Advanced Robotics

doi: 10.1080/01691864.2024.2391681pmid: N/A

Hashimoto, Wataru; Hashimoto, Kazumune; Wachi, Akifumi; Shen, Xun; Kishida, Masako; Takai, Shigemasa

doi: 10.1080/01691864.2024.2401897pmid: N/A

We consider the safe navigation of robots in unknown environments using data from sensory devices. The control barrier function (CBF) is one of the promising approaches to enforcing safety in robot navigation and the recent progress on learning-based approaches realizes online synthesis of CBF-based safe controllers with sensor measurements. However, the learned CBF candidates in these works cannot be generalized to different environments and the re-synthesis is necessary when changes in the environment occur. With this observation, this paper attempts to develop a method that can effectively use past data in different environments to quickly learn the CBF candidate in the current setting by leveraging the currently developed Bayesian meta-learning framework. Our method realizes data-efficient online learning as shown in the simulation and provides safety guarantees on the resulting controller.

Shimane, Yuta; Ishigaki, Taiki; Kim, Sunghee; Yamamoto, Ko

doi: 10.1080/01691864.2024.2407118pmid: N/A

In this study, we present a method for estimating the viscoelasticity of a leaf-spring sports prosthesis using advanced energy minimizing inverse kinematics based on the Piece-wise Constant Strain (PCS) model to reconstruct the three-dimensional dynamic behavior. Dynamic motion analysis of the athlete and prosthesis is important to clarify the effect of prosthesis characteristics on foot function. However, three-dimensional deformation calculations of the prosthesis and viscoelasticity have rarely been investigated. In this letter, we apply the PCS model to a prosthesis deformation, which can calculate flexible deformation with low computational cost and handle kinematics and dynamics. In addition, we propose an inverse kinematics calculation method that is consistent with the material properties of the prosthesis by considering the minimization of elastic energy. Furthermore, we propose a method to estimate the viscoelasticity by solving a quadratic programming based on the measured motion capture data. The calculated strains are more reasonable than the results obtained by conventional inverse kinematics calculation. From the result of the viscoelasticity estimation, we simulate the prosthetic motion by forward dynamics calculation and confirm that this result corresponds to the measured motion. These results indicate that our approach adequately models the dynamic phenomena, including the viscoelasticity of the prosthesis.

Yamamoto, Shunya; Yoshikawa, Daiki; Iwamoto, Noriyasu; Umedachi, Takuya

doi: 10.1080/01691864.2024.2411691pmid: N/A

Continuum soft-robotic fingers/arms have emerged as a promising solution to realize abilities to delicately manipulate objects, conform to various shapes, and maintain cost-efficiency (i.e. fewer actuators and less computational resource). This paper introduces a novel approach to designing continuum flexible fingers by optimizing the fingers' thickness distribution to achieve specific target force vectors for specific manipulations, enabling actions like pull-in handing (retraction), push-out manipulation, secure grip, and gentle object hold. The proposed method employs a genetic algorithm to optimize the thickness distribution of the continuum finger driven by a single motor, resulting in a highly versatile and cost-effective solution. The proposed method includes physical simulations to validate the approach's effectiveness in applying desired force vectors during manipulation interactions. This work represents a significant advancement in continuum robotic systems, potentially impacting a wide range of applications in human-robot collaboration.

Iribe, Masatsugu; Fukuda, Kaito; Kinugasa, Tetsuya; Osuka, Koichi

doi: 10.1080/01691864.2024.2411695pmid: N/A

In this paper, the authors propose a new design methodology for legged walking robots with dynamic characteristics suitable for walking motions by applying the unique properties of Passive Dynamic Walking as design criteria. The authors have identified the ‘adaptive behaviour’, in which the passive dynamic walking robot automatically adjusts its gait to keep walking in response to variations in physical parameters, such as the slope angle or mass, that constitute its mechanics. Furthermore, the authors discovered that this adaptive behaviour enables the transition of the structure and degrees of freedom of the passive dynamic walking robot to different mechanical systems through the application of ‘interconnections among different dynamics’. This paper introduces new mechanical design approach for legged robots that applies these unique properties, with its validity verified through the computer simulations.

Habib, Yassine; Papadakis, Panagiotis; Le Barz, Cédric; Fagette, Antoine; Gonçalves, Tiago; Buche, Cédric

doi: 10.1080/01691864.2024.2415084pmid: N/A

We present a dense and metric 3D mapping pipeline designed for embedded operation on-board UAVs, by loosely coupling deep neural networks trained to infer dense depth single images with a SLAM system that restores metric scale from sparse depth. In contrast to computationally restrictive approaches that leverage multiple views, we propose a highly efficient, single-view approach without sacrificing 3D mapping performance. This enables real-time construction of a global 3D voxel map by iterative fusion of the rescaled dense depth maps obtained via raycasting from the estimated camera poses. Quantitative and qualitative experimentations of our framework in challenging environmental conditions show comparable or superior performance with respect to state-of-the-art approaches via a better effectiveness-efficiency trade-off.