cs.RODec 22, 2025

Collaborative Continuum Robots: A Survey from an Automation-System Perspective

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

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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, flexibility and load capacity can be further improved, thus offering significant advantages. In recent years, interest in this emerging field has grown steadily within the continuum-robotics community. In this survey, a closed-loop task-execution architecture for CCRs is constructed from an automation-system perspective, clarifying the system-level logic that transforms target-task specifications into multi-CR collaborative execution. Based on this architecture, CCRs are classified into three representative collaboration modes, namely separated collaboration, assistance collaboration, and parallel collaboration. Research progress is then systematically summarized and analyzed in terms of key technologies, including multi-CR task allocation, structural design, modeling, motion planning methods, control strategies, and sensing mechanisms. Finally, research opportunities in key technologies and system integration challenges for real-world deployment are discussed, providing guidance for research and application of CCRs.

Figures & tables

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) Pyramid-type actuation [ 22 ] . (c) Rigid guiding structures [ 23 ] . (d) Modular guiding structures [ 17 ] . (e) Flexible guiding structures [ 24 ] . (f) Flexible guiding structures [ 25 ] . (g) CCR-based legged robot [ 26 ] . (h) Multiple fluid-driven CRs [ 27 ] . (i) Logarithmic spiral-based structure [ 28 ] . (j) Dexterous hand [ 29 ] . (k) Inspired by Angel Oak tree growth [ 20 ] . (l) Multi-port design [ 30 ] . (m) Shape-memory alloy connectors [ 31 ] . (n) Multi-finger spherical gripper [ 32 ] . (o) Reconfigurable design [ 19 ] . (p) Alternating follower motion [ 33 ] . (q) Planar configuration with CRs [ 34 ] . (r) Planar configuration with CRs and rigid links [ 21 ] . (s) Spatial configuration with CRs [ 35 ] . (t) Spatial configuration with high load capacity [ 10 ] . (u) Composed of CR joints [ 36 ] . (v) Antagonistic configuration [ 37 ] .
Ref.YearReview TitleCCR-Related ContentApplicationRelevance to CCR
[ 43 ]2018Medical technologies and challenges of robot-assisted minimally invasive intervention and diagnosticsSC: S-M-CSurgeryH
[ 47 ]2020In the soft grip of natureSC: SGraspingL
[ 41 ]2021Research progress and development trend of surgical robot and surgical instrument armSC: SSurgeryM
[ 42 ]2022Continuum robots for medical interventionsSC: SSurgeryL
[ 44 ]2022Continuum robots for endoscopic sinus surgery: Recent advances, challenges, and prospectsSC: S-P-CSurgeryH
[ 48 ]2023Current designs of robotic arm grippers: A comprehensive systematic reviewSC: SGraspingL
Table I: Summary of Related Reviews for CCRs
Figure 3: Closed-loop task-execution architecture of CCRs from an automation-system perspective.
ModeRef.ActuationApplication
SC[ 9 ] , [ 18 ] , [ 23 ] , [ 25 ] , [ 72 ] , [ 74 ] , [ 77 ] , [ 87 ] , [ 85 ] , [ 62 ] , [ 88 ]Concentric tubeSU
 [ 16 ] , [ 17 ] , [ 28 ] , [ 26 ] , [ 69 ] , [ 75 ] , [ 76 ] , [ 78 ] , [ 79 ] , [ 82 ] , [ 84 ] , [ 89 ]TendonSU-MA-GR-LO
 [ 90 ] , [ 81 ]RodSU
 [ 6 ] , [ 27 ] , [ 91 ] , [ 92 ] , [ 93 ] , [ 70 ] , [ 94 ] , [ 95 ]PneumaticGR-LO
 [ 96 ]HydraulicGR
 [ 97 ] , [ 98 ]OrigamiGR
Table II: Comparison of Collaborative Modes for CCRs: Actuation and Applications
ModeNo. of CRsPhysical couplingConnection typeRole relationshipSystem end-effectorIntegration complexityLoad- bearingEnd-effector flexibilityInterference risk
SC≥2\geq 2NoneNonePeer/non-peerIndividual CR tipsLLMH
AC≥2\geq 2Side connection to the main armFixed/ detachableNon-peerMain arm tipMMHM
PC≥2\geq 2Distal connection to a moving platformFixedPeerMoving platformHHLL
Table III: Structural and performance comparison of collaboration modes for CCRs
Figure 4: 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.
ModeRef.Modeling methodsConstraint typesModel typeConstraint extensibilityI. K. solutionComputational costNumerical robustnessεpos\boldsymbol{\varepsilon}_{\mathrm{pos}}εori\boldsymbol{\varepsilon}_{\mathrm{ori}}
SC[ 69 ]PCCNoneKinematicNANumericalNRH\mathrm{H}0.463%NR
 [ 84 ]CCNoneKinematicNAAnalyticalNRH\mathrm{H}NRNR
 [ 76 ]PCCNoneKinematicNANRNRNRNRNR
 [ 7 ]PCCNoneKinematicNANumericalNRH\mathrm{H}0.425%NR
 [ 114 ]PCCNoneKinematicNANumericalNRH\mathrm{H}0.333%NR
 [ 60 ]CCGCKinematicM\mathrm{M}Semi-analyticalNRH\mathrm{H}1.200%NR
Table IV: Comparison of modeling methods for CCRs
Figure 5: Motion planning methods for CCRs. (a) End-effector planning. (b) Conforming planning. (c) Avoidance planning. (d) Real-time planning.
Figure 6: Control of CCRs. (a) Multi-task-driven control strategy. (b) Sequential control strategy. (c) Hierarchical control strategy. (d) Switching control strategy.
ModeRef.Sensing mech.Open/ClosedControl strategyControl frequencyεpos\boldsymbol{\varepsilon}_{\mathrm{pos}}εori\boldsymbol{\varepsilon}_{\mathrm{ori}}εfor\boldsymbol{\varepsilon}_{\mathrm{for}}
SC[ 74 ]VSOpenMaster-slaveNRNRNRNR
[ 78 ]VS-FS-ESOpenMaster-slaveNRNRNRNR
[ 75 ]VS-ESOpenMaster-slaveNR0.050% ∼\sim 0.280%0.573%NR
[ 128 ]VS-FSOpenMaster-slaveNRNRNRNR
[ 17 ]VSOpenMaster-slaveNR1.340%NRNR
[ 59 ]VSClosedSequential10 HzNR0.430% ∼\sim 0.462%NR
Table V: Comparison of control strategies for CCRs
Figure 7: Sensor arrangement configurations. (a) Eye-to-hand. (b) Eye-in-hand. (c) Vision–IMU. (d) External magnetic sensing. (e) Distributed magnetic sensing. (f) Magnetic–FBG sensing.
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