In context
By mid-2025, tensegrity robots—lightweight, deformable structures of compression struts and tension cables—were gaining traction for navigating complex environments where conventional wheeled or legged robots struggle. However, most research focused on gait types, actuation methods, and path planning, leaving the energy cost of continuous rolling largely unexamined. This study addressed that gap by optimizing the actuation of a six-strut tensegrity rolling robot (TR-6) to reduce driving energy.
What was reported
Researchers from Shaoxing University and Zhejiang University proposed a cable-driven actuation strategy using a hybrid genetic algorithm and beetle antennae search (BAGA). They built an equivalent model of the TR-6 robot, which consists of six hollow aluminum struts and 24 composite cables (spring-steel wire combinations). The optimization objective was to minimize the strain energy difference before and after each rolling step, subject to constraints on gravity moment, rolling energy, and cable length adjustments.
To handle the robot's posture during unbalanced states, they employed non-rigid-body motion analysis (NRMA), which accounts for structural deformation unlike traditional rigid-body methods. The BAGA algorithm, combining genetic algorithm's multi-objective handling with beetle antennae search's computational efficiency, determined optimal cable actuation lengths. Simulations in ADAMS and physical prototype tests with motor-driven telescopic actuators validated the approach.
Results showed that a single-cable actuation scheme saved approximately 14% in strain energy compared to a double-cable scheme, effectively reducing the robot's propelling cost. The study also identified that the robot can execute COC (closed-open-closed triangle) evolutionary gaits when energy conditions are met, otherwise it performs single-step independent gaits.
Why it mattered
This work provided a systematic method for minimizing actuation energy in tensegrity rolling robots, a key step toward practical deployment in energy-constrained applications such as search-and-rescue or planetary exploration. The optimization framework and NRMA-based posture analysis offer a theoretical basis for designing more efficient soft robots, potentially influencing future autonomous systems that require low-power, adaptable locomotion.
"The results imply that the proposed method can effectively minimize the propelling cost of tensegrity rolling robots (single-cable scheme saves about 14% in strain energy compared to double-cable scheme)."
Source: 《机器人》期刊 (robot.sia.cn) · Published 2025-09-16 · “六杆张拉整体翻滚机器人驱动优化设计”