Soft Robotic Mannequin: Design and Algorithm for Deformation Control

This paper presents a novel soft robotic system for a deformable mannequin that can be employed to physically realize the 3D geometry of different human bodies. The soft membrane on a mannequin is deformed by inflating several curved chambers using pneumatic actuation. Controlling the freeform surface of a soft membrane by adjusting the pneumatic actuation in different chambers is challenging as the membrane's shape is commonly determined by the interaction between all chambers. Using vision feedback provided by a structured-light based 3D scanner, we developed an efficient algorithm to compute the optimized actuation of all chambers which could drive the soft membrane to deform into the best approximation of different target shapes. Our algorithm converges quickly by including pose estimation in the loop of optimization. The time-consuming step of evaluating derivatives on the deformable membrane is avoided by using the Broyden update when possible. The effectiveness of our soft robotic mannequin with controlled deformation has been verified in experiments.

Collision-Aware Fast Simulation for Soft Robots by Optimization-Based Geometric Computing

Soft robots are able to safely interact with environments because of their mechanical compliance. Self-collision is also employed in the modern design of soft robots to enhance their performance in different tasks. However, developing an efficient and reliable simulator which can handle the collision response well, is still a challenging task in the research of soft robotics. This paper presents a collision-aware simulator based on geometric optimization, in which we develop a highly efficient and realistic collision checking / response model incorporating a hyperelastic material property. Both actuated deformation and collision response for soft robots are formulated as geometry-based objectives. The collision-free body of a soft robot can be obtained by minimizing the geometry-based objective function. Different from the FEA-based physical simulation, the proposed pipeline performs a much lower computational cost. Moreover, adaptive remeshing is applied to achieve the improvement of the convergence when dealing with soft robots having large volume variations. Experimental tests are conducted on different soft robots to verify the performance of our approach.

CLOVER Robot: A Minimally Actuated Jumping Robotic Platform for Space Exploration

Robots have been critical instruments to space exploration by providing access to environments beyond human limitations. Jumping robot concepts are attractive solutions to negotiate complex terrain. However, among the engineering challenges to overcome to enable jumping robot concepts for sustained operation, reduction of mechanical failure modes is one of the most fundamental. This study set out to develop a jumping robot with focus on minimal actuation for reduced mechanism maintenance. We present the synthesis of a Sarrus-style linkage to constraint the system to a single translational degree of freedom without the use of typical synchronising gears. We delimit the present research to vertical solid jumps to assess the performance of the fundamental main-drive linkage. A laboratory demonstrator assists the transfer of theoretical concepts and approaches. The laboratory demonstrator performs jumps with 63% potential-to-kinetic energy conversion efficiency, with a theoretical maximum of 73%. Satisfactory operation opens up design optimisation and directional jump capability towards the development of a jumping robotic platform for space exploration.