Renxi Qiu

User-centered design of a dynamic-autonomy remote interaction concept for manipulation-capable robots to assist elderly people in the home

In this article, we describe the development of a human-robot interaction concept for service robots to assist elderly people in the home with physical tasks. Our approach is based on the insight that robots are not yet able to handle all tasks autonomously with sufficient reliability in the complex and heterogeneous environments of private homes. We therefore employ remote human operators to assist on tasks a robot cannot handle completely autonomously. Our development methodology was user-centric and iterative, with six user studies carried out at various stages involving a total of 241 participants. The concept is under implementation on the Care-O-bot 3 robotic platform. The main contributions of this article are (1) the results of a survey in form of a ranking of the demands of elderly people and informal caregivers for a range of 25 robot services, (2) the results of an ethnography investigating the suitability of emergency teleassistance and telemedical centers for incorporating robotic teleassistance, and (3) a user-validated human-robot interaction concept with three user roles and corresponding three user interfaces designed as a solution to the problem of engineering reliable service robots for home environments.

Towards robust personal assistant robots: Experience gained in the SRS project

SRS is a European research project for building robust personal assistant robots using ROS (Robotic Operating System) and Care-O-bot (COB) 3 as the initial demonstration platform. In this paper, experience gained while building the SRS system is presented. A main contribution of the paper is the SRS autonomous control framework. The framework is divided into two parts. First, it has an automatic task planner, which initialises actions on the symbolic level. The planner produces proactive robotic behaviours based on updated semantic knowledge. Second, it has an action executive for coordination actions at the level of sensing and actuation. The executive produces reactive behaviours in well-defined domains. The two parts are integrated by fuzzy logic based symbolic grounding. As a whole, they represent the framework for autonomous control. Based on the framework, several new components and user interfaces are integrated on top of COBs existing capabilities to enable robust fetch and carry in unstructured environments. The implementation strategy and results are discussed at the end of the paper.

Superhydrophobic dual micro- and nanostructures fabricated by direct laser interference lithography

Abstract. A method for the fabrication of highly ordered superhydrophobic dual micro- and nanostructures on silicon by direct laser interference lithography (LIL) is presented. The method offers its innovation that the superhydrophobic dual micro- and nanostructures can be fabricated directly by controlling the process of four-beam laser interference and the use of hydrofluoric acid (HF) to wipe off the silica generated during the process. Different laser fluences, exposure durations, and cleanout times have been investigated to obtain the optimum value of the contact angle (CA). The superhydrophobic surface with the CA of 153.2 deg was achieved after exposure of 60 s and immersion in HF with a concentration of 5% for 3 min. Compared with other approaches, it is a facile and efficient method with its significant feature for the macroscale fabrication of highly ordered superhydrophobic dual micro- and nanostructures on silicon.

A new exact substructure method using mixed modes

In this paper, the use of free- or fixed-interface modes in exact substructure displacement expansions is briefly summarized. Then the substructural displacements are expressed exactly in terms of mixed modes, i.e., the displacements consist of linear combinations of fixed- and free-interface modes. This yields a new exact mixed-mode substructure method. It is demonstrated that the exact substructure methods with fixed or free interfaces are two limiting cases of this new mixed-mode exact method. Thus, the exact substructure method variants with free-interface, fixed-interface or mixed modes form a systematic theory of substructure methods, which is unified by the new mixed-mode variant. This new exact variant not only has this important theoretical significance but also has great practical significance because it can lead to new approximate methods and to a deeper understanding of existing ones, e.g., quasi-comparison function methods, dynamic condensation methods or substructure modal synthesis methods.

Both antireflection and superhydrophobicity structures achieved by direct laser interference nanomanufacturing

Inspired by nature, a number of techniques have been developed to fabricate the bionic structures of lotus leaves and moth eyes in order to realize the extraordinary functions of self-cleaning and antireflection. Compared with the existing technologies, we present a straightforward method to fabricate well-defined micro and nano artificial bio-structures in this work. The proposed method of direct laser interference nanomanufacturing (DLIN) takes a significant advantage of high efficiency as only a single technological procedure is needed without pretreatment, mask, and pattern transfer processes. Meanwhile, the corresponding structures show both antireflection and superhydrophobicity properties simultaneously. The developed four-beam nanosecond laser interference system configuring the TE-TE-TE-TE and TE-TE-TE-TM polarization modes was set up to generate periodic micro cone and hole structures with a huge number of nano features on the surface. The theoretical and experimental results have shown that the periodic microcone structure exhibits excellent properties with both a high contact angle (CA = 156.3°) and low omnidirectional reflectance (5.9–15.4%). Thus, DLIN is a novel and promising method suitable for mass production of self-cleaning and antireflection surface structures.

Integrating Robot Task Planner with Common-sense Knowledge Base to Improve the Efficiency of Planning☆

This paper presents a developed approach for intelligently generating symbolic plans by mobile robots acting in domestic environments, such as offices and houses. The significance of the approach lies in developing a new framework that consists of the new modeling of high-level robot actions and then their integration with common-sense knowledge in order to support a robotic task planner. This framework will enable interactions between the task planner and the semantic knowledge base directly. By using common-sense domain knowledge, the task planner will take into consideration the properties and relations of objects and places in its environment, before creating semantically related actions that will represent a plan. This plan will accomplish the user order. The robot task planner will use the available domain knowledge to check the next related actions to the current one and the actions conditions met will be chosen. Then the robot will use the immediately available knowledge information to check whether the plan outcomes are met or violated.

Towards automated task planning for service robots using semantic knowledge representation

Automated task planning for service robots faces great challenges in handling dynamic domestic environments. Classical methods in the Artificial Intelligence (AI) area mostly focus on relatively structured environments with fewer uncertainties. This work proposes a method to combine semantic knowledge representation with classical approaches in AI to build a flexible framework that can assist service robots in task planning at the high symbolic level. A semantic knowledge ontology is constructed for representing two main types of information: environmental description and robot primitive actions. Environmental knowledge is used to handle spatial uncertainties of particular objects. Primitive actions, which the robot can execute, are constructed based on a STRIPS-style structure, allowing a feasible solution (an action sequence) for a particular task to be created. With the Care-O-Bot (CoB) robot as the platform, we explain this work with a simple, but still challenging, scenario named “get a milk box”. A recursive back-trace search algorithm is introduced for task planning, where three main components are involved, namely primitive actions, world states, and mental actions. The feasibility of the work is demonstrated with the CoB in a simulated environment.

Adaptive bees algorithm : bioinspiration from honeybee foraging to optimize fuel economy of a semi-track air-cushion vehicle

This interdisciplinary study covers bionics, optimization and vehicle engineering. Semi-track air-cushion vehicle (STACV) provides a solution to transportation on soft terrain, whereas it also brings a new problem of excessive fuel consumption. By mimicking the foraging behaviour of honeybees, the bioinspired adaptive bees algorithm (ABA) is proposed to calculate its running parameters for fuel economy optimization. Inherited from the basic algorithm prototype, it involves parallel-operated global search and local search, which undertake exploration and exploitation, respectively. The innovation of this improved algorithm lies in the adaptive adjustment mechanism of the range of local search (called ‘patch size’) according to the source and the rate of change of the current optimum. Three gradually in-depth experiments are implemented for 143 kinds of soils. First, the two optimal STACV running parameters present the same increasing or decreasing trend with soil parameters. This result is consistent with the terramechanics-based theoretical analysis. Second, the comparisons with four alternative algorithms exhibit the ABAs effectiveness and efficiency, and accordingly highlight the advantage of the novel adaptive patch size adjustment mechanism. Third, the impacts of two selected optimizer parameters to optimization accuracy and efficiency are investigated and their recommended values are thus proposed.

Automation in handling uncertainty in semantic-knowledge based robotic task-planning by using Markov Logic Networks

Generating plans in real world environments by mobile robot planner is a challenging task due to the uncertainty and environment dynamics. Therefore, task-planning should take in its consideration these issues when generating plans. Semantic knowledge domain has been proposed as a source of information for deriving implicit information and generating semantic plans. This paper extends the Semantic-Knowledge Based (SKB) plan generation to take into account the uncertainty in existing of objects, with their types and properties, and proposes a new approach to construct plans based on probabilistic values which are derived from Markov Logic Networks (MLN). An MLN module is established for probabilistic learning and inferencing together with semantic information to provide a basis for plausible learning and reasoning services in supporting of robot task-planning. In addition, an algorithm has been devised to construct MLN from semantic knowledge. By providing a means of modeling uncertainty in system architecture, task-planning serves as a supporting tool for robotic applications that can benefit from probabilistic inference within a semantic domain. This approach is illustrated using test scenarios run in a domestic environment using a mobile robot.

Challenges for service robots operating in non-industrial environments

The concept of service robotics has grown considerably over the past two decades with many robots being used in non-industrial environments such homes, hospitals and airports. Many of these environments were never designed to have mobile service robots deployed within them. This paper describes some of the challenges that are faced and need to be overcome in order for robots to successfully work in non-industrial environments (specifically homes and hospitals). These include the problems caused by an environment not having been designed to be robot-friendly, the unstructured nature of the environment and finally the challenges presented by certain user populations who may have difficulties interacting with a robot.