Trajectory Planning and Control of a Quadrotor Choreography for Real-Time Artist-in-the-Loop Performances

被引:7
作者
El-Jiz, Michael [1 ]
Rodrigues, Luis [1 ]
机构
[1] Concordia Univ, Dept Elect & Comp Engn, 1515 St Catherine St,EV5-139, Montreal, PQ, Canada
基金
加拿大自然科学与工程研究理事会;
关键词
Quadrotor choreography; trajectory planning; real-time control; artist-in-the-loop;
D O I
10.1142/S2301385018500012
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
This paper proposes a systematic methodology for the guidance, control, and navigation of a quadrotor to perform a choreographed dance in real-time as a function of, and interacting with, the music performed by an artist-in-the-loop. This methodology allows for a real-time interaction with improvized music by an artist based on the pitch of the acoustic signal being played without prior knowledge of the music. The four main components of a human choreography (namely, the notions of space, shape, time and structure) are analyzed and mathematically formulated for a robotic performance. A new approach for mapping music features to trajectory parameters is proposed, as well as the design of a trajectory shaping filter based on two coefficients that are set in real-time by an artist through a MIDI foot-pedal board. The proposed approach maps motion parameters and the music to trajectory motifs that are then switched in harmony with the chord structure. The overall system is validated in a hardware-in-the-loop simulation where the hardware will consist of the musical instrument and the foot pedals. In the simulation, the trajectory generator system is inverted to generate a sequence of music pitches from the actual trajectory of the quadrotor. The music generated by the quadrotor is then played back to the musician allowing for real-time interaction. The simulation results show that the proposed methodology yields an effective performance for a quadrotor choreography based on the real-time interaction with a musician. The proposed system was successfully used by an artist as can be seen in a video link to the work described in this paper and listed in the conclusions.
引用
收藏
页码:1 / 13
页数:13
相关论文
共 50 条
[41]   A real-time algorithm of task allocation and trajectory planning considering task weights for drone swarms [J].
Lee D. .
Journal of Institute of Control, Robotics and Systems, 2021, 27 (02) :118-123
[42]   Real-Time Trajectory Planning and Obstacle Avoidance for Human-Robot Co-Transporting [J].
Yu, Xinbo ;
Guo, Xiong ;
He, Wei ;
Arif Mughal, Muhammad ;
Zhang, Dawei .
IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING, 2025, 22 :2969-2985
[43]   Sequential Convex Programming Methods for Real-Time Optimal Trajectory Planning in Autonomous Vehicle Racing [J].
Scheffe, Patrick ;
Henneken, Theodor Mario ;
Kloock, Maximilian ;
Alrifaee, Bassam .
IEEE TRANSACTIONS ON INTELLIGENT VEHICLES, 2023, 8 (01) :661-672
[44]   Real-Time Optimal Trajectory Planning for Autonomous Driving with Collision Avoidance Using Convex Optimization [J].
Li, Guoqiang ;
Zhang, Xudong ;
Guo, Hongliang ;
Lenzo, Basilio ;
Guo, Ningyuan .
AUTOMOTIVE INNOVATION, 2023, 6 (3) :481-491
[45]   Real-Time Optimal Trajectory Planning for Autonomous Driving with Collision Avoidance Using Convex Optimization [J].
Guoqiang Li ;
Xudong Zhang ;
Hongliang Guo ;
Basilio Lenzo ;
Ningyuan Guo .
Automotive Innovation, 2023, 6 :481-491
[46]   Real-Time Trajectory Planning and Obstacle Avoidance for Human-Robot Co-Transporting [J].
Yu, Xinbo ;
Guo, Xiong ;
He, Wei ;
Arif Mughal, Muhammad ;
Zhang, Dawei .
IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING, 2025, 22 :2969-2985
[47]   Closing the loop as an inverse problem: the real-time control of Themis adaptive optics [J].
Thiebauta, Eric ;
Tallona, Michel ;
Tallon-Bosc, Isabelle ;
Gelly, Bernard ;
Douet, Richard ;
Langlois, Maud ;
Moretto, Gil .
ADAPTIVE OPTICS SYSTEMS VIII, 2022, 12185
[48]   A Trajectory Planning and Control System for Quadrotor Unmanned Aerial Vehicle in Field Inspection Missions [J].
Chen, Gang ;
Wang, Rong ;
Dong, Wei ;
Sheng, Xinjun .
INTELLIGENT ROBOTICS AND APPLICATIONS, ICIRA 2017, PT III, 2017, 10464 :551-562
[49]   Six-DOF Spacecraft Optimal Trajectory Planning and Real-Time Attitude Control: A Deep Neural Network-Based Approach [J].
Chai, Runqi ;
Tsourdos, Antonios ;
Savvaris, Al ;
Chai, Senchun ;
Xia, Yuanqing ;
Chen, C. L. Philip .
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2020, 31 (11) :5005-5013
[50]   A Real-time Trajectory Re-planning Method for Orbital Transfer Vehicle via Convex Optimization [J].
Ma Haolei ;
Li Xuefeng ;
Huo Siqi .
PROCEEDINGS OF THE 32ND 2020 CHINESE CONTROL AND DECISION CONFERENCE (CCDC 2020), 2020, :5068-5073