 5 figures, 4 tables)

This paper contains 26 sections, 12 equations, 5 figures, 4 tables.

Table of Contents

  1. Introduction
  2. Structure design of CCRs
  3. Separated collaboration
  4. Assistance collaboration
  5. Parallel collaboration
  6. Summary
  7. Modeling of CCRs
  8. Modeling strategies
  9. Formulation of coupling constraints
  10. Separate collaboration
  11. Assistance collaboration
  12. Parallel collaboration
  13. Summary
  14. Motion planning and control of CCRs
  15. Motion planning of CCRs
  16. ...and 11 more sections

Figures (5)

  • Figure 1: Development of representative collaborative robots. (a) YuMi dual-arm robot [13]. (b) Da Vinci surgical robot [12]. (c) The Hong Kong Polytechnic University R&D surgical robot [14]. (d) Waseda University R&D surgical robot [15] . (e) University of Nottingham R&D repair CCR [16]. (f) Shanghai Jiao Tong University R&D surgical CCR [17].
  • Figure 2: Classification and representative examples of CCRs. (a) Dual-arm concentric tube robots 18. (b) Rigid guiding structures 20. (c) Modular guiding structures 90. (d) Flexible guiding structures 89. (e) Pyramid-type actuation 27. (f) Without flexible guiding structures 11. (g) Dexterous hand 103. (h) Multiple fluid-driven CRs 111. (i) Logarithmic spiral-based structure 81. (j) CCR-based legged robot 96. (k) Inspired by Angel Oak tree growth 57. (l) Multi-port design 66. (m) Shape-memory alloy connectors 70. (n) Multi-finger spherical gripper 125. (o) Reconfigurable design 60. (p) Alternating follower motion 88. (q) Planar configuration with CRs 78. (r) Planar configuration with CRs and rigid links 73. (s) Spatial configuration with CRs 121. (t) Spatial configuration with high load capacity 119. (u) Composed of CR joints 58. (v) System architecture levels of CCRs. (w) Publication statistics on CCR-related studies.
  • Figure 3: Coupling modeling strategies for CCRs. (a) Operating spaces of coupling modeling strategies. (b) Schematic of the PCC model. (c) Schematic of the Cosserat rod theory. (d) Formulation of coupling constraints for separated collaboration. (e) Formulation of coupling constraints for assistance collaboration. (f) Formulation of coupling constraints for parallel collaboration.
  • Figure 4: Motion planning methods for CCRs. (a) End-effector planning. (b) Conforming planning. (c) Avoidance planning. (d) Real-time planning.
  • Figure 5: Control of CCRs. (a) Sensor arrangement configurations. (b) Sequential control strategy. (c) Hierarchical control strategy. (d) Switching control strategy.