A design and manufacturing method is described for creating a motor tendon–actuated soft foam robot. The method uses a castable, light, and easily compressible open-cell polyurethane foam, producing a structure capable of large (~70% strain) deformations while requiring low torques to operate (<0.2 N·m). The soft robot can change shape, by compressing and folding, allowing for complex locomotion with only two actuators. Achievable motions include forward locomotion at 13 mm/s (4.3% of body length per second), turning at 9◦/s, and end-over-end flipping. Hard components, such as motors, are loosely sutured into cavities after molding. This reduces unwanted stiffening of the soft body. This work is the first demonstration of a soft open-cell foam robot locomoting with motor tendon actuators. The manufacturing method is rapid (~30 min per mold), inexpensive (under $3 per robot for the structural foam), and flexible, and will allow a variety of soft foam robotic devices to be produced.

The paper reviews the state of the art of natural language engineering (NLE) around 1995, when this journal first appeared, and makes a critical comparison with the current state of the art in 2018, as we prepare the 25th Volume. Specifically the then state of the art in parsing, information extraction, chatbots, and dialogue systems, speech processing and machine translation are briefly reviewed. The emergence in the 1980s and 1990s of machine learning (ML) and statistical methods (SM) is noted. Important trends and areas of progress in the subsequent years are identified. In particular, the move to the use of n-grams or skip grams and/or chunking with part of speech tagging and away from whole sentence parsing is noted, as is the increasing dominance of SM and ML. Some outstanding issues which merit further research are briefly pointed out, including metaphor processing and the ethical implications of NLE.

Path planning on a two-dimensional grid is a well-studied problem in robotics. It usually involves searching for a shortest path between two vertices on a grid given that some of the grid cells are impassable (occupied by obstacles). Single-source path planning finds shortest paths from a given source vertex to all other vertices of the grid. Singles-source path planning enhances robot autonomy by calculating multiple possible paths for various navigation scenarios when the destination state is unknown. A high-performance algorithm for single-source any-angle path planning on a grid called CWave is proposed here. Any-angle attribute implies that the algorithm calculates paths which can include line segments at any angle, as opposed to standard A* that runs on an 8-connected graph, which permits turns with 45° increments only. The key idea of CWave is to abandon the graph model and operate directly on the grid geometry using discrete geometric primitives (instead of individual vertices) to represent the wave front. In its most basic form (CWaveInt), CWave requires only integer arithmetics. CWaveInt, however, can accumulate the distance error at turning points. A modified version of CWave (CWaveFpuSrc) with minimal usage of floating-point calculations is also developed to eliminate any accumulative errors, which is proven mathematically and experimentally on several maps. The performance of the algorithm on most of the tested maps is demonstrated to be significantly faster than that of Theta*, Lazy Theta*, Field A*, ANYA, Block A*, and A* adapted for single-source planning (on maps with lower number of isolated obstacles, CWaveFpuSrc is 2−3 times faster than its fastest tested alternative Block A*). An N-threaded implementation (CWaveN) of CWave is presented and tested to demonstrate an improved performance (multithreaded implementation is 1.5−3 times faster than single-threaded CWave). The paper discusses foundations and experimental validation of CWave, and presents future work to address the limitations of the current implementations and obtain further performance enhancements.

Linear quadratic regulator (LQR), a popular technique for designing optimal state feedback controller, is used to derive a mapping between continuous and discrete time inverse optimal equivalence of proportional integral derivative (PID) control problem via dominant pole placement. The aim is to derive transformation of the LQR weighting matrix for fixed weighting factor, using the discrete algebraic Riccati equation (DARE) to design a discrete time optimal PID controller producing similar time response to its continuous time counterpart. Continuous time LQR-based PID controller can be transformed to discrete time by establishing a relation between the respective LQR weighting matrices that will produce similar closed loop response, independent of the chosen sampling time. Simulation examples of first/second order and first-order integrating processes exhibiting stable/unstable and marginally stable open loop dynamics are provided, using the transformation of LQR weights. Time responses for set-point and disturbance inputs are compared for different sampling times as fraction of the desired closed loop time constant.

This paper presents a vision-based path planning strategy that aims to reduce the computational time required by a robot to find a feasible path from a starting point to the goal point. The proposed algorithm presents a novel strategy that can be implemented on any well-known path planning algorithm such as A*, D* and probabilistic roadmap (PRM), to improve the swiftness of these algorithms. This path planning algorithm is suitable for real-time scenarios since it reduces the computational time compared to the basis and traditional algorithms. To test the proposed path planning strategy, a tracking control strategy is implemented on a mobile platform. This control strategy consists of three major stages. The first stage deals with gathering information about the surrounding environment using vision techniques. In the second stage, a free-obstacle path is generated using the proposed reduced scheme. In the final stage, a Lyapunov kinematic tracking controller and two Artificial Neural Network (ANN) based-controllers are implemented to track the proposed path by adjusting the rotational and linear velocity of the robot. The proposed path planning strategy is tested on a Pioneer P3-DX differential wheeled mobile robot and an Xtion PRO depth camera. Experimental results prove the efficiency of the proposed path planning scheme, which was able to reduce the computational time by a large percentage which reached up to 88% of the time needed by the basis and traditional scheme, without significant adverse effect on the workability of the basis algorithm. Moreover, the proposed path planning algorithm has improved the path efficiency, in terms of the path length and trackability, challenging the traditional trade-off between swiftness and path efficiency.

This paper addresses three control implementation issues for trajectory tracking of robotic manipulators: unmodeled dynamics, unknown input saturation and peaking effects during the transient phase. A model-free first-order robust-adaptive control method is used to deal with the unmodeled dynamics. Robust optimality and stability of the controller are proved using the 𝓗∞ technique and the game-algebraic Riccati equation. An intuitive approach is devised to incorporate the unknown input saturation by modifying the speed of the desired trajectory. The trajectory scaling is performed by using only the state errors. Furthermore, two different techniques are utilized to suppress peaking during the transient response of the trajectory tracking. The first method adds an extra term in the input while the second method uses variable gain to improve the transient response. A systematic procedure for finding the controller parameters is formulated using features, such as rise time and settling time. A three-degree-of-freedom robot manipulator is used for the validation of the proposed controller in simulations and experiments.

This paper investigates a battery snap-in operation performed by an industrial robot. The snap-fit phenomenon is experienced during the insertion of batteries into the battery holder. In order to understand the nature of the snap-fit phenomenon, the large displacement but small deformation theory is used to model the insertion operation. The stability of equilibrium points is analysed in order to determine the so-called trip point. The purpose of the investigation is to augment the displacement control of the robot with force feedback. A microcontroller-based measurement system is implemented to provide the force feedback. The proposed method is valid for a class of snap-fit problems not only for the investigated specific one.

We argue against Foreman’s proposal to settle the continuum hypothesis and other classical independent questions via the adoption of generic large cardinal axioms.

In complex products the values of parameters are rarely exactly the required values, rather they often have a margin that might be designed in deliberately or be the incidental results of other design decisions. These margins play a critical role in design processes in managing engineering change and iteration. While engineers often talk about margins informally, designers and researchers also use other terms for specific margin concepts. This paper reviews the existing literature on related concepts and defines margins formally. It discusses the role margins play in handling uncertainty by distinguishing between buffer and excess. Buffer deals with uncertainty and excess with the remaining overcapacity of the design. Buffer can transition into excess of the design solution if the uncertainty can be reduced. The concepts are applied to the temperature margins of several candidate materials for a non-rotary jet engine component. This shows that a clear understanding of margins can help a company to select design alternatives.

This paper presents the second release of arrau, a multigenre corpus of anaphoric information created over 10 years to provide data for the next generation of coreference/anaphora resolution systems combining different types of linguistic and world knowledge with advanced discourse modeling supporting rich linguistic annotations. The distinguishing features of arrau include the following: treating all NPs as markables, including non-referring NPs, and annotating their (non-) referentiality status; distinguishing between several categories of non-referentiality and annotating non-anaphoric mentions; thorough annotation of markable boundaries (minimal/maximal spans, discontinuous markables); annotating a variety of mention attributes, ranging from morphosyntactic parameters to semantic category; annotating the genericity status of mentions; annotating a wide range of anaphoric relations, including bridging relations and discourse deixis; and, finally, annotating anaphoric ambiguity. The current version of the dataset contains 350K tokens and is publicly available from LDC. In this paper, we discuss in detail all the distinguishing features of the corpus, so far only partially presented in a number of conference and workshop papers, and we also discuss the development between the first release of arrau in 2008 and this second one.

A crisis is a complex situation, which actors have some difficulties to manage it. They are under stress to deal with problems that they cannot predict consequences. The human conditions (familial and life) and, the influence of the environment (politic, economic, media) pushes the actors to lose control of the crisis situation. The question we face in this paper is: “is it possible to predict the impact of the stress in this type of situation?” Our main hypothesis to answer is to represent experience feedback using knowledge management. To model the crisis management as systemic system emphasizing regulation loops, and the collaboration activity by showing the dimension of the communication, coordination, and cooperation. This modeling is illustrated in a terrorist attack situation in Algeria. To predict stress consequences, fuzzy set principle is adopted, based on experience feedback and situations modeling, as a generator of alternative states given a stress event.

During a crisis situation, the ability of emergency department to take reliable and quick decisions is the main feature that defines the success or failure of this organization in the course of its crisis management. Decision makers spend time on identifying the decisions that will be taken for the whole of the crisis management, and on anticipating the preparation of these decisions, ensuring that they have time to properly prepare all decisions to be taken and, be able to implement them as fast as possible. However, the context and the characteristics of the crisis make the decision process complicated because there is no specific methodology to anticipate these decisions and properly manage collaboration with the other protagonists. There is also the pressure of time, a significant stress and, the emotional impact on the decision maker that lead to losing objectivity in decision making. We understand so that the right decision will be greatly facilitated and enhanced by the development of an adequate tool and process for decision-making. This tool must respect methods of the emergency department considered, and highlight the importance of experience feedback referencing to past cases, especially success and failures. We propose in this paper, software in order to handle experience feedback as a support for decision-making in crisis management “Crisis Clever System”. Several dimensions are considered in this study, from one side: organization, communication and problem-solving activities and from the other side the presentation and finding of experience feedback thanks to an analogy technique.

Innovation and creativity are a mandatory for companies who wish to stay competitive. In order to promote an inventive dynamic, it implies to set up tools, habits, and an adapted environment to foster creativity. Creativity is the wealth of companies that should be valorized. To promote creativity, companies implement creativity workshops that gather people with various roles and expertise exchange and create knowledge to solve collectively open-ended engineering problems. However, group dynamics or facilitation can make the wrong decision and make the creative problem-solving unfruitful. The aim of our research project is to create a digital system to manage and valorize knowledge during creativity workshops. To design this system, we need to formalize the knowledge domain of creative workshops. The ontologies are used for decades to structure and manage information and knowledge in different domains. However, methodologies to design these ontologies are either hardly reproducible or not oriented to extract knowledge from organization. This article describes a methodology based on an organizational modeling to build ontologies. We will illustrate our approach by designing an ontology that models knowledge of creativity workshops.

This work aims to evaluate the energy savings that can be achieved in domestic hot water (DHW) production using consumption forecasting through statistical modeling. It uses our forecast algorithm and aims at investigating how it can improve energy efficiency depending on the system configuration. Especially, the influence of the DHW production type used is evaluated as well as the water tank insulation. To that end, real consumption measurements are used for model training. Then simulations are run on using TRNSYS software to compute the total energy consumption of DHW production systems over 1 year. Simulations are also based on real consumption measurements for realistic results. To appraise the energy savings, we compared simulations that consider either no forecast (reactive control), perfect forecast (to estimate the control ability to consider forecast), or the forecast provided by our algorithm. The measurements and simulations are run on 26 different but real dwellings to assess results variability. Several system configurations are also compared with varying thermal insulation indices for a complete benchmark of the approach so that an overall performance of the system and the anticipation strategy could be evaluated.

Recently, interest in integrated assembly sequence planning (ASP) and assembly line balancing (ALB) began to pick up because of its numerous benefits, such as the larger search space that leads to better solution quality, reduced error rate in planning, and expedited product time-to-market. However, existing research is limited to the simple assembly problem that only runs one homogenous product. This paper therefore models and optimizes the integrated mixed-model ASP and ALB using Multi-objective Discrete Particle Swarm Optimization (MODPSO) concurrently. This is a new variant of the integrated assembly problem. The integrated mixed-model ASP and ALB is modeled using task-based joint precedence graph. In order to test the performance of MODPSO to optimize the integrated mixed-model ASP and ALB, an experiment using a set of 51 test problems with different difficulty levels was conducted. Besides that, MODPSO coefficient tuning was also conducted to identify the best setting so as to optimize the problem. The results from this experiment indicated that the MODPSO algorithm presents a significant improvement in term of solution quality toward Pareto optimal and demonstrates the ability to explore the extreme solutions in the mixed-model assembly optimization search space. The originality of this research is on the new variant of integrated ASP and ALB problem. This paper is the first published research to model and optimize the integrated ASP and ALB research for mixed-model assembly problem.

The ability to successfully conduct radical innovations is mandatory for mature industrial companies that want to remain competitive in the global market. This ability relies on several ingredients, namely: (1) the structuring of the innovation process; (2) managerial principles; (3) methodological tools; (4) the presence of a culture of innovation. This paper reports about the impact of applying the User eXperience-Fuzzy Front End (UX-FFE) model, which brings together the systemic innovation process with the social, economical, and methodological aspects on the outcomes of the innovation process. Firstly, it appears that the operational performance of the upstream innovation process relies on the quality of the social context, intrinsic to the group of co-creators, corresponding to the reported perceived experience. Secondly, the UX-FFE model application, therefore, allows optimizing the upstream innovation process performance. Indeed, we argue that the evaluation of the co-creators perceived experience brings new opportunities to optimize the operational performance of the upstream innovation process. The first part of this paper presents deeper a theoretical model, named UX-FFE, which combines a UX approach with an upstream innovation process (FFE). The main interest of this UX-FFE model is that it allows evaluating the social aspect of the upstream innovation process, which may be detrimental to the success of radical innovation projects in mature companies. The second part presents the results of previous experiments that validated the model. The results allow the design of an instrument dedicated to the evaluation of the user experience of co-creators in the ideation stage. Finally, the third part reports about the experimentation of the UX-FFE in a mature company. Results present the impact of the co-creators' experience on the performance of radical innovation projects.