Exploring Drone Dynamics: Stability and Force Generation in Multimedia Design

1 Jul 2024


(1) Hamed Alimohammadzadeh, University of Southern California, Los Angeles, USA;

(2) Rohit Bernard, University of Southern California, Los Angeles, USA;

(3) Yang Chen, University of Southern California, Los Angeles, USA;

(4) Trung Phan, University of Southern California, Los Angeles, USA;

(5) Prashant Singh, University of Southern California, Los Angeles, USA;

(6) Shuqin Zhu, University of Southern California, Los Angeles, USA;

(7) Heather Culbertson, University of Southern California, Los Angeles, USA;

(8) Shahram Ghandeharizadeh, University of Southern California, Los Angeles, USA.

Abstract and Introduction

Flying Light Specks

User Interaction

Multimedia Systems Challenges

Related Work

Conclusions and Current Efforts, Acknowledgments, and References


A DV is an essential tool to design and implement future multimedia applications using FLSs. We are currently designing and developing hardware and software in support of a DV’s kinesthetic haptic user interactions. This includes IRB-approved human subject studies towards the goal of using one or more FLSs to provide a high force for generating a stiff surface without losing stability or compromising user safety.

Our effort is a combination of empirical studies, physics inspired simulation and analytical models. We are designing large drones with different cage arrangements to conduct the human subject studies. This includes quantifying the force exerted by the drones, see Figure 8 and a video demonstration at https://youtu.be/ O7nFdFXhbwQ. Figure 9 shows the measured force in Newtons as a function of the maximum voltage of the motors. The resulting thrust of the motors is directly related to this voltage and creates the measured force. These measurements were made across 3000 samples during 3 seconds. The standard deviation is small[5], demonstrating the force exerted can be repeatedly controlled with a high accuracy. With our cage designs and a single motor, we observe minimal impact on the thrust and force generated by the motor with the presence of a cage.

Focusing on small drones, in addition to the discussions of Section 3, we are investigating the feasibility of multiple miniature sized FLSs coming in contact with a user at high speeds. To evaluate the stability of FLSs in such formations, we are conducting empirical studies that fly a swarm of (Crazyflies) drones in a close circular formation. This formation may be horizontal, vertical, or slanted at 45 degrees. Videos available at https://youtu.be/oT5RR8RPl0I, https:// youtu.be/TQM4hMBwLHM, and https://youtu.be/NNlWn9VW894 respectively.

We will use the results from both the large and small drones to model how the measured forces change with different sized drones, propeller sizes, and motor characteristics. Moreover, we are developing a class of PID controllers that allow a drone to follow a pre-defined path and render a pre-specified force output. These use a Vicon localization system with centimeter-level accuracy.

Figure 9: Observed force as a function of the percentage of maximum motor voltage.

We interface a Raspberry Pi 4 as the CPU of Figure 4 with the drone’s FC to control its attitude and exerted force. We are implementing a decentralized localization technique, collision avoidance, and FLS failure handling techniques in software. Each is a finite state machine with an event-driven framework to implement its functionality. This framework represents an FLS as an abstract machine that can be in exactly one of a finite number of states at a given time. An event handler processes a queue of events that transitions the state of this abstract machine. It processes events sequentially and atomically, preventing undesirable race conditions caused by inter-leaved execution of events.

Using the Python programming language, we have implemented a scalable emulator with processes. Each process represents an FLS, communicates using UDP[6], and implements the aforementioned state machines with an event queue and event handlers. At the time of this writing, we have an implementation of a decentralized localization technique, a centralized and decentralized FLS group formation technique, and a centralized (APF [38, 68]) and a decentralized collision avoidance techniques. While the centralized algorithms are intended for use by the DV Hub, the decentralized algorithms are to be deployed on the FLSs. The emulator scales both vertically with many (400) cores and horizontally with multiple servers.


This research was supported in part by the NSF grant IIS-2232382.


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[5] The highest standard deviation is 0.43 and observed with 90% thrust.

[6] We support both packet loss and out of order packet delivery.