The global push for sustainability and energy efficiency is driving significant advancements across various industries, including the development of electrified solutions for heavy-duty mobile manipulators (HDMMs). Electromechanical linear actuators (EMLAs), powered by permanent magnet synchronous motors, present an all-electric alternative to traditional internal combustion engine (ICE)-powered hydraulic actuators, offering a promising path toward an eco-friendly future for HDMMs. However, the limited operational range of electrified HDMMs, closely tied to battery capacity, highlights the need to fully exploit the potential of EMLAs that drive the manipulators. This goal is contingent upon a deep understanding of the harmonious interplay between EMLA mechanisms and the dynamic behavior of heavy-duty manipulators. To this end, this paper introduces a bilevel multi-objective optimization framework, conceptualizing the EMLA-actuated manipulator of an electrified HDMM as a leader–follower scenario. At the leader level, the optimization algorithm maximizes EMLA efficiency by considering electrical and mechanical constraints, while the follower level optimizes the manipulator’s motion through a trajectory reference generator that adheres to manipulator limits. This optimization approach ensures that the system operates with a synergistic trade-off between the most efficient operating region of the actuation system, achieving a total efficiency of 70.3%. Furthermore, to complement this framework and ensure precise tracking of the generated optimal trajectories, a robust decomposed system control (RDSC) strategy is developed with accurate control and exponential stability. The proposed methodologies are validated on a 3-degrees-of-freedoms (DoFs) manipulator, demonstrating significant efficiency improvements while maintaining high-performance operation. Finally, experiments were conducted on an EMLA test bed under predefined optimal trajectories, simulating the dynamic load conditions of the manipulator’s lift joint and controlled with the developed RDSC. The results validate the effectiveness of the optimization framework and the control strategy.

The shift to electric and hybrid powertrains in vehicular systems has propelled advancements in mobile robotics and autonomous vehicles. This paper examines the sensitivity of key performance metrics in a electrified heavy-duty mobile manipulator (HDMM) driven by electromechanical linear actuators (EMLAs) powered by permanent magnet synchronous motors (PMSMs). The study evaluates power delivery, force dynamics, energy consumption, and overall efficiency of the actuation mechanisms. By computing partial derivatives (PD) with respect to the payload mass at the tool center point (TCP), it provides insights into these factors under various loading conditions. This research aids in the appropriate choice or design of EMLAs for HDMM electrification, addressing actuation mechanism selection challenge in vehicular system with mounted manipulator and determines the necessary battery capacity requirements.

Electrification, a key strategy in combating climate change, is transforming industries, and off-highway machines (OHM) will be next to transition from combustion engines and hydraulic actuation to sustainable fully electrified machines. Electromechanical linear actuators (EMLAs) offer superior efficiency, safety, and reduced maintenance, and they unlock vast potential for high-performance autonomous operations. However, a key challenge lies in optimizing the kinematic parameters of OHMs’ on-board manipulators for EMLA integration to exploit the full capabilities of actuation systems and maximize their performance. This work addresses this challenge by delving into the structural optimization of a prevalent closed kinematic chain configuration commonly employed in OHM manipulators. Our approach aims to retain the manipulator’s existing capabilities while reducing its energy expenditure, paving the way for a greener future in industrial automation, one in which sustainable and high-performing robotized OHMs can evolve. The feasibility of our methodology is validated through simulation results obtained on a commercially available parallel-serial heavy-duty manipulator mounted on a battery electric vehicle. The results demonstrate the efficacy of our approach in modifying kinematic parameters to facilitate the replacement of conventional hydraulic actuators with EMLAs, all while minimizing the overall energy consumption of the system.

Emission reduction targets in both highway trucks and off-road vehicles have turned the electrification focus into vehicle mountable equipment such as loader cranes and tail lifts. Nowadays, these devices are powered by hydraulic linear actuators, which provide cost-efficient and robust solutions that also fulfill lifting device safety standards. However, lifting device electrification has the potential to offer several advances, such as efficient utilization of on-board battery with fewer components and energy conversions. In order to contribute to the electrification trend, different aspects of substituting those conventional actuators with the almost new electromechanical linear actuators (EMLA) technology need to be investigated. This paper studies the different energy conversion processes in the EMLA for a heavy-duty mechanism and estimates the loss in each component. The investigation of energy conversion processes in EMLA paves the way for obtaining the efficiency map of the system and its power flow with the purpose of performance analysis. To this end, all the mentioned energy conversions are categorized into desirable and power loss categories. Subsequently, the dynamic model of one degree of freedom (DOF) parallel–serial mechanism is exerted in a trajectory optimization framework. Objectives such as minimum effort, energy expenditure, and delivered power are selected to generate optimal trajectories that feed the efficiency algorithms and examine the EMLA performance. The results show the efficacy of the above-mentioned trajectories concerning the criteria costs, total time, and efficiency of the whole system.

This paper presents a robot trajectory optimization formulation that builds upon numerical optimal control and Lie group methods. In particular, the inherent sparsity of direct collocation is carefully analyzed to dramatically reduce the number of floating-point operations to get first-order information of the problem. We describe how sparsity exploitation is employed with both numerical and analytical differentiation. Furthermore, the use of geometric algorithms based on Lie groups and their associated Lie algebras allow to analytically evaluate the state equations and their derivatives with efficient recursive algorithms. We demonstrate the scalability of the proposed formulation with three different articulated robots, such as a finger, a mobile manipulator and a humanoid composed of five, eight and more than twenty degrees of freedom, respectively. The performance of our implementation in C++ is also validated and compared against a state-of-the-art general purpose numerical optimal control solver.

This paper focuses on important implementation issues of numerical optimal control that are often overlooked. In particular, transcription methods should be carefully implemented for obtaining a discrete representation of the problem. For this purpose, we explain the algorithms to solve the minimum-effort problem by applying a direct collocation method based on B-Splines. In addition, we describe how to compute the gradient of the objective function as well as the Jacobian of the constraints without the use of finite differences and automatic differentiation. Geometric algorithms based on Lie groups and Lie algebra are examined to efficiently compute the analytical derivatives of the equations of motion of articulated robots. These ingredients allow the fast computation of dynamically feasible robot motions. We provide numerical comparisons with a biped robot to validate our recipe against classical direct collocation methods.