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Continuum robots modeling, be it from a hard or soft class, is giving rise to several challenges compared with rigid robots. These challenges are mainly due to kinematic redundancy, dynamic nonlinearity and high flexibility. This paper aims initially at designing a hard class of continuum robots, namely, cable-driven continuum robot (CDCR) and equally at developing their kinematic and dynamic models.

First, the CDCR prototype is constructed, and its description is made. Second, kinematic models are established based on the constant curvature assumption and inextensible bending section. Third, by using the Lagrange method, the dynamic model is derived under some simplifications and based on the kinematic equations, in which the flexible backbone’s elasticity modulus was identified experimentally. Finally, the static model of the CDCR is also derived based on the dynamic model.

Numerical examples are carried out using Matlab software to verify the static and dynamic models. Moreover, the static model is validated by comparing the simulation’s results to the real measurements that have been provided with satisfactory results.

To reduce the complexity of the dynamic model’s expressions and avoid the numerical singularity when the bending angle is close to zero, some simplifications have been taken, especially for the kinetic energy terms, by using the nonlinear functions approximation. Hence, the main advantage of this analytical-approximate solution is that it can be applied in the bending angle that ranges up to 2p with reasonable errors, unlike the previously proposed techniques. Furthermore, the resulting dynamic model has, to some extent, the proprieties of simplicity, accuracy and fast computation time. Ultimately, the obtained results from the simulations and real measurements demonstrate that the considered CDCR’s static and dynamic models are feasible.

Robotic arms’ interactions with the external environment are growing more intricate, demanding higher control precision. This study aims to enhance control precision by establishing a dynamic model through the identification of the dynamic parameters of a self-designed robotic arm.

This study proposes an improved particle swarm optimization (IPSO) method for parameter identification, which comprehensively improves particle initialization diversity, dynamic adjustment of inertia weight, dynamic adjustment of local and global learning factors and global search capabilities. To reduce the number of particles and improve identification accuracy, a step-by-step dynamic parameter identification method was also proposed. Simultaneously, to fully unleash the dynamic characteristics of a robotic arm, and satisfy boundary conditions, a combination of high-order differentiable natural exponential functions and traditional Fourier series is used to develop an excitation trajectory. Finally, an arbitrary verification trajectory was planned using the IPSO to verify the accuracy of the dynamical parameter identification.

Experiments conducted on a self-designed robotic arm validate the proposed parameter identification method. By comparing it with IPSO1, IPSO2, IPSOd and least-square algorithms using the criteria of torque error and root mean square for each joint, the superiority of the IPSO algorithm in parameter identification becomes evident. In this case, the dynamic parameter results of each link are significantly improved.

A new parameter identification model was proposed and validated. Based on the experimental results, the stability of the identification results was improved, providing more accurate parameter identification for further applications.

This paper aims to generate an obstacle free real time optimal path in a cluttered environment for a two-wheeled mobile robot (TWMR).

This TWMR resembles an inverted pendulum having an intermediate body mounted on a robotic mobile platform with two wheels driven by two DC motors separately. In this article, a novel motion planning strategy named as DAYANI arc contour intelligent technique has been proposed for navigation of the two-wheeled self-balancing robot in a global environment populated by obstacles. The developed new path planning algorithm evaluates the best next feasible point of motion considering five weight functions from an arc contour depending upon five separate navigational parameters.

Authenticity of the proposed navigational algorithm has been demonstrated by computing the path length and time taken through a series of simulations and experimental verifications and the average percentage of error is found to be about 6%.

This robot dynamically stabilizes itself with taller configuration, can spin on the spot and rove along through obstacles with smaller footprints. This diversifies its areas of application to both indoor and outdoor environments especially with very narrow spaces, sharp turns and inclined surfaces where its multi-wheel counterparts feel difficult to perform.

A new obstacle avoidance and path planning algorithm through incremental step advancement by evaluating the best next feasible point of motion has been established and verified through both experiment and simulation.

The purpose of this paper is to model the aircraft-cargo’s coupling dynamics during ultra-low altitude heavy cargo airdrop and to design the aircraft’s robust flight control law counteracting its aerodynamic coefficients perturbation induced by ground effect and the disturbance from the sliding cargo inside.

Aircraft-cargo system coupling dynamics model in vertical plane is derived using the Kane method. Trimmed point is calculated when the cargo fixed in the cabin and then the approximate linearized motion equation of the aircraft upon it is derived. The robust stability and robust H_∞ optimal disturbance restraint flight control law are designed countering the aircraft’s aerodynamic coefficients perturbation and the disturbance moment, respectively.

Numerical simulation shows the effectiveness of the proposed control law with elevator deflection as a unique control input.

The model derived and control law designed in the paper can be applied to heavy cargo airdrop integrated design and relevant parameters choice.

The dynamics model derived is closed, namely, the model can be called in numerical simulation free of assuming the values of parachute’s extraction force or cargo’s relative sliding acceleration or velocity as seen in many literatures. The modeling is simplified using Kane method rather than Newton’s laws. The robust control law proposed is effective in guaranteeing the aircraft’s flight stability and disturbance restraint performance in the presence of aerodynamic coefficients perturbation.

The purpose of this paper is to describe the use of group concept mapping (GCM) as a tool for developing a conceptual model of an episode of acute, unscheduled care from illness or injury to outcomes such as recovery, death and chronic illness.

After generating a literature review drafting an initial conceptual model, GCM software (CS Global MAX^TM) is used to organize and identify strengths and directionality between concepts generated through feedback about the model from several stakeholder groups: acute care and non-acute care providers, patients, payers and policymakers. Through online and in-person population-specific focus groups, the GCM approach seeks feedback, assigned relationships and articulated priorities from participants to produce an output map that described overarching concepts and relationships within and across subsamples.

A clustered concept map made up of relational data points that produced a taxonomy of feedback was used to update the model for use in soliciting additional feedback from two technical expert panels (TEPs), and finally, a public comment exercise was performed. The results were a stakeholder-informed improved model for an acute care episode, identified factors that influence process and outcomes, and policy recommendations, which were delivered to the Department of Health and Human Services’s (DHHS) Assistant Secretary for Preparedness and Response.

This study provides an example of the value of cross-population multi-stakeholder input to increase voice in shared problem health stakeholder groups.

This paper provides GCM results and a visual analysis of the relational characteristics both within and across sub-populations involved in the study. It also provides an assessment of observational key factors supporting how different stakeholder voices can be integrated to inform model development and policy recommendations.

Soft robotics, regarded as a new research branch of robotics, has generated increasing interests in this decade and has demonstrated its outperformance in addressing safety issues when cooperating with human beings. However, there is still lack of accurate close-loop control because of the difficulty in acquiring feedback information and accurately modeling the system, especially in interactive environments. To this end, this paper aims to improve the controllability of the soft robot working in specific underwater environment. The system dynamics, which takes complicated hydrodynamics into account, is solved using Kane’s method. The dynamics-based adaptive visual servoing controller is proposed to realize accurate sensorimotor control.

This paper presents an image-based visual servoing control scheme for a cable-driven soft robot with a fixed camera observing the motions. The intrinsic and extrinsic parameters of the camera can be adapted online so that tedious camera calibration work can be eliminated. It is acknowledged that kinematics-based control can be only applied into tasks in the free space and has limitation in accelerating the motion speed of robot arms. That is, one must consider the unneglectable interaction effects generated from the environment and objectives when operating soft robots in such interactive control tasks. To extend the application of soft robots into underwater environment, the study models system dynamics considering complicated hydrodynamic effects. With the pre-knowledge of the external effects, the performance of the robot can be further improved by adding the compensation term into the controller.

The proposed controller has theoretically proved its convergence of image error, adaptive estimation error and the stability of the dynamical system based on Lyapunov’s analysis. The authors also validate the performance of the controller in positioning control task in an underwater environment. The controller shows its capacity of rapid convergence to and accurate tracking performance of a static image target in a physical experiment.

To the best of the authors’ knowledge, there is no such research before that has developed dynamics-based visual servoing controller which takes into account the environment interactions. This work can thus improve the control accuracy and enhance the applicability of soft robotics when operating in complicated environments.

This paper proposes a bar‐system graph representation for structural topology optimization using a genetic algorithm (GA).

Based on graph theory, a graph is first used to represent a skeletal structure consisting of joining paths in the design domain, each of which is represented by a chain subgraph with finite number of vertices. Based on the edges of this graph, a bar‐system representation is proposed to define all the bars and the resulting topology is obtained by mapping each bar with its corresponding thickness to the design domain which is discretized into a regular mesh. The design variables are thus reduced to the spatial distribution of the vertices and the thickness of each bar. This method combines the advantages of both continuum and ground structure optimization methods.

The overall procedure is applied to classical structural topology optimization problems and its good performance is illustrated in the numerical examples.

It is suggested that the present representation method is both physically meaningful and computationally effective in the framework of topological optimum design using GAs.

The purpose of this paper is to describe a dynamic and continuous process for evaluating entrepreneurship pedagogies to implement continuous improvement of entrepreneurship education in order to achieve increased student engagement in face-to-face classes. Pedagogy is argued to be a significant contributor to entrepreneurship education programmes, consisting of dynamic activities and initiatives within the scope of defined entrepreneurship education ecosystems.

A “minute paper” was used as a quick and convenient method to obtain qualitative data on student perceptions of different pedagogies. The research adopted an action-research strategy where data were analysed using concept mapping to identify key themes that the educator can use to further develop or modify the pedagogy during course delivery.

The research identified student perceptions of the nature of engagement with pedagogies, and of possible improvements that were used by the educator to increase student engagement during course delivery. Different pedagogies were found to have varying outcomes on students’ engagement with entrepreneurship learning, and as such, contextual and spatial factors have to be taken into account when implementing new and/or adjusted pedagogies.

Repeated application of the research method to different pedagogies was carried out in several deliveries of an undergraduate entrepreneurship foundation course in one university. As such, further research requires testing in various institutional and delivery contexts as well as comparisons of learning and other outcomes including entrepreneurial intentions between classes where particular pedagogies may or may not be used.

The approach described is relatively straightforward to implement, with marginal resource and time. It provides rich data that gives insights into student perceptions of engagement with an individual pedagogy that the educator can use to modify to modify in order to increase student engagement.

The paper describes a practical method for educators to evaluate and develop optimal pedagogies for a particular class or group of students. This method can be applied to small as well as large class sizes, and data analysis can be carried out in real time to make improvements during course delivery. Although this method is described in the context of entrepreneurship education, it can be applied to other fields of instruction.

The purpose of this paper is to review the progress made in arc welding automation using trajectory planning, seam tracking and control methodologies.

This paper discusses key issues in trajectory planning towards achieving full automation of arc welding robots. The identified issues in trajectory planning are real-time control, optimization methods, seam tracking and control methodologies. Recent research is considered and brief conclusions are drawn.

The major difficulty towards realizing a fully intelligent robotic arc welding system remains an optimal blend and good understanding of trajectory planning, seam tracking and advanced control methodologies. An intelligent trajectory tracking ability is strongly required in robotic arc welding, due to the positional errors caused by several disturbances that prevent the development of quality welds. An exciting prospect will be the creation of an effective hybrid optimization technique which is expected to lead to new scientific knowledge by combining robotic systems with artificial intelligence.

This paper illustrates the vital role played by optimization methods for trajectory design in arc robotic welding automation, especially the non-gradient approaches (those based on certain characteristics and behaviour of biological, molecular, swarm of insects and neurobiological systems). Effective trajectory planning techniques leading to real-time control and sensing systems leading to seam tracking have also been studied.

Business plan writing seems the panacea to gain stakeholder legitimacy and financial backing. Our chapter explores the contributions and disconnections between business plan writing and the start-up process for incubated technology entrepreneurs. The study is set in the South East Enterprise Platform Programme (SEEPP), an incubator programme for technology graduate entrepreneurs in the South East of Ireland. Using a purposive sample of technology entrepreneurs in start-up mode, we took a qualitative approach consisting of content analysis of 40 business plans and in-depth interviews with 25 technology entrepreneurs. Our research found that writing a detailed business plan constrains the technology entrepreneur’s natural penchant for action, compelling them to focus on business plan writing rather than enactment. Technology entrepreneurs favour a market-led rather than funding-led operational level document to plan, and learn from, near-term activities using milestones.