Vision-Based Control of a Tether-Suspended Aerial Radiation Sensing Payload
Authors: Ian Snider, Brian J. Quiter, Emil Rofors, Mark W. Mueller
Organizations: Department of Nuclear Engineering, University of California, Berkeley, CA 94720, USA · Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA · Department of Mechanical Engineering, University of California, Berkeley, CA 94720, USA
Aerial radiation surveys achieve higher sensitivity when the radiation detector is held close to the ground. Detector sensitivity falls off roughly with the inverse square of the distance to the source, so a detector flown high is slower to reach a given minimum detectable activity. Flying the vehicle low puts the propellers near the ground, where downwash can disturb the surveyed area and resuspend contaminated particulates. Tether suspension decouples the detector from the vehicle altitude, but leaves the payload unactuated and only indirectly controllable. We therefore present a vision-based control approach for an aerial sensing payload suspended on a tether beneath a heavy-lift drone. Because a survey plan is decided as radiation detections arrive, we design a pilot aid for commanding the survey trajectory manually with a handheld transmitter. The controller regulates the payload, rather than the vehicle, onto that trajectory. The system uses onboard sensors with a downward-facing camera fixed to the drone body tracking a ring marker on the payload. A four-state Kalman filter estimates the tether swing angles and rates from payload bearing measurements, and a linear quadratic regulator with integral action takes the payload position as the regulated output. In outdoor flight tests under wind, the payload-aware controller reduced payload tracking error during transit by 20% when compared against a vehicle-referenced baseline, with the cost of higher peak error on arrival at a waypoint.
Figures & tables
Fig. 1: Heavy-lift drone flying with tether-suspended payload. The right image is the downward-facing camera view with the overlaid reference trajectory: the current reference point (red), the payload position estimate (green), a line giving the estimated distance from the reference (blue), and the point at which a vertically hung tether would sit beneath the drone at hover (purple).
Fig. 2: Diagram of drone with tether-suspended payload.
Fig. 3: Block diagram of the control architecture. The system is separated into the 50 Hz payload estimator and acceleration commands, the ArduPilot inner-loop flight controller, and the 48 fps vision thread. The system receives external input from the reference generator, pilot, and wind.
Fig. 4: Tether occlusions, as shown in downward-facing camera.
Hexacopter mass
16.5 kg
Payload mass
5.65 kg
Tether length
6.2 m
Ring outer diameter
31 cm
Ring width
3.5 cm
TABLE I: Hexacopter with Load Parameters
Fig. 5: East-North payload trajectories for the two control modes. The test included 15 m legs with 15 s hovers at each corner.
Payload Control
ArduPilot Baseline
Leg
Run 1
Run 2
Run 1
Run 2
1 (S)
0.72
0.62
0.82
0.86
2 (W)
0.70
0.70
0.74
0.76
3 (N)
0.66
0.57
0.67
0.56
4 (E)
0.66
0.67
0.68
0.69
Square
0.68
0.64
0.73
0.72
TABLE II: Tracking RMSE by Leg [m]
Fig. 6: Payload position error versus time, aligned at the start of each leg. The bold blue and red lines are the average payload position error for the payload-aware controller and the vehicle-referenced ArduPilot baseline respectively.
Fig. 7: Payload position (top) and velocity (bottom) as commanded with the piloting aid. Orange, green, and purple show the East, North, and Up components respectively; dashed lines are the reference and solid lines the achieved response.
Cooperative payload transportation using multiple Unmanned Aerial Vehicles (UAVs) poses challenges in stability, coordination, and robustness, especially under external disturbances and unmodeled dynamics. This work proposes a dual-UAV payload transportation framework supported by a compact, custom-designed force sensor measuring the interaction force at the UAV cable anchor point. The sensor design and mathematical model are presented, and its performance is characterized through static and dynamic tests evaluating linearity, hysteresis, repeatability, and crossload. The control architecture follows a cascade structure: fast inner loops handle vehicle stabilization, while outer loops are designed to compensate for the measured forces. The approach is validated through simulations and indoor experiments under position uncertainty. Payload-drop and constrained-space tests assess the proposed sensing and control architecture against literature-based distributed references, showing improved stabilization, coordination, and disturbance rejection. A video of the experiments is available at: https://youtu.be/rIw9-fvV8Qw.
Andrea Delbene, Giorgio Cannata, Giorgio Carlini +2
Department of Informatics, Bioengineering, Robotics and Systems Engineering, Università degli Studi di Genova, Via all’Opera Pia 13, 16145, Genoa, Italy
This work proposes a novel control and estimation approach for aerial manipulation of a cable-suspended load using Unmanned Aerial Vehicles (UAVs). Common approaches in the state of the art have practical limitations, relying on direct load measurements and Lagrangian methods for dynamic modeling. The lack of a straightforward dynamic model of the system led us to propose adopting the Udwadia-Kalaba method to explicitly incorporate the cable's geometric constraints. This formulation allowed for the consistent derivation of the tension force and its direct integration into the NMPC prediction model. Additionally, we propose a sensorless load state estimation based on the same geometric constraints. Results from real-robot experiments demonstrated that the explicit inclusion of load dynamics in the optimization problem significantly reduces trajectory-tracking errors and yields better overall performance compared to strategies based on incomplete models.
Ana Maria Nascimento, Augusto Sales, Antonio Marcus Lima +1
Recent developments in planetary exploration have shown the potential of Unmanned Aerial Vehicles (UAVs), such as the Ingenuity helicopter that provided valuable mapping data. However, limited payload capabilities constrain the flight times and compute available for localization, which restrict their applicability. By providing a tethered connection, issues such as battery and computational constraints are offloaded to the base rover. At the same time, the cable can be exploited for non-drifting localization. This work presents a novel Tether-Inertial Localization approach that uses tether length and angle measurements to estimate the UAV position relative to its base. The method combines a computationally efficient analytical catenary model with a Gaussian Process (GP) residual error compensation. This accounts for systematic sensor inaccuracies and model limitations. Experimental validation across circular, triangular, and figure-eight trajectories with tether lengths up to 4.5 m and a total flight time of 37 minutes demonstrates the effectiveness of the proposed approach. Using only tether-based position estimates for feedback, the analytical catenary model achieves an average RMSE of 7.4 cm, which is further reduced to 5.2 cm through GP-based residual compensation, one order of magnitude better than the state-of-the-art. These results establish Tether-Inertial Localization as a practical alternative to vision- and GNSS-based localization for Tethered Unmanned Aerial Vehicles (TUAVs).
Dielof van Loon, Anton Bredenbeck, Lennart Puck +2
Dept. of Cognitive Robotics, Faculty of Mechanical Engineering, TU Delft, The Netherlands. · Biomorphic Intelligence Laboratory, Dept. of Control & Operations, Faculty of Aerospace Engineering, TU Delft, The Netherlands. · Planetary Robotics Lab, European Space Agency, Noordwijk, The Netherlands.