Design Optimizer for Soft Growing Robot Manipulators in Three-Dimensional Environments
This work provides a tool for engineers and robot enthusiasts to optimize soft robot designs before manufacturing, though it is incremental as it extends a planar optimizer to three dimensions.
The authors tackled the problem of optimizing the design of soft growing robot manipulators for three-dimensional tasks, achieving high precision in reaching targets and robust performance across different evolutionary computation algorithms.
Soft growing robots are novel devices that mimic plant-like growth for navigation in cluttered or dangerous environments. Their ability to adapt to surroundings, combined with advancements in actuation and manufacturing technologies, allows them to perform specialized manipulation tasks. This work presents an approach for design optimization of soft growing robots; specifically, the three-dimensional extension of the optimizer designed for planar manipulators. This tool is intended to be used by engineers and robot enthusiasts before manufacturing their robot: it suggests the optimal size of the robot for solving a specific task. The design process models a multi-objective optimization problem to refine a soft manipulator's kinematic chain. Thanks to the novel Rank Partitioning algorithm integrated into Evolutionary Computation (EC) algorithms, this method achieves high precision in reaching targets and is efficient in resource usage. Results show significantly high performance in solving three-dimensional tasks, whereas comparative experiments indicate that the optimizer features robust output when tested with different EC algorithms, particularly genetic algorithms.