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The purpose of this paper is to present the development and validation of a methodology which allows modeling and solving the inverse and direct dynamic problem in real time in robot manipulators.

The robot dynamic equation is based on the Gibbs‐Appell equation of motion, yielding a well‐structured set of equations that can be computed in real time. This paper deals with the implementation and calculation of the inverse and direct dynamic problem in robots, with an application to the real‐time control of a PUMA 560 industrial robot provided with an open control architecture based on an industrial personal computer.

The experimental results show the validity of the dynamic model and that the proposed resolution method for the dynamic problem in real time is suitable for control purposes.

The accuracy of the applied friction model determines the accuracy of the identified overall model and consequently of the control. This is especially obvious in the case of the PUMA 560 robot, in which the presence of friction is remarkable in some of their joints. Hence, future work should focus on identifying a more precise friction model. The robot model could also be extended by incorporating rotor dynamics and could be applied for different robot configurations as parallel robots.

Gibbs‐Appell equations are used in order to develop the robotic manipulator dynamic model, instead of more usual dynamics formulations, due to several advantages that these exhibit. The obtained non‐physical identified parameters are adapted in order to enable their use in a control algorithm.

This paper aims to improve the accuracy of robot payload identification and decrease the complexity in its industrial application by developing a new method based on the actuator current.

Instead of previous general robot dynamic modeling of the actuators, links, together with payload inertial parameters, the paper discovers that the difference of the actuator torque between the robot moving along the same trajectory with and without carrying payload can be described as a function of the payload inertial parameters directly. Then a direct dynamic identification model of payload is built, a set of specialized novel exciting trajectories are designed for accurate identification and the least square method is applied for the estimation of the load parameters.

The experiments confirm the effectiveness of the proposed method in robot payload identification. The identification accuracy is greatly improved compared with that of existing methods based on the actuator current and is close to the accuracy of the methods that direct use the wrist-mounted force-torque sensor.

As the provided experiments indicate, the proposed method expands the application range and greatly improves the accuracy, hence making payload identification fully operational in the industrial application.

The novelty of such an identification method is that it does not require the rotor inertias and inertial parameters of links as a prior knowledge, and the specially designed trajectories provide completed decoupling of the load parameters.

The purpose of this paper is to enhance control accuracy, energy efficiency and productivity of customized industrial robots by the proposed multi-objective trajectory optimization approach. To obtain accurate dynamic matching torques of the robot joints with optimal motion, an improved dynamic model built by a novel parameter identification method has been proposed.

This paper proposes a novel multi-objective optimal approach to minimize the time and energy consumption of robot trajectory. First, the authors develop a reliable dynamic parameters identification method to obtain joint torques for formulating the normalized energy optimization function and dynamic constraints. Then, optimal trajectory variables are solved by converting the objective function into relaxation constraints based on second-order cone programming and Runge–Kutta discrete method to reduce the solving complexity.

Extensive experiments via simulation and in real customized robots are conducted. The results of this paper illustrate that the accuracy of joint torque predicted by the proposed model increases by 28.79% to 79.05% over the simplified models used in existing optimization studies. Meanwhile, under the same solving efficiency, the proposed optimization trajectory consumes a shorter time and less energy compared with the existing optimization ones and the polynomial trajectory.

A novel time-energy consumption optimal trajectory planning method based on dynamic identification is proposed. Most existing optimization methods neglect the effect of dynamic model reliability on energy efficiency optimization. A novel parameter identification approach and a complete dynamic torque model are proposed. Experimental results of dynamic matching torques verify that the control accuracy of optimal robot motion can be significantly improved by the proposed model.

This paper aims to propose a novel method to identify the parameters of robotic manipulators using the torque exerted by the robot joint motors (measured by current sensors).

Previous studies used additional sensors like force sensor and inertia measurement unit, or additional payload mounted on the end-effector to perform parameter identification. The settings of these previous works were complicated. They could only identify part of the parameters. This paper uses the torque exerted by each joint while performing Fourier periodic excited trajectories. It divides the parameters into a linear part and a non-linear part, and uses linear least square (LLS) parameter estimation and dual-swarm-based particle swarm optimization (DPso) to compute the linear and non-linear parts, respectively.

The settings are simpler and can identify the dynamic parameters, the viscous friction coefficients and the Coulomb friction coefficients of two joints at the same time. A SIASUN 7-Axis Flexible Robot is used to experimentally validate the proposal. Comparison between the predicted torque values and ground-truth values of the joints confirms the effectiveness of the method.

The proposed method identifies two joints at the same time with satisfying precision and high efficiency. The identification errors of joints do not accumulate.

The case study offers interesting learning possibilities and offers the following learning opportunities to the learner. assess and conduct a macro- and micro-environmental analysis, comprehend the nature of the competitive landscape and how it changes when one looks at a digital-only versus an omnichannel marketplace, examine the product mix and policy of the firm and evaluate how it delivers customer value and analyse the pros and cons of growth strategies available to a firm and arrive at a viable and actionable future business and product strategy.

The short case study presents the story of a young start-up called Country Delight. The firm began operations in 2011 and was the brainchild of Chakradhar Gade and Nitin Kaushal. The direct-to-consumer firm addressed urban consumers’ non-articulated, latent need to get “fresh and uncontaminated” milk to their doorstep. Country Delight delivered farmer-to-consumer fresh cow and buffalo milk and milk products based on a well-designed and efficient value chain where the supply chain was either wholly owned or quality monitored by the firm. The firm began operations in India’s National Capital Region and was spread across 15 metro cities. Slowly, over the years, Gade and Kaushal added more product categories.Country Delight had a subscriber base of around 500,000, and the ambitious duo wanted to double their subscriber base and reach one million subscribers by financial year 2025. The firm was looking at various paths to achieve this number. Should Country Delight expand into new geographies? Or look at adding to the existing product portfolio? Diversification into agritourism, like the Pune-based vineyard – Sula, also looked attractive to build consumer engagement. Would taking the consumer to the farmers from whom they sourced the milk and vegetables contribute additional revenue to Country Delight and their farmer-suppliers? As the firm got ready to raise another round of funding, it needed a well-articulated growth strategy that was exciting and profitable for all stakeholders.

This case study presents the dilemma entrepreneurs face as they look at the next phase of growth. Thus, this case study serves as a learning opportunity for a graduate-level course in management and as a sounding board for those who aspire to enter the start-up space. Though this case study has the potential to illustrate basic concepts such as value chain and macro- and micro-environment analysis, the protagonist’s dilemma and the problem statement make it apt for integrated discussions that are critical in advanced electives in marketing management.

Teaching notes are available for educators only.

CSS 8: Marketing.

The purpose of the paper is to propose an efficient and accurate force/torque (F/T) sensing method for the robotic wrist-mounted six-dimensional F/T sensor based on an excitation trajectory.

This paper presents an efficient and accurate F/T sensing method based on an excitation trajectory. First, the dynamic identification model is established by comprehensively considering inertial forces/torques, sensor zero-drift values, robot base inclination errors and forces/torques caused by load gravity. Therefore, the sensing accuracy is improved. Then, the excitation trajectory with optimized poses is used for robot following and data acquisition. The data acquisition is not limited by poses and its time can be significantly shortened. Finally, the least squares method is used to identify parameters and sense contact forces/torques.

Experiments have been carried out on the self-developed robot manipulator. The results strongly demonstrate that the proposed approach is more efficient and accurate than the existing widely-adopted method. Furthermore, the data acquisition time can be shortened from more than 60 s to 3 s/20 s. Thus, the proposed approach is effective and suitable for fast-paced industrial applications.

The main contributions of this paper are as follows: the dynamic identification model is established by comprehensively considering inertial forces/torques, sensor zero-drift values, robot base inclination errors and forces/torques caused by load gravity; and the excitation trajectory with optimized poses is used for robot following and data acquisition.

This paper aims to propose a dynamic parameter identification method based on sensitivity analysis for the 5-degree of freedom (DOF) hybrid robots, to solve the problems of too many identification parameters, complex model, difficult convergence of optimization algorithms and easy-to-fall into a locally optimal solution, and improve the efficiency and accuracy of dynamic parameter identification.

First, the dynamic parameter identification model of the 5-DOF hybrid robot was established based on the principle of virtual work. Then, the sensitivity of the parameters to be identified is analyzed by Sobol’s sensitivity method and verified by simulation. Finally, an identification strategy based on sensitivity analysis was designed, experiments were carried out on the real robot and the results were verified.

Compared with the traditional full-parameter identification method, the dynamic parameter identification method based on sensitivity analysis proposed in this paper converges faster when optimized using the genetic algorithm, and the identified dynamic model has higher prediction accuracy for joint drive forces and torques than the full-parameter identification models.

This work analyzes the sensitivity of the parameters to be identified in the dynamic parameter identification model for the first time. Then a parameter identification method is proposed based on the results of the sensitivity analysis, which can effectively reduce the parameters to be identified, simplify the identification model, accelerate the convergence of the optimization algorithm and improve the prediction accuracy of the identified model for the joint driving forces and torques.

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.

Entrepreneurship can be understood as a systemic phenomenon, thus relying on sets of influential factors associated with socioeconomic contexts. Institutional conditions play a pivotal role in this regard, affecting the allocation of entrepreneurial efforts. The goal of this research is to verify to what extent do the pillars of Countries' Institutional Profiles – regulatory, cognitive and normative – affect both the prevalence and quality of entrepreneurship, assessing the differences between developing and developed countries both in total entrepreneurial activity and in the following qualitative frames: innovation rate, high job creation expectations and motivational index.

The authors’ assessment uses data from the Global Entrepreneurship Monitor (GEM) with a longitudinal approach for 112 countries over the period 2003–2019. Dynamic panel data regressions are applied.

By comparing developing and developed countries, findings highlight that institutional effects are heterogeneous among developing and developed countries, with informal institutions being more relevant for developing countries than formal ones. Also, using a broad range of institutional indicators, the authors’ assessment indicated that the association between institutional conditions and productive entrepreneurship seems to be far more intricate than argued by theoretical literature.

The authors’ findings indicate the need for developing countries to address formal institutional voids in order to generate more effective conditions for productive entrepreneurship to emerge. Following prior literature, this can have systemic impacts on trajectories for economic growth and development.

The originality of this research consists in using a longitudinal and integrative approach to compare institutional effects on different types of entrepreneurship, as well as comparing these effects in countries at different stages of development.

The safety performance of cooperative robots is particularly important. This paper aims to study collision detection and response of cooperative robots, which meet the lightweight requirements of cooperative robots and help to ensure the safety of humans and robots.

This paper proposes a collision detection, recognition and response method based on dynamic models. First, this paper identifies the dynamic model of the robot. Second, an external torque observer is established based on the model, and a dynamic threshold collision detection method is designed to reduce the interference of model uncertainty on collision detection. Finally, a collision position and direction estimation method is designed, and a robot collision response strategy is proposed to reduce the harm caused by collisions to humans.

Comparative experiments are conducted on static threshold and dynamic threshold collision detection, and the results showed that the static threshold only detected one collision while the dynamic threshold could detect all collisions. Conducting collision position and direction estimation and collision response experiments, and the results show that this method can determine the location and direction of collision occurrence, and enable the robot to achieve collision separation.

This paper designs a dynamic threshold collision detection method that does not require external sensors. Compared with static threshold collision detection methods, this method can significantly improve the sensitivity of collision detection. This paper also proposes a collision position direction estimation method and collision separation response strategy, which can enable robots to achieve post collision separation and improve the safety of cooperative robots.