This article presents a methodology to reduce the energy consumption of an industrial robot. We propose a design for a 3R serial manipulator of general geometry. We show an analytical model aiming to analyze the search space of architectures based on the torsion angles of the robot to determine the optimal architecture that allows the efficient use of energy. The analytical model provides a theoretical estimation of the energy consumption and is validated by monitoring the experimental robot. The numerical calculations obtained with a particular case reduced the energy consumption by approximately 7.5%.

In this paper, we have investigated the relativistic electron acceleration by plasma wave in an axially magnetized plasma by considering the self-magnetic field effects. We show that the optimum value of an external axial magnetic field could increase the electron energy gain more than 40% than that obtained in the absence of the magnetic field. Moreover, results demonstrate that the self-magnetic field produced by the electric current of the energetic electrons plays a significant role in the plasma wakefield acceleration of electron. In this regard, it will be shown that taking into account the self-magnetic field can increase the electron energy gain up to 36% for the case with self-magnetic field amplitude Ωs = 0.3 and even up to higher energies for the systems containing stronger self-magnetic field. The effects of plasma wave amplitude and phase, the ion channel field magnitude, and the electron initial kinetic energy on the acceleration of relativistic electron have also been investigated. A scaling law for the optimization of the electron energy is eventually proposed.

The Mecanum wheel is one of the practical omni-directional wheel designs in industry, especially for heavy-duty tasks in a confined floor. An issue with Mecanum-wheeled robots is inefficient use of energy. In this study, the robotic motion trajectories are optimized to minimize the energy consumption, where a robotic path is expressed in polynomial functions passing through a given set of via points, and a genetic algorithm is used to find the polynomial’s coefficients being decision variables. To attempt a further reduction in the energy consumption, the via points are also taken as decision variables for the optimization. Both simulations and experiments are conducted, and the results show that the optimized trajectories result in a significant reduction in energy consumption, which can be further lowered when the via points become decision variables. It is also found that the higher the order of the polynomials the larger the reduction in the energy consumption.