This research is by Guanrui Li, Rundong Ge, and Giuseppe Loianno at the Agile Robotics and Perception Lab (ARPL) at NYU. As you can see from the video, the drone makes keeping rock-solid control over that suspended payload look easy, but it’s very much not, especially considering that everything you see is running onboard the drone itself at 500Hz— all it takes is an IMU and a downward-facing monocular camera, along with the drone’s Snapdragon processor.
To get this to work, the drone has to be thinking about two things. First, there’s state estimation, which is the behavior of the drone itself along with its payload at the end of the tether. The drone figures this out by watching how the payload moves using its camera and tracking its own movement with its IMU. Second, there’s predicting what the payload is going to do next, and how that jives (or not) with what the drone wants to do next. The researchers developed a model predictive control (MPC) system for this, with some added perception constraints to make sure that the behavior of the drone keeps the payload in view of the camera.
At the moment, the top speed of the system is 4 m/s, but it sounds like rather than increasing the speed of a single payload-swinging drone, the next steps will be to make the overall system more complicated by somehow using multiple drones to cooperatively manage tethered payloads that are too big or heavy for one drone to handle alone.
For more on this, we spoke with Giuseppe Loianno, head of the ARPL.
IEEE Spectrum: We’ve seen some examples of delivery drones delivering suspended loads. How will this work improve their capabilities?
Giuseppe Loianno: For the first time, we jointly design a perception-constrained model predictive control and state estimation approaches to enable the autonomy of a quadrotor with a cable suspended payload using onboard sensing and computation. The proposed control method guarantees the visibility of the payload in the robot camera as well as the respect of the system dynamics and actuator constraints. These are critical design aspects to guarantee safety and resilience for such a complex and delicate task involving transportation of objects.
The additional challenge involves the fact that we aim to solve the aforementioned problem using a minimal sensor suite for autonomous navigation made by a single camera and IMU. This is an ambitious goal since it concurrently involves estimating the load and the vehicle states. Previous approaches leverage GPS or motion capture systems for state estimation and do not consider the perception and physical constraints when solving the problem. We are confident that our solution will contribute to making a reality the autonomous delivery process in warehouses or in dense urban areas where the GPS signal is currently absent or shadowed.
Will it make a difference to delivery systems that use an actuated cable and only leave the load suspended for the delivery itself?
This is certainly an interesting question. We believe that adding an actuated cable will introduce more disadvantages than benefits. Certainly, an actuated cable can be leveraged to compensate for cable’s swinging motions in windy conditions and/or increase the delivery precision. However, the introduction of additional actuated mechanisms and components come at the price of an increased system mass and inertia. This will reduce the overall flight time and the vehicle’s agility as well as the system resilience with respect to the transportation task. Finally, active mechanisms are also more difficult to design compared to passive ones.
What’s challenging about doing all of this on-vehicle?
There are several challenges to solve on-board this problem. First, it is very difficult to concurrently run perception and action on such computationally constrained platforms in real-time. Second, the first aspect becomes even more challenging if we consider as in our case a perception-based constrained receding horizon control problem that aims to guarantee the visibility of the payload during the motion, while concurrently respecting all the system physical and sensing limitations. Finally, it has been challenging to run the entire system at a high rate to fully unleash the system’s agility. We are currently able to reach rates of 500 Hz.
Can your method adapt to loads of varying shapes, sizes, and masses? What about aerodynamics or flying in wind?
Technically, our approach can easily be adapted to varying objects sizes and masses. Our previous contributions have already shown the ability to estimate online changes in the vehicle/load configuration and can potentially be used to operate the proposed system in dynamic conditions, where the load’s characteristics are unknown and/or may vary across consecutive flights. This can be useful for both package delivery or warehouse operations, where different types of objects need to be transported or manipulated.
The aerodynamics problem is a great point. Overall, our past work has investigated the aerodynamics of wind disturbances for a single robot without a load. Formulating these problems for the proposed system is challenging and is still an open research question. We have some ideas to approach this problem combining Bayesian estimation techniques with more recent machine learning approaches and we will tackle it in the near future.
What are the limitations on the performance of the system? How fast and agile can it be with a suspended payload?
The limits of the performances are established by the actuating and sensing system. Our approach intrinsically considers both physical and sensing limitations of our system. From a sensing and computation perspective, we believe to be close to the limits with speeds of up to 4 m/s. Faster speeds can potentially introduce motion blur while decreasing the load tracking precision. Moreover, faster motions will increase as well aerodynamic disturbances that we have just mentioned. In the future, modeling these phenomena and their incorporation in the proposed solution can further push the agility.
Your paper talks about extending this approach to multiple vehicles cooperatively transporting a payload, can you tell us more about that?
We are currently working on a distributed perception and control approach for cooperative transportation. We already have some very exciting results that we will share with you very soon! Overall, we can employ a team of aerial robots to cooperatively transport a payload to increase the payload capacity and endow the system with additional resilience in case of vehicles’ failures. A cooperative cable suspended payload cooperative transportation system allows as well to concurrently and independently control the load’s position and orientation. This is not possible just using rigid connections. We believe that our approach will have a strong impact in real-world settings for delivery and constructions in warehouses and GPS-denied environments such as dense urban areas. Moreover, in post disaster scenarios, a team of physically interconnected aerial robots can deliver supplies and establish communication in areas where GPS signal is intermittent or unavailable.
PCMPC: Perception-Constrained Model Predictive Control for Quadrotors with Suspended Loads using a Single Camera and IMU, by Guanrui Li, Alex Tunchez, and Giuseppe Loianno from NYU, will be presented (virtually) at ICRA 2021.
Source: IEEE Spectrum