Selection/control concurrent optimization of BLDC motors for industrial robots

This paper aims to concurrently select and control off-the-shelf BLDC motors of industrial robots by using a synergistic model-based approach. The BLDC motors are considered with trapezoidal back-emf, where the three-phase (a,b,c) dynamics of motors are modeled in a mechatronic powertrain model of the robot for the selection and control problem, defining it as a multi-objective dynamic optimization problem with static and dynamic constraints. Since the mechanical and electrical actuators' parameters modify the robot's performance, the selection process considers the actuators' parameters, their control input, operational limits, and the mechanical output to the transmission of the robot joints. Then, three objective functions are to be minimized, the motor's energy consumption, the tracking error, and the total weight of installed motors on the robot mechanism. The control parameterization approach via a cascade controller with PI controllers for actuators' voltage and a PID controller for actuators' torque is used to solve the multi-objective dynamic optimization problem. Based on simulations of the closed-loop system, a Pareto front is obtained to examine trade-offs among the objective functions before implementing any actuators in the existing robotic system. The proposed method is tested on an experimental platform to verify its effectiveness. The performance of an industrial robot with the actuators originally installed is compared with the results obtained by the synergic approach. The results of this comparison show that 10.85% of electrical power can be saved, and the trajectory tracking error improved up to 57.41% using the proposed methodology.

An Almost Comprehensive Approach for the Choice of Motor and Transmission in Mechatronic Applications: Torque Peak of the Motor

The choice of motor and transmission to move a joint must ensure that the torque peaks of the motor lie inside its dynamic operating range. With this aim, this paper proposes an approach in which all the candidate transmissions are processed one by one to find among all the candidate motors those they could execute the reference task with. Consequently, all the transmission parameters, and not only its transmission ratio, are taken into consideration in advance. For rectangular dynamic operating ranges, this approach allows a direct and precise evaluation of all the admissible motor-transmission couples, without any approximation and further check. Apart from an entirely automated procedure, the method also provides diagrams through which the designer can concisely compare the admissible solutions. Furthermore, the method provides a solution for the drive systems in which the limit torque of the dynamic operating range does depend on the motor speed.

Compact Gearboxes for Modern Robotics: A Review

On the eve of Human-Robot-Interaction (HRI) becoming customary in our lives, the performance of HRI robotic devices remains strongly conditioned by their gearboxes. In most industrial robots, two relatively unconventional transmission technologies-Harmonic Drives© and Cycloid Drives-are usually found, which are not so broadly used in other industries. Understanding the origin of this singularity provides valuable insights in the search for suitable, future robotic transmission technologies. In this paper we propose an assessment framework strongly conditioned by HRI applications, and we use it to review the performance of conventional and emerging robotic gearbox technologies, for which the design criterion is strongly shifted toward aspects like weight and efficiency. The framework proposes to use virtual power as a suitable way to assess the inherent limitations of a gearbox technologies to achieve high efficiencies. This paper complements the existing research dealing with the complex interaction between gearbox technologies and the actuators, with a new gearbox-centered perspective particularly focused on HRI applications.

Placing and scheduling many depth sensors for wide coverage and efficient mapping in versatile legged robots

This article tackles the problem of designing 3D perception systems for robots with high visual requirements, such as versatile legged robots capable of different locomotion styles. In order to guarantee high visual coverage in varied conditions (e.g., biped walking, quadruped walking, ladder climbing), such robots need to be equipped with a large number of sensors, while at the same time managing the computational requirements that arise from such a system. We tackle this problem at both levels: sensor placement (how many sensors to install on the robot and where) and run-time acquisition scheduling under computational constraints (not all sensors can be acquired and processed at the same time). Our first contribution is a methodology for designing perception systems with a large number of depth sensors scattered throughout the links of a robot, using multi-objective optimization for optimal trade-offs between visual coverage and the number of sensors. We estimate the Pareto front of these objectives through evolutionary optimization, and implement a solution on a real legged robot. Our formulation includes constraints on task-specific coverage and design symmetry, which lead to reliable coverage and fast convergence of the optimization problem. Our second contribution is an algorithm for lowering the computational burden of mapping with such a high number of sensors, formulated as an information-maximization problem with several sampling techniques for speed. Our final system uses 20 depth sensors scattered throughout the robot, which can either be acquired simultaneously or optimally scheduled for low CPU usage while maximizing mapping quality. We show that, when compared with state-of-the-art robotic platforms, our system has higher coverage across a higher number of tasks, thus being suitable for challenging environments and versatile robots. We also demonstrate that our scheduling algorithm allows higher mapping performance to be obtained than with naïve and state-of-the-art methods by leveraging on measures of information gain and self-occlusion at low computational costs.

Non-Rectangular Dynamic Range of the Drive System: A New Approach for the Choice of Motor and Transmission

This paper deals with the choice of the motor-transmission couple to drive a joint in the frequent case in which the limit curve of the dynamic operating range of the drive system depends on the motor speed. The paper considers a given drive system and proposes a method that resorts to a succession of instant analyses during the reference task: for a transmission with no energy dissipation the result, if it exists, is a range of transmission ratios that can be coupled with the given motor in order to perform the reference task. The extension of the method to a real transmission is a diagram that correlates the transmission ratio and the direct and inverse efficiency of the reducer and that offers the designer an overview of the transmissions that can be coupled with the given motor to perform the reference task.

On the design of power gear trains: Insight regarding number of stages and their respective ratios

This paper presents a formulation for selecting the stage ratios and number of stages in a multistage transmission with a given desired total transmission ratio in a manner that maximizes efficiency, maximizes acceleration, or minimizes the mass of the transmission. The formulation is used to highlight several implications for gear train design, including the fact that minimizing rotational inertia and mass are competing objectives with respect to optimal selection of stage ratios, and that both rotational inertia and mass can often be minimized by increasing the total number of stages beyond a minimum realizable number. Additionally, a multistage transmission will generally provide maximum acceleration when the stage ratios increase monotonically from the motor to the load. The transmission will have minimum mass when the stage ratios decrease monotonically. The transmission will also provide maximum efficiency when the corresponding stages employ constant stage ratios. This paper aims to use this optimization formulation to elucidate tradeoffs between various common objectives in gear train design (efficiency, acceleration, and mass).

A unified design for lightweight robotic arms based on unified description of structure and drive trains

This article presents a unified design for lightweight robotic arms based on a unified description of structure and drive trains. In the unified design, the drive trains and structural dimensions are parameterized as design variables, and a major objective minimizes the total mass of robotic arms satisfying the constraint conditions and design criteria. To implement the optimization problem, a mapping relationship between mass and torque of drive trains is introduced as their power–density curves, which enable a unified description of structure and drive trains combining with the dynamics of robotic arms. In this implementation of unified design, there are two modules: structure optimization and drive trains design. The finite element method with nonlinear programming by quadratic Lagrange algorithm is adopted to implement the structure optimization. Moreover, the dynamic analysis in MSC ADAMS is achieved to design the drive trains of robotic arms. This method could uniformly evaluate all components of robotic arms in mass and continuously search the global optimal results. Finally, a design example on this unified design is compared with a referenced design to illustrate the validity and advantage of the proposed scheme.