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A Mini-drone Development, Genetic Vector Field-Based Multi-agent Path Planning, and Flight Tests

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Abstract

In this paper, we detail the development of mini-drones and propose a Genetic Vector Field (GVF) algorithm for multi-agent path planning. The developed mini-drone has a compact size, with a wheelbase of 130 mm and mass of 76 g, including all the sensors necessary for autonomous flight in an outdoor environment. In addition, the mini-drone can successfully complete a complex multi-agent flight mission via the integrated Linux computer and wireless network system that allows real-time feedback. The control system of the mini-drone is designed as based on the dynamics established by the system identification process, which is developed as based on actual flight data; in addition, a complementary filter is adopted for the navigation algorithm to reduce computational cost, which is essential to increasing the update rate. The proposed GVF algorithm utilizes a vector field algorithm to generate the path to track the given target while also avoiding collisions with other agents and obstacles. Moreover, because the GVF algorithm optimally allocates the targets to each agent using a modified genetic algorithm, the proposed algorithm can generate optimal paths for multiple agents. This algorithm has been implemented in the integrated Linux computer of the mini-drone, and the feasibility has been verified by carrying out several flight tests in actual outdoor environments.

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Abbreviations

F f :

Forgetting factor

ω c :

Cut-off frequency

θ cmd,sum ϕ cmd,sum :

Attitude control command (pitch, roll)

C lon C lat C down :

Coefficient reflecting drag effect (longitudinal, lateral, and downward)

v lon,wind v lat,wind :

Wind speed (longitudinal and lateral)

C thr :

Throttle coefficient

u thr u thr,trim :

Throttle input and throttle trim

u FL,lon u FL,lat u FL,down :

Control input to feedback linearization controller

r :

Yaw rate

a lon a lat a down :

Linear acceleration (longitudinal, lateral, and downward)

\( \hat{a}_{\text{lon}} \,\,\,\hat{a}_{\text{lat}} \,\,\,\hat{a}_{\text{down}} \) :

Estimated linear acceleration (longitudinal, lateral, and downward)

L lon L lat L down :

Luenberger observer gain (longitudinal, lateral, and downward)

t k,elapsed :

Elapsed time of kth member

f k :

Fitness value of kth member

\( \vec{A}_{\text{VF,unsat}} \,\,\,\vec{A}_{\text{VF}} \) :

Acceleration vector from vector field (unsaturated and saturated)

\( \vec{r}_{\text{T}} \,\,\,\vec{r}_{{{\text{a}},i}} \,\,\,\vec{r}_{{{\text{o}},i}} \) :

Relative position vector (target, ith agent, ith obstacle)

\( \vec{e}_{\text{T}} \,\,\,\vec{e}_{{{\text{a}},i}} \,\,\,\vec{e}_{{{\text{o}},i}} \) :

Unit vector of relative position vector (target, ith agent, ith obstacle)

k VF :

Vector field gain

\( \vec{V}_{\text{VF,unsat}} \,\,\,\vec{V}_{\text{VF}} \) :

Velocity vector from vector field (unsaturated and saturated)

\( \vec{V}_{\text{cur}} \) :

Current velocity vector

T s :

Sampling time

\( \vec{A}_{\text{cmd}} \) :

Acceleration command vector

T * :

Optimally assigned target

M k :

kth member

POP_SIZE:

Population size

MAX_ITER:

Maximum number of iterations

MAX_ACC:

Maximum acceleration magnitude

MAX_VEL:

Maximum velocity magnitude

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Acknowledgements

This work was supported by Institute for Information & Communications Technology Promotion (IITP) grant funded by the Korea government (MSIT) (no. R0126-17-1005, Development of High Reliable Communications and Security SW for Various Unmanned Vehicles).

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Authors and Affiliations

  1. Department of Aerospace Engineering, KAIST, Daejeon, Republic of Korea

    Dasol Lee

  2. Department of Electrical Engineering, KAIST, Daejeon, Republic of Korea

    David Hyunchul Shim

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  1. Dasol Lee
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Corresponding author

Correspondence to Dasol Lee.

Appendix

Appendix

To acquire the dynamics required for controller design, MATLAB® System Identification Toolbox™ was utilized to establish system identification. Since the roll and pitch axes are symmetric, we experimented for the pitch and yaw axes only; the results are shown in Table 4. Figures 18 and 19 show the results of system identification for the pitch and yaw axes, respectively. In both figures, the blue and red lines in the upper graph represent the data measured and estimated by the established dynamics, and the lower graph corresponds to the input data.

Table 4 Summary of the system identification results
Full size table
Fig. 18
figure 18

System identification results for pitch axis

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Fig. 19
figure 19

System identification results for yaw axis

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The pitch and yaw axes dynamics are a second- and first-order system, respectively, and the fit values for the pitch and yaw axes are 70.8 and 82.3, respectively. These values are calculated using the function, ‘goodnessOfFit’, which is a MATLAB internal function.

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Lee, D., Shim, D.H. A Mini-drone Development, Genetic Vector Field-Based Multi-agent Path Planning, and Flight Tests. Int. J. Aeronaut. Space Sci. 19, 785–797 (2018). https://doi.org/10.1007/s42405-018-0052-0

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