Nonlinear Model Predictive Control for Human-Robot Handover with Application to the Aerial Case

Abstract

In this article, we consider the problem of delivering an object to a human coworker by means of an aerial robot.To this aim, we present an ergonomics-aware Nonlinear Model Predictive Control (NMPC) designed to autonomously perform the handover. The method is general enough to be applied to any mobile robot with a minimal adaptation of the robot model. Our formulation lets the NMPC steer the robot toward a handover location optimizing the human coworker ergonomics metrics, which includes the predicted joint torques of the human. The motion task is expressed in a frame relative to the human, whose motion model is included in the equations of the NMPC. This allows the controller to reactively adapt to the human movements by predicting her future poses over the horizon. The control framework also accounts for the problem of maintaining visibility on the human coworker, while respecting both the actuation and state limits. A safety barrier is also embedded in the controller to avoid any risk of collision with the human partner. Realistic simulations are used to validate the feasibility of the approach and the source code of the implementation is released open-source.

Multimodal Learning‐Based Proactive Human Handover Intention Prediction Using Wearable Data Gloves and Augmented Reality

“…Upon prediction, the robot commences the process of receiving the object, as shown in Figure 10(3)-( 4). Subsequently, it positions the object correctly, as illustrated in Figure 10(5)-( 6), before returning to its original location, as depicted in Figure 10(7)- (8). Furthermore, as indicated in Figure 11(1) and ( 2), the human presents the robot with a large and heavy object.…”

“…Subsequently, the robot accurately interpreted the human's intention, as evident in Figure 11 (5). It proceeded to pick up the heavy object and then place it correctly, as shown in Figure 11(6)- (8). The robot then returned to its initial position, marking the completion of the interaction, as depicted in Figure 11(9) and (10).…”

“…[4][5][6] The exchange of objects between humans and robots (referred to as human-robot handovers) becomes pivotal in assembly activities involving human-robot collaboration. [7,8] Implementing human-robot handovers contributes substantially to time and labor saving during the assembly process, thereby augmenting overall efficiency. [9] In automobile assembly, for example, human workers spend considerable time and effort collecting and delivering parts or tools to their partners (or receiving parts or tools from partners and correctly placing them).…”

Modelling, Analysis, and Control of OmniMorph: an Omnidirectional Morphing Multi-rotor UAV

“…10. Electronic speed controllers Kiss Racing 32A 5 were used. The effect of aerodynamic interference was estimated by measuring the change in force on the single propeller due to the increasing speed of the remaining propellers.…”

“…Over the last decade, the design, perception, and control of Unmanned Aerial Vehicles (UAVs) have substantially evolved in terms of functionalities and capabilities indoors or outdoors. Research now considers the use of UAVs as Aerial Robotic Manipulators that can interact physically with the environment [1] and humans [2]; contact-based inspection [3], object manipulation [4], and assisting human workers [5] are some of the typical tasks.…”