Liquid Marble Patchwork on Super-Repellent Surface

Liquid marble (LM) is a droplet that is wrapped by hydrophobic solid particles, which behave as a non-wetting soft solid. Based on these properties, LM can be applied in fluidics and soft device applications. A wide variety of functional particles have been synthesized to form functional LMs. However, the formation of multifunctional LMs by integrating several types of functional particles is challenging. Here, a general strategy for the flexible patterning of functional particles on droplet surfaces in a patchwork-like design is reported. It is shown that LMs can switch their macroscopic behavior between a stable and active state on super-repellent surfaces in situ by jamming/unjamming the surface particles. Active LMs hydrostatically coalesce to form a self-sorted particle pattern on the droplet surface. With the support of LM handling robotics, on-demand cyclic activation–manipulation–coalescence–stabilization protocols by LMs with different sizes and particle types result in the reliable design of multi-faced LMs. Based on this concept, a single bi-functional LM is designed from two mono-functional LMs as an advanced droplet carrier.     

Perception‐aware autonomous mast motion planning for planetary exploration rovers

Highly accurate real‐time localization is of fundamental importance for the safety and efficiency of planetary rovers exploring the surface of Mars. Mars rover operations rely on vision‐based systems to avoid hazards as well as plan safe routes. However, vision‐based systems operate on the assumption that sufficient visual texture is visible in the scene. This poses a challenge for vision‐based navigation on Mars where regions lacking visual texture are prevalent. To overcome this, we make use of the ability of the rover to actively steer the visual sensor to improve fault tolerance and maximize the perception performance. This paper answers the question of where and when to look by presenting a method for predicting the sensor trajectory that maximizes the localization performance of the rover. This is accomplished by an online assessment of possible trajectories using synthetic, future camera views created from previous observations of the scene. The proposed trajectories are quantified and chosen based on the expected localization performance. In this study, we validate the proposed method in field experiments at the Jet Propulsion Laboratory (JPL) Mars Yard. Furthermore, multiple performance metrics are identified and evaluated for reducing the overall runtime of the algorithm. We show how actively steering the perception system increases the localization accuracy compared with traditional fixed‐sensor configurations.     

The World robot summit disaster robotics category – achievements of the 2018 preliminary competition

The World Robot Summit is a robot Olympics and aims to be held in a different country every four years from 2020. The concept of the Plant Disaster Prevention challenge is daily inspections, checks, and emergency response in industrial plants, and in this competition, robots must carry out these types of missions in a mock-up plant. The concept of the Tunnel Disaster Response and Recovery challenge is emergency response to tunnel disasters, and is a simulation competition whereby teams compete to show their ability to deal with disasters, by collecting information and removing debris. The Standard Disaster Robotics challenge assesses, in the form of a contest, the standard performance levels of a robot that are necessary for disaster prevention and emergency response. The World Robot Summit Preliminary Competition was held at Tokyo Big Sight in October 2018, and 36 teams participated in the Disaster Robotics Category. UGVs and UAVs contended the merits of new technology for solving complex problems, using core technologies such as mobility, sensing, recognition, performing operations, human interface, autonomous intelligence etc., as well as system integration and implementation of strategies for completing missions, gaining high-level results.     

Highly stretchable self-sensing actuator based on conductive photothermally-responsive hydrogel

Soft robots built with active soft materials have been increasingly attractive. Despite tremendous efforts in soft sensors and actuators, it remains extremely challenging to construct intelligent soft materials that simultaneously actuate and sense their own motions, resembling living organisms’ neuromuscular behaviors. This work presents a soft robotic strategy that couples actuation and strain-sensing into a single homogeneous material, composed of an interpenetrating double-network of a nanostructured thermo-responsive hydrogel poly(N-isopropylacrylamide) (PNIPAAm) and a light-absorbing, electrically conductive polymer polypyrrole (PPy). This design grants the material both photo/thermal-responsiveness and piezoresistive-responsiveness, enabling remotely-triggered actuation and local strain-sensing. This self-sensing actuating soft material demonstrated ultra-high stretchability (210%) and large volume shrinkage (70%) rapidly upon irradiation or heating (13%/°C, 6-time faster than conventional PNIPAAm). The significant deswelling of the hydrogel network induces densification of percolation in the PPy network, leading to a drastic conductivity change upon locomotion with a gauge factor of 1.0. The material demonstrated a variety of precise and remotely-driven photo-responsive locomotion such as signal-tracking, bending, weightlifting, object grasping and transporting, while simultaneously monitoring these motions itself via real-time resistance change. The multifunctional sensory actuatable materials may lead to the next-generation soft robots of higher levels of autonomy and complexity with self-diagnostic feedback control.     

Learning fuzzy controllers in mobile robotics with embedded preprocessing

The automatic design of controllers for mobile robots usually requires two stages. In the first stage, sensorial data are preprocessed or transformed into high level and meaningful values of variables which are usually defined from expert knowledge. In the second stage, a machine learning technique is applied to obtain a controller that maps these high level variables to the control commands that are actually sent to the robot. This paper describes an algorithm that is able to embed the preprocessing stage into the learning stage in order to get controllers directly starting from sensorial raw data with no expert knowledge involved. Due to the high dimensionality of the sensorial data, this approach uses Quantified Fuzzy Rules (QFRs), that are able to transform low-level input variables into high-level input variables, reducing the dimensionality through summarization. The proposed learning algorithm, called Iterative Quantified Fuzzy Rule Learning (IQFRL), is based on genetic programming. IQFRL is able to learn rules with different structures, and can manage linguistic variables with multiple granularities. The algorithm has been tested with the implementation of the wall-following behavior both in several realistic simulated environments with different complexity and on a Pioneer 3-AT robot in two real environments. Results have been compared with several well-known learning algorithms combined with different data preprocessing techniques, showing that IQFRL exhibits a better and statistically significant performance. Moreover, three real world applications for which IQFRL plays a central role are also presented: path and object tracking with static and moving obstacles avoidance.     

一个新的力觉临场感系统模型

本文提出了一个新的力觉临场感系统模型，讨论了它的二端口网络结构，推导了相应的网络特性参数，分析了参数的含仪和测量方法。此外，对理想系统的参数也进行了分析，本文的研究为临场感技术的发展提供了基础。     

Real-World Robotics: Learning to Plan for Robust Execution

In executing classical plans in the real world, small discrepancies between a planner's internal representations and the real world are unavoidable. These can conspire to cause real-world failures even though the planner is sound and, therefore, proves that a sequence of actions achieves the goal. Permissive planning, a machine learning extension to classical planning, is one response to this difficulty. This paper describes the permissive planning approach and presents GRASPER, a permissive planning robotic system that learns to robustly pick up novel objects.     

Robot-assisted wayfinding for the visually impaired in structured indoor environments

We present a robot-assisted wayfinding system for the visually impaired in structured indoor environments. The system consists of a mobile robotic guide and small passive RFID sensors embedded in the environment. The system is intended for use in indoor environments, such as office buildings, supermarkets and airports. We describe how the system was deployed in two indoor environments and evaluated by visually impaired participants in a series of pilot experiments. We analyze the system’s successes and failures and outline our plans for future research and development.     

Children, robotics, and education

Popular interest in robotics has increased astonishingly in the last few years. Robotics is seen by many as offering major new benefits in education at all levels. Before rushing to exploit this popularity, educators should ask serious questions about the universality and longevity of the robotics phenomenon. Is it a fashion? To be useful, the energy released by robotics must be sustained and universal, and the means of exploiting it must be systematic. Universities define their own robotics curriculum, but most schools lack both the resources and the freedom to do this, and must work with a national curriculum. If it can be shown that robotics has sustained potential in education, it seems inevitable that new ways need to be found to integrate it into the school curriculum.