Segment-Wise Soft Robotics Inspired Flexible Antenna Arrays: Design and Optimization

2026-06-10

AI Summary

This study addresses the limitations of conventional reconfigurable antenna systems, whose fixed architectures hinder large-scale, flexible spatial arrangements and thus constrain communication performance. To overcome this, the authors propose a segmented soft robotic antenna system that uniquely integrates programmable compliant mechanical structures with antenna arrays, enabling synergistic optimization between macroscopic spatial reconfiguration and microscopic antenna tuning through independently controlled flexible segments. Two deployment schemes—SEAC and HEIAC—are developed, accompanied by tailored algorithms: PDD-PGA for SEAC, and a hybrid approach combining BCD-PDD-PGA with a greedy reverse antenna selection strategy for HEIAC, both modeling the nonlinear mapping between segment deformations and antenna coordinates. Experimental results demonstrate that SEAC and HEIAC achieve sum-rate improvements of 37.9% and 32.1%, respectively, over traditional three-dimensional reconfigurable arrays, with SEAC further delivering a 49.3% gain under compact deployment conditions.

Abstract

In this paper, we propose a segment-wise soft robotic antenna (SRA) system, where each soft robotic arm referred to as a tentacle, comprises multiple independently controllable segments with bending, elongation-retraction, and sweeping motions. By adjusting segment motion parameters, the positions of surface-mounted antennas are reconfigured, distinguishing it from conventional reconfigurable antenna (RA) systems. Based on this model, we propose two antenna deployment schemes: the segmented end-antenna configuration (SEAC), where fixed antennas are mounted at the segment ends and reconfigured via segment motions; and the hybrid end-and-intermediate antenna configuration (HEIAC), where RAs are further integrated as intra-segment antennas. In HEIAC, soft-robot segment deformation provides large-scale spatial reconfiguration, while RAs enable fine-grained adjustment. For SEAC, we formulate a sum-rate maximization problem accounting for inter-segment connectivity and the nonlinear mapping from segment deformation parameters to antenna coordinates, and develop a penalty dual decomposition-projected gradient ascent (PDD-PGA) algorithm. For HEIAC, we jointly optimize segment deformation, intra-segment antenna positions, and antenna activation using a block coordinate descent (BCD)-PDD-PGA algorithm with greedy backward antenna selection. Simulation results demonstrate that the proposed schemes substantially outperform fixed-position antenna arrays and conventional RA baselines. In particular, SEAC and HEIAC achieve 37.9% and 32.1% sum-rate gains over conventional 3D reconfigurable arrays, respectively, while SEAC provides up to a 49.3% gain in compact array deployments.

Problem

• soft robotics
• reconfigurable antenna
• antenna array
• spatial reconfiguration
• sum-rate maximization

Innovation

• soft robotics
• reconfigurable antenna arrays
• segment-wise actuation
• spatial reconfiguration
• sum-rate maximization