This work proposes a framework that generates and optimally selects task-specific assembly configurations for a large group of homogeneous modular aerial systems, explicitly enforcing bounds on inter-module downwash. Prior work largely focuses on planar layouts and often ignores aerodynamic interference. In contrast, firstly we enumerate non-isomorphic connection topologies at scale; secondly, we solve a nonlinear program to check feasibility and select the configuration that minimizes control input subject to actuation limits and downwash constraints. We evaluate the framework in physics-based simulation and demonstrate it in real-world experiments.
Figures & tables
Fig. 1: The optimal assembly of 12 modules, given the target wrench set defined in Section IV . Each black square represents one module, each blue rectangle represents a rigid connection, each red arrow represents the module frame z -axis, and the purple capsules indicate the collision-free downwash volume for each module.
Fig. 2: An illustration of two modules with four connection ports on each frame. Each connector can rotate along the green axis, where the orange screw can fix the connector. The white circles on the connector are permanent magnets.
Fig. 3: An example of a four-module assembly and its matrix representation, where a black square represents a single module, the four rectangles within each square are the connectors. Blue indicates a valid connection, yellow indicates a blocked connection, and gray indicates an available connector.
Fig. 4: All non-isomorphic configurations under the symmetric point group C4 using 5 modules.
n
Number of available configurations
Number of non-isomorphic configurations
CPU Time (s)
1
1
1
0.0000
2
4
1
0.0006
3
6
2
0.0007
4
16
7
0.0017
5
68
24
0.0072
6
272
97
0.0261
TABLE I: Exhaustive enumeration of non-isomorphic configurations for connector angles all fixed to π/2 .
Fig. 5: Comparison of exhaustive and sampling-based enumeration. The sample count is fixed at Nn=500 for all n . For n>11 , CPU time is shown only for every fifth value of n .
Fig. 6: All optimal configurations with control costs Jcost using 4 modules for the wrench set specified in Section IV-C .
m
cf
cm
larm
lc
r
0.24kg
3.87×10−7
1.06×10−8
0.06m
0.11m
0.095m
TABLE II: Single module parameters used in this work.
Fig. 7: Achievable force polytope for the best configuration in Fig. 6 (c); the blue, red, and purple vectors are projections onto the force subspace at zero torque and all lie within the polytope.
Fig. 8: Computation time, averaged per CPU core, with 10 randomly selected wrenches for n∈{10,20,…,90} .
Fig. 9: Two optimal configurations from Fig. 8 with n=30 and n=60 , tracking a circular trajectory with fixed identity orientation.
Fig. 10: The 2 -module configuration hovering in the air.
Fig. 11: Experiment results of the system in free flight when tracking a 3D figure- 8 trajectory while maintaining the target roll angle at 0∘ (left) and −5∘ (right). The black dashed lines are the target trajectories.
Aerial robotics studies the design, perception, control, and coordination of flying robots for navigation, inspection, transport, and physical interaction.
Department of Electrical and Computer Engineering, National University of Singapore, Singapore 117583 · Department of Mechanical Engineering, National University of Singapore, Singapore 117583