Human-Aware Physical Human-Robot Collaborative Transportation and Manipulation with Multiple Aerial Robots

TL;DR

This work addresses all-$6$-DoF physical collaboration between a human operator and a team of quadrotors carrying a cable-suspended payload. It introduces a distributed wrench estimation framework that does not rely on force sensors, a 6-$D$ admittance controller, and a human-aware force distribution that exploits system redundancy to maintain inter-robot and human safety distances without impacting payload tracking. The approach is validated through extensive simulations and real-world experiments, showing successful collaboration in all $6$ DoF, safe obstacle avoidance, and effective human-guided navigation. The results demonstrate the practicality of multi-UAV cable-based cooperative manipulation and highlight avenues for onboard sensing, safety, and perception-driven improvements in future work.

Abstract

Human-robot interaction will play an essential role in various industries and daily tasks, enabling robots to effectively collaborate with humans and reduce their physical workload. Most of the existing approaches for physical human-robot interaction focus on collaboration between a human and a single ground or aerial robot. In recent years, very little progress has been made in this research area when considering multiple aerial robots, which offer increased versatility and mobility. This paper proposes a novel approach for physical human-robot collaborative transportation and manipulation of a cable-suspended payload with multiple aerial robots. The proposed method enables smooth and intuitive interaction between the transported objects and a human worker. In the same time, we consider distance constraints during the operations by exploiting the internal redundancy of the multi-robot transportation system. The key elements of our approach are (a) a collaborative payload external wrench estimator that does not rely on any force sensor; (b) a 6D admittance controller for human-aerial-robot collaborative transportation and manipulation; (c) a human-aware force distribution that exploits the internal system redundancy to guarantee the execution of additional tasks such inter-human-robot separation without affecting the payload trajectory tracking or quality of interaction. We validate the approach through extensive simulation and real-world experiments. These include scenarios where the robot team assists the human in transporting and manipulating a load, or where the human helps the robot team navigate the environment. We experimentally demonstrate for the first time, to the best of our knowledge, that our approach enables a quadrotor team to physically collaborate with a human in manipulating a payload in all 6 DoF in collaborative human-robot transportation and manipulation tasks.

Figures (18)

• Figure 1: System convention definition: $\mathcal{I}$, $\mathcal{L}$, $\mathcal{B}_{k}$ denote the world frame, the payload body frame, and the $k^{th}$ robot body frames, respectively, for a generic quadrotor team that's cooperatively transporting and manipulating a cable-suspended payload.
• Figure 2: Block diagram of the system illustrating the overview of the system. It begins with a trajectory generator, which outputs the desired trajectory of the payload in all six degrees of freedom. The payload admittance controller updates this trajectory based on the estimated human input, adapting to the interaction wrench exerted by the human on the payload. The modified trajectory is then passed to the payload controller as the desired payload state to track. The payload controller calculates the desired wrench to control the payload's motion. Subsequently, the dynamic force distribution module allocates the desired cable forces based on the human's position and the system dynamics, and distributes the desired cable forces for each robot to track. Each robot controller will then track its corresponding desired cable force and output the corresponding thrust and moment commands to the quadrotor platform.
• Figure 3: Wrench estimation evaluation. The human operator uses a force measurement device to measure the applied wrench on the payload, which is used to validate our wrench estimation algorithm results. On the left, we show the human operator applies force via the force measurement device, and, on the right, we show the force measurement device in detail.
• Figure 4: Cable force estimation experiment results. Comparison between the cable force estimation algorithm results and the force measurements from the force measurement device in all 3 DoF.
• Figure 5: Results of the wrench estimation experiment. This figure compares the wrench estimation results from our proposed wrench estimation algorithm (blue) with those obtained using the momentum observer method (red) sanalitro2022ralindirectforce, as well as with the actual measurements recorded by the wrench measurement device (green) across all 6 DoF.