Continuum arm aerial manipulation systems leverage soft-manipulator compliance and dexterity for tasks in confined or hazardous environments, but propeller downwash can degrade performance, particularly near walls and the ground. This effect remains uncharacterized for continuum manipulators. This letter experimentally studies downwash-induced kinematic deviations of a tendon-driven continuum manipulator integrated with a multirotor platform. Under still-air conditions, the CM is compared with a constant-curvature (CC) model. Downwash- induced end-effector pose deviations are then quantified relative to the mean still-air experimental baseline at four propeller throttle levels in free space, and at maximum throttle near a wall, and near the ground. Vertical position and yaw show the largest deviations and are amplified by ground effect. A CC-guided Gaussian process regression (GPR) residual model is learned from experimental data that improves forward pose prediction RMSE (position by 89-95%, orientation by 47-79%), and support compensation-oriented, downwash-aware modeling of continuum arm aerial manipulation systems.
Figures & tables
Fig. 1 : (a) The mechanical and (b) electrical system architectures of the CAAMS used for the experimental study.
Fig. 2 : (a) CAAMS with the CC workspace and frames: world {W} , UAV {U} , CM base {B} , and CM tip {E} . The inset triangle marks cables C1–C3. (b) Workspace sampling: the 3D workspace is divided into four horizontal slices (HS1–HS4) and 12 vertical slices (V1–V12) about ZB , whose intersections define points P1 – P48 . The circular panel shows HS1 in top view with dashed vertical slices, propellers and Dashed-yellow shaded boxes: Airframe attachments causing flow disturbance. (c) Definition of position and orientation error between the expected {ref} and measured {exp} pose in {B} .
Fig. 3 : Bench-top experiment setups for (a)free-space (b) wall effect where a board is placed next to the CAAMS, and (c) ground effect where the UAV is close to a horizontal board.
Experiment
Physical interpretation
Throttle levels T%
Case(i)
No DW
0
Case(ii)
DW; No influence of wall/ground
25, 50, 75, 100
Case(iii)
DW under the influence of wall
100
Case(iv)
DW under the influence of ground
100
TABLE I : Bench-top experiments under different cases
Fig. 4 : Case (i) – Euclidean ( epos ) and Cartesian EE position error components across slices with propellers off ( T=0% )
Fig. 5 : Case (i) – scalar quaternion errors ( eori ) and the extrinsic Euler components of EE orientation with ( T=0% )
Fig. 6 : EE errors of Euclidean position epos and scalar quaternion orientation e∘ relative to mean at T=0% in Cases (ii)–(iv).
Fig. 7 : Driving motors’ (M1,M2 & M3) torques when operating in V1, V5 and V9 planes at T=0% and T=100% .
Case
Position RMSE (mm)
Orientation RMSE ( ∘ )
Pos. imp. (%)
Ori. imp. (%)
T0
124.56 → 8.63
28.40 → 10.86
93.00
62.42
T25
131.52 → 14.51
24.69 → 5.80
90.28
79.72
T50
130.81 → 11.94
35.20 → 18.92
91.15
47.56
T75
132.11 → 10.36
26.66 → 11.29
92.17
58.64
T100
130.90 → 14.68
24.15 → 6.72
89.86
76.77
TABLE II : Held-out RMSE of the raw CC prior and the GP-corrected predictor across still-air and DW cases.
Fig. 8 : Test points’ position errors (mean ± SD) of the raw CC prior and the GP-corrected predictor for T0 – T100 .
Fig. 9 : Test points’ orientation errors (mean ± SD) of the raw CC prior and the GP-corrected predictor for T0 – T100 .
Continuum manipulators mounted on multi-rotor UAVs enable compliant aerial manipulation, but payloads and propeller downwash amplify out-of-plane bending and twisting that degrade end-effector pose accuracy. To address this problem, we present a universal-joint-based continuum manipulator designed to improve resistance to out-of-plane deformation during aerial manipulation. The proposed design uses a tubular backbone with spring-reinforced universal joints and an integrated conduit for internal routing and fluid delivery. We evaluate the design in still air and under peak propeller downwash across varying payloads, and benchmark it against a prior Nitinol-backbone CM. Bench tests show improved resistance to out-of-plane deformation across all conditions. Under peak downwash, the proposed design reduces mean error by 2.5-4x in yaw, 2-45x in y-axis, and up to 5x in roll compared to the NiTi-backbone design. We further analyze hover stability through in-flight coupled-disturbance tests over varying payloads and actuation speeds, and demonstrate the system in water sampling, spot spraying, and object transport.
Aerial manipulators extend the reach and manipulation capabilities of uncrewed multirotor aerial vehicles for inspection, agriculture, sampling, and delivery. Continuum arm aerial manipulation systems offer lightweight, dexterous, and compliant interaction opportunities. Existing designs allow manipulation only below the UAV which restricts their deployability in multiple directions and through clutter. They are also sensitive to propeller downwash. Addressing these limitations, we present Tilt-X, a continuum arm aerial manipulator that integrates a tilting mechanism, a telescopic stage, and a cable-driven continuum section. We present its design and kinematic model and validate it through flight demonstrations. Tilt-X enables a volumetric workspace with up to 75 mm extension and planar orientations between 0∘ to 90∘. Experiments comparing end effector pose with and without downwash quantitatively measure its accuracy, providing critical evidence to guide the design and control of reliable aerial manipulators. Results show stabilisation of end effector pose as the manipulator extends out of the propeller influence zone.
Anuraj Uthayasooriyan, Krishna Manaswi Digumarti, Jack Breward +3
QUT Centre for Robotics, Queensland University of Technology, Brisbane, QLD Australia 4000 · School of Electrical Engineering and Robotics, Queensland University of Technology, Brisbane, QLD Australia 4000 · REF,Queensland University of Technology, Brisbane, QLD Australia 4000
Although multicopter drones are traditionally designed for "perception-only" tasks, like mapping and exploration, recent work has sought to develop Unmanned Aerial Manipulators (UAMs) to solve mobile manipulation tasks. Aerial manipulation performance can be impacted by "downwash," the airflow produced by propellers, but current state-of-the-art UAMs either ignore downwash or treat it as a disturbance. Instead, is it possible to actively use downwash as a tool during manipulation? We design a drone soccer task to explore the feasibility of downwash-based manipulation. Specifically, we develop a simplified downwash dynamics model which we use to train an RL policy to dribble a soccer ball. We further demonstrate that our policy transfers to real world deployment. This work provides key insights into novel manipulation capabilities for multicopters.
Neelay Joglekar, Bavin Saravanan, Yutong Wang +3
The Robotics Institute, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15213, USA · Department of Aerospace Engineering, Pennsylvania State University, University Park, PA 16802, USA