Development of Topology Optimized Bending-Twisting Soft Finger

This paper proposed a systematic framework to automatically design and fabricate optimized soft robotic fingers. The soft finger is composed of a soft silicone structure with inner air chambers and a harder outer layer, which are fabricated by molding process and 3D printing, respectively. The softer layer is utilized for actuation while the supportive hard structure is used to impose constraints. The framework applies a topology optimization approach based on RAMP method to obtain an optimal design of the outer layer of the soft fingers. Two basic motion primitives (bending and twisting) of the soft finger were explored. A multi-segmented soft bending finger and a soft twisting finger were designed and fabricated through the proposed framework. This work also explored the combination of bending and twisting primitives by developing a combined bending-twisting soft finger. The soft fingers were characterized by free and blocked movement tests. The experiments showed that the triple-segmented soft finger can achieve a maximum of 50.5 no-load bending under the actuation pressure of 53 kPa. The blocked movement test on the multi-segmented soft actuating finger showed that this finger could generate up to a maximum of 0.63 N force under 57 kPa actuation pressure in 7 seconds of inflating time. The developed twisting soft finger was shown to achieve tip rotation of up to 219 degrees under 29 kPa actuation pressure. Finally, the potential capability of the bending-twisting soft fingers was verified through applications like screwing and object grasping.

E3MoP: Efficient Motion Planning Based on Heuristic-Guided Motion Primitives Pruning and Path Optimization With Sparse-Banded Structure

To solve the autonomous navigation problem in complex environments, an efficient motion planning approach is newly presented in this paper. Considering the challenges from large-scale, partially unknown complex environments, a three-layer motion planning framework is elaborately designed, including global path planning, local path optimization, and time-optimal velocity planning. Compared with existing approaches, the novelty of this work is twofold: 1) a novel heuristic-guided pruning strategy of motion primitives is proposed and fully integrated into the state lattice-based global path planner to further improve the computational efficiency of graph search, and 2) a new soft-constrained local path optimization approach is proposed, wherein the sparse-banded system structure of the underlying optimization problem is fully exploited to efficiently solve the problem. We validate the safety, smoothness, flexibility, and efficiency of our approach in various complex simulation scenarios and challenging real-world tasks. It is shown that the computational efficiency is improved by 66.21\% in the global planning stage and the motion efficiency of the robot is improved by 22.87\% compared with the recent quintic B\'{e}zier curve-based state space sampling approach. We name the proposed motion planning framework E$ \mathbf{^3} $MoP, where the number 3 not only means our approach is a three-layer framework but also means the proposed approach is efficient in three stages.

Humanoid Robot Path Planning Using Rapidly Explored Random Tree and Motion Primitives

Path planning is an essential part of the control system of any mobile robot. In this article the path planner for a humanoid robot is presented. The short description of an universal control framework and the Motion Generation System is also presented. Described path planner utilizes a limited number of motions called the Motion Primitives. They are generated by Motion Generation System. Four different algorithms, namely the: Informed RRT, Informed RRT with random bias, and RRT with A* likeheuristics were tested. For the last one the version with biased random function was also considered. All mentioned algorithms were evaluated considering three dif ferent scenarios. Obtained results are described and discussed.

EffMoP: Efficient Motion Planning Based on Heuristic-Guided Motion Primitives Pruning and Path Optimization With Sparse-Banded Structure

To solve the autonomous navigation problem in complex environments, an efficient motion planning approach called EffMoP is presented in this paper. Considering the challenges from large-scale, partially unknown complex environments, a three-layer motion planning framework is elaborately designed, including global path planning, local path optimization, and time-optimal velocity planning. Compared with existing approaches, the novelty of this work is twofold: 1) a novel heuristic-guided pruning strategy of motion primitives is proposed and fully integrated into the state lattice-based global path planner to further improve the computational efficiency of graph search, and 2) a new soft-constrained local path optimization approach is proposed, wherein the sparse-banded system structure of the underlying optimization problem is fully exploited to efficiently solve the problem. We validate the safety, smoothness, flexibility, and efficiency of EffMoP in various complex simulation scenarios and challenging real-world tasks.