Actuators and Sensors for Smart Systems

Smartness of technical systems relies also on appropriate actuators and sensors. Different to the prevalent definition of smartness to be embedded machine intelligence, in this paper elegance and simplicity of solutions is postulated be a more uniform and useful characterization. This is discussed in view of the current trends towards cyber physical systems and the role of components and subsystems, as well as of models for their effective realization. Current research on actuators and sensing in the fluid power area has some emphasis on simplicity and elegance of solution concepts and sophisticated modeling. This is demonstrated by examples from sensorless positioning, valve actuation, and compact hydraulic power supply

Decision-Theoretic Planning with Linguistic Terms in GOLOG

Abstract In this paper we propose an extension of the action language GOLOG that integrates linguistic terms in non-deterministic argument choices and the reward function for decision-theoretic planning. It is often cumbersome to specify the set of values to pick from in the non-deterministic-choice-of-argument statement. Also, specifying a reward function is not always easy, even for domain experts. Instead of providing a finite domain for values in the non-deterministic-choice-of-argument statement in GOLOG, we now allow for stating the argument domain by simply providing a formula over linguistic terms and fuzzy fluents. In GOLOG's forwardsearch DT planning algorithm, these formulas are evaluated in order to find the agent's optimal policy. We illustrate this in the Diner Domain where the agent needs to calculate the optimal serving order

LIO-PPF: Fast LiDAR-Inertial Odometry via Incremental Plane Pre-Fitting and Skeleton Tracking

As a crucial infrastructure of intelligent mobile robots, LiDAR-Inertial odometry (LIO) provides the basic capability of state estimation by tracking LiDAR scans. The high-accuracy tracking generally involves the kNN search, which is used with minimizing the point-to-plane distance. The cost for this, however, is maintaining a large local map and performing kNN plane fit for each point. In this work, we reduce both time and space complexity of LIO by saving these unnecessary costs. Technically, we design a plane pre-fitting (PPF) pipeline to track the basic skeleton of the 3D scene. In PPF, planes are not fitted individually for each scan, let alone for each point, but are updated incrementally as the scene 'flows'. Unlike kNN, the PPF is more robust to noisy and non-strict planes with our iterative Principal Component Analyse (iPCA) refinement. Moreover, a simple yet effective sandwich layer is introduced to eliminate false point-to-plane matches. Our method was extensively tested on a total number of 22 sequences across 5 open datasets, and evaluated in 3 existing state-of-the-art LIO systems. By contrast, LIO-PPF can consume only 36% of the original local map size to achieve up to 4x faster residual computing and 1.92x overall FPS, while maintaining the same level of accuracy. We fully open source our implementation at https://github.com/xingyuuchen/LIO-PPF.Comment: IROS 202

Application of wavelet networks to adaptive control of robotic manipulators

Published version of a chapter in the book: Intelligent Robotics and Applications. Also available from the publisher at; http://dx.doi.org/10.1007/978-3-642-25489-5_39In this paper, a wavelet-based adaptive control is proposed for a class of robotic manipulators, which consist of nonlinearities for friction effects and uncertain terms as disturbances. The controller is calculated by using a mixed of feedback linearization technique, supervisory control and H∞ control. In addition, the parameter adaptive laws of the wavelet network are developed using a Lyapunov-based design. It is also shown that both system tracking stability and convergence of the error estimation can be guaranteed in the closed-loop system. Simulation results on a three-link robot manipulator show the satisfactory performance of the proposed control schemes even in the presence of large modeling uncertainties and external disturbances

iCLAP: Shape Recognition by Combining Proprioception and Touch Sensing

The work presented in this paper was partially supported by the Engineering and Physical Sciences Council (EPSRC) Grant (Ref: EP/N020421/1) and the King’s-China Scholarship Council Ph.D. scholarship

‘Hey robot, please step back!’ - exploration of a spatial threshold of comfort for human-mechanoid spatial interaction in a hallway scenario

Within the scope of the current research the goal was to develop an autonomous transport assistant for hospitals. As a sort of social robots, they need to fulfill two main requirements with respect to their interactive behavior with humans: (1) a high level of safety and (2) a behavior that is perceived as socially proper. One important element includes the characteristics of movement. However, state-of-the-art hospital robots rather focus on safe but not smart maneuvering. Vital motion parameters in human everyday environment are personal space and velocity. The relevance of these parameters has also been reported in existing human-robot interaction research. However, to date, no minimal accepted frontal and lateral distances for human-mechanoid proxemics have been explored. The present work attempts to gain insights into a potential threshold of comfort and additionally, aims to explore a potential interaction of this threshold and the mechanoid's velocity. Therefore, a user study putting the users in control of the mechanoid was conducted in a laboratory hallway-like setting. Findings align with previously reported personal space zones in human-robot interaction research. Minimal accepted frontal and lateral distances were obtained. Furthermore, insights into a potential categorization of the lateral personal space area around a human are discussed for human-robot interaction

Development of a model for the integration of the factor "human" in factory planning

Nowadays factory planning has to face a turbulent environment due to changes in customer demand and rules of competition. Thus, there is a growing need for responsiveness, rapidity and predictability of the outcome in the planning stage. This can be obtained only if the planner is fully integrated with the system and is able to “turn the route “ promptly. The aim of this work is supporting manufacturing industry to facilitate the full integration of the planning team with factory planning processes in order to minimise the variability of the expected output of the project, which is introduced by the involvement of the human factor. This variability can affect the desired outcome because it induces uncertainty. “Factory planning project is not a black box as well as human factor is not a machine”. The trigger of the study is an evident lack of a holistic approach in the planning stage due to a widespread “watertight compartments view” which avoids the integration of each factory planning area with the system and mainly with the brain, the planner. Accordingly, The innovation of this study is contained in the ability of providing a comprehensive approach to avoid local optimisations and detachment of planning decision points within compartments. Another key aspect is the analysis that derives from the awareness that it’s impossible to achieve synergy between planner and system just considering technical aspects and neglecting the human and psychological ones. The outcome of the research is a set of structured methodologies and a qualitative model with the aim of avoiding local optimisations and supporting the planner in the decision making process. Human issue cannot be ignored without huge benefits reduction in the production system