Deep neural networks (DNNs) can enable precise control while maintaining low computational costs by circum-venting the need for dynamic modeling. However, the deployment of such black-box approaches remains challenging for heavy-duty wheeled mobile robots (WMRs), which are subject to strict international standards and prone to faults and disturbances. We designed a hierarchical control policy for heavy-duty WMRs, monitored by two safety layers with differing levels of authority. To this end, a DNN policy was trained and deployed as the primary control strategy, providing high-precision performance under nominal operating conditions. When external disturbances arise and reach a level of intensity such that the system performance falls below a predefined threshold, a low-level safety layer intervenes by deactivating the primary control policy and activating a model-free robust adaptive control (RAC) policy. This transition enables the system to continue operating while ensuring stability by effectively managing the inherent trade-off between system robustness and responsiveness. Regardless of the control policy in use, a high-level safety layer continuously monitors system performance during operation. It initiates a shutdown only when disturbances become sufficiently severe such that compensation is no longer viable and continued operation would jeopardize the system or its environment. The proposed synthesis of DNN and RAC policy guarantees uniform exponential stability of the entire WMR system while adhering to safety standards to some extent. The effectiveness of the proposed approach was further validated through real-time experiments using a 6,000 kg WMR.

Flexible manipulators offer high power-to-weight ratios and are well suited for challenging environments, but their control is complicated by structural flexibility. This paper presents a practical modeling and control framework that approximates the flexible link as an open chain of rigid segments, using data from an inertial measurement unit (IMU) sensor network. This replaces complex partial differential equation-based models with a tractable set of ordinary differential equations, enabling real-time implementation. A complementary filter recovers virtual system states from IMU data, despite noise and drift effects. Based on this model, we propose a robust subsystem-based model predictive control strategy for task space control. The controller is computationally efficient and avoids the complexity of controlling a high degree-of-freedom open chain based on the system’s modular structure. Experimental results using a long-reach flexible manipulator demonstrate improved tracking accuracy and smoother input generation, validated against laser-based position and force cell sensor measurements.

Undesired lateral and longitudinal wheel slippage can disrupt a mobile robot’s heading angle, traction, and, eventually, desired motion. This issue makes the robotization and accurate modeling of heavy-duty machinery very challenging because the application primarily involves off-road terrains, which are susceptible to uneven motion and severe slippage. As a step toward robotization in skid-steering heavy-duty robot (SSHDR), this paper aims to design an innovative robust model-free control system developed by neural networks to strongly stabilize the robot dynamics in the presence of a broad range of potential wheel slippages. Before the control design, the dynamics of the SSHDR are first investigated by mathematically incorporating slippage effects, assuming that all functional modeling terms of the system are unknown to the control system. Then, a novel tracking control framework to guarantee global exponential stability of the SSHDR is designed as follows: 1) the unknown modeling of wheel dynamics is approximated using radial basis function neural networks (RBFNNs); and 2) a new adaptive law is proposed to compensate for slippage effects and tune the weights of the RBFNNs online during execution. Simulation and experimental results verify the proposed tracking control performance of a 4,836 kg SSHDR operating on slippery terrain.

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.

Vast industrial investment along with increased academic research on heavy-duty hydraulic manipulators has unavoidably paved the way for their automatization, necessitating the design of robust and high-precision controllers. In this study, an orchestrated robust controller is designed to address the mentioned issue for generic manipulators with an anthropomorphic arm and spherical wrist. Thanks to virtual decomposition control (VDC), the entire robotic system is decomposed into subsystems, and a robust controller is designed at each local subsystem by considering unknown model uncertainties, unknown disturbances, and compound input nonlinearities. As such, radial basis function neural networks (RBFNNs) are incorporated into VDC to tackle unknown disturbances and uncertainties, resulting in novel decentralized RBFNNs. All robust local controllers designed at each local subsystem, then, are orchestrated to accomplish high-precision control. In the end, for the first time in the context of VDC, a semi-globally uniformly ultimate boundedness is achieved under the designed controller. The validity of the theoretical results is verified by performing extensive simulations and experiments on a 6-degrees-of-freedom industrial manipulator with a nominal lifting capacity of 600 kg at 5 meters reach. Comparing the simulation results with the state-of-the-art controllers along with provided experimental results, demonstrates that proposed method fulfilled all promises and performed excellently.

This paper introduces an innovative observer-based modular control strategy in a class of$n_{a}$-degree-of-freedom (DoF) fully electrified heavy-duty robotic manipulators (HDRMs) to 1) guarantee robustness in the presence of uncertainties and disturbances, 2) address the complexities arising from several interacting mechanisms, 3) ensure uniformly exponential stability, and 4) enhance overall control performance. To begin, the dynamic model of HDRM actuation systems, which exploits the synergy between cleantech electromechanical linear actuators (EMLAs) and permanent magnet synchronous motors (PMSMs), is investigated. In addition, the reference trajectories of each joint are computed based on direct collocation with B-spline curves to extract the key kinematic and dynamic quantities of HDRMs. To guarantee robust tracking of the computed trajectories by the actual motion states, a novel control methodology, called robust subsystem-based adaptive (RSBA) control, is enhanced through an adaptive state observer. The RSBA control addresses inaccuracies inherent in motion, including modeling errors, non-triangular uncertainties, and both torque and voltage disturbances, to which the EMLA-driven HDRM is susceptible. Furthermore, this approach is presented in a unified generic equation format for all subsystems to mitigate the complexities of the overall control system. By applying the RSBA architecture, the uniformly exponential stability of the EMLA-driven HDRM is proven based on the Lyapunov stability theory. The proposed RSBA control performance is validated through simulations and experiments of the scrutinized PMSM-powered EMLA-actuated mechanisms. Note to Practitioners—Following strict global regulations, such as the 2015 Paris Agreement, there has been significant attention paid to the electrification trend. In this regard, the advancement of zero-emission electromechanical linear actuator technology has played a substantial role in developing fully electrified HDRMs. However, these systems are highly nonlinear and complex, comprising several interacting components, such as electric motors, reduction gearboxes, screw mechanisms, and load-bearing structures. Each of these components is prone to adverse effects arising from inaccuracies in modeling equations, sensor readings, and torque or voltage disturbances. As a result, achieving high-performance control presents significant challenges for engineers and necessitates computationally intensive approaches in practice. This paper presents a subsystem-based approach, enhanced by a robust state observer, to 1) mitigate the impact of uncertainties and disturbances substantially, 2) alleviate the computational burden and complexity of the targeted system, 3) prove mathematical stability, and 4) offer highly accurate and fast tracking performance. The proposed approach employs the dynamic motion of the studied EMLA-actuated HDRM, decomposing it into distinct subsystems and introducing a unified generic equation control for all subsystems. This modularity feature paves the way for researchers to extend the proposed approach to address other intricate applications.

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.

This study proposes a new semi-analytical design and implementation method for nonlinear partial differential equation (PDE) control of a flexible manipulator. The proposed scheme considers the effects of the boundary input force and gravity on the payload, which results in non-homogenous boundary conditions. This objective is achieved based on a model transformation scheme for homogenizing boundary conditions, obtaining semi-analytical solutions for the corresponding PDE model. Model transformation is assigned as a hybrid exponential–polynomial function whose coefficients are conveniently calculable without the need for any additional boundary condition measurements. This eliminates the need to use intensive numerical solvers—for example, methods based on finite element analysis—and allows the implementation of sophisticated PDE control schemes considering fully nonlinear PDE models with high computation speed. The presented controller is robust to parametric model uncertainty due to its adaptive design. The precision and efficiency of calculating distributed states using the proposed model transformation are demonstrated based on experimental data for the flexible manipulator with respect to the ground truth camera-based motion capture system. Model transformation is also numerically implemented for the proposed nonlinear endpoint control method based on the original PDE model.Note to Practitioners—This paper investigates the difficulty of obtaining data describing the flexible manipulator pose required for precise control and analysis and proposes a computationally efficient method to overcome this issue.

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.

An algorithm that uses low-cost inertial measurement units (IMUs) for estimating link angles for floating base robotic platforms is proposed. Each link has four IMUs attached on its surfaces, and an Extended Kalman Filter (EKF) and a Complementary Filter (CF) are used for fusing the sensors' data. The algorithm is validated with a commercial mobile working machine, which consist of six degrees-of-freedom (DOF) wheeled base platform, and a 3-DOF hydraulic anthropomorphic arm. Although there are vibrational disturbances from the machine's diesel engine and deformation of the links themselves, the measured results from the planar motion of a floating base hydraulic arm show that the accuracy of the angle estimation is impressively less than 1 degree in the root mean square (RMS) error.

This paper presents, for the first time, a method for learning in-contact tasks from a teleoperated demonstration with a hydraulic manipulator. Due to the use of extremely powerful hydraulic manipulator, a force-reflected bilateral teleoperation is the most reasonable method of giving a human demonstration. An advanced subsystem-dynamic-based control design framework, virtual decomposition control (VDC), is used to design a stability-guaranteed controller for the teleoperation system, while taking into account the full nonlinear dynamics of the master and slave manipulators. The use of fragile force/torque sensor at the tip of the hydraulic slave manipulator is avoided by estimating the contact forces from the manipulator actuators' chamber pressures. In the proposed learning method, it is observed that a surface-sliding tool has a friction-dependent range of directions (between the actual direction of motion and the contact force) from which the manipulator can apply force to produce the sliding motion. By this intuition, an intersection of these ranges can be taken over a motion to robustly find a desired direction for the motion from one or more demonstrations. The compliant axes required to reproduce the motion can be found by assuming that all motions outside the desired direction is caused by the environment, signalling the need for compliance. Finally, the learning method is incorporated to a novel VDC-based impedance control method to learn compliant behaviour from teleoperated human demonstrations. Experiments with 2-DOF hydraulic manipulator with a 475kg payload demonstrate the suitability and effectiveness of the proposed method to perform learning from demonstration (LfD) with heavy-duty hydraulic manipulators.

This paper addresses the problem of pallet picking by an Articulated-Frame-Steering (AFS) hydraulic machine. We propose a macro-micro visual mobile manipulation architecture, where a smooth switching logic navigates the robot to pick an object. The state space is divided into several regions depending on the accuracy of the vision and robot's degrees of freedom. The control architecture benefits from the following phenomena: at distance, when the location of the object of interest is detected, its orientation may not be reliably estimated; at some closer distances, orientations also become available; and because pallets are wide with small height, yaw angle estimation are more accurate than pitch is. The switching logic is devised to control the corresponding degree of freedom of the mobile manipulator in each region. Moreover, in different regions, we employ different coordinate frames, namely an earth-fixed frame or an object-local frame, which is more natural for the problem in that region. We show that the architecture accomplishes the following: 1) it eliminates the need for replanning as the accuracy of pose estimation improves; and 2) it provides the mobile base with a longer corridor to steer toward the pallet and align its heading. We also incorporate a robust, accurate solution based on fiducial markers for object manipulation in unstructured outdoor environments and unfavorable weather conditions, which relies solely on a monocular camera for pallet detection. The presented experimental results demonstrate the superiority of the method, as the model starts following the target even when the pallet is still 6m away from the vehicle.

This paper, as a generalization of our previous works, presents a unified time-optimal path-following controller for Wheeled Mobile Robots (WMRs). Unlike other path-following controllers, we solve the path-following problem for all common categories of WMRs such as car-like, differential, omnidirectional, all wheels steerable and others. We show that the insertion of our path-following controller into the kinematic and non-holonomic constraints of the wheels, simplifies the otherwise impenetrable constraints, resulting in explicit monotonic functions between the velocity of the base and that of the wheels. Based on this foundation, we present a closed-form solution that keeps all the wheels' steering and driving velocities within their corresponding pre-specified bounds. Simulation data and experimental results from executing the controller in a real-time environment demonstrate the efficacy of the method.

We consider low-noise angular acceleration estimation for multi-axis robotic manipulators. The proposed model uses pairwise coupled inertial measurements across a section of the kinematic chain, which is reduced to a single rigid body. Experimental validation is built upon compact low-power micro-electro-mechanical (MEMS) components, installed on a full-scale heavy-duty mobile manipulator. While the model itself has a built-in mechanism for common-mode disturbance rejection, an adaptive transversal filter is devised for a further improvement. The results indicate a 40-80 fold suppression of high-frequency perturbations with respect to a baseline motion derivative, the discrete difference. As inertial sensors require no mechanical contact to rotating axes and the number of parameters is kept low, the model is easily applicable to motion control feedback of typical heavy-duty manipulators.

In this paper, a force-sensorless high-performance contact force/motion control approach is proposed for multiple-degree-of-freedom hydraulic manipulators. A rigorous stability proof for an entire hydraulic manipulator performing contact tasks is provided for the first time. The controller design for the manipulator is based on the recently introduced virtual decomposition control approach. As a significant novelty, the end-effector contact force is directly estimated from the manipulator's cylinder pressure data, which provides a practical solution for heavy-duty contact force control without engaging fragile force/torque sensors. In the experiments, the proposed controller achieved a force control accuracy of 4.1% at a desired contact force of 8000 N while in motion. This can be considered a significant result due to the hydraulic actuators' highly nonlinear behaviors, the coupled mechanical linkage dynamics, and the complex interaction dynamics between the manipulator and the environment.

This paper addresses the macro-micro configuration of a mobile manipulation problem for a forklift; specifically, it investigates pallet picking with visual feedback. A manipulator with limited degrees of freedom and differential constraint mobility, together with the large dimensions of the machine, requires reliable visual feedback (pallet pose) and navigation from relatively large distances. It has been shown that the problem can be divided into two parts in order to solve the related issues based on path following theories and visual servoing. Moreover, visual pallet detection is non-real-time and unreliable, especially due to large distances, unfavorable vibrations, and fast steering. To address these issues, we introduce a simple and efficient method that integrates the vision output with odometry and realizes a smooth and nonstop transition from global navigation to visual servoing. Real-world implementation on a small-sized forklift demonstrates the efficacy of the proposed macro-micro architecture.

Mobile robots with independently steerable wheels provide better robustness and efficiency compared to the other types of omnidirectional mobile robots. However, the non-holonomic constraints and singular configurations give rise to several challenging issues in exploiting the high maneuverability features of the robot. Many proposed motion controllers for such robots force the robot to stay outside of bulky regions around its singular points, which in turn limits the robot's dexterity. In this paper, which extends our previous works, we present an online trajectory generation along with a globally stable path following controller that enables the robot to follow any given smooth path and heading function. We show that the control signals extensively simplify the kinematic constraints and are utilized to develop an efficient online “Phase Plane” switching algorithm that bounds the velocities and accelerations of the actuators. Moreover, we show that the algorithm efficiently regulates the velocity of the robot around the singular configurations which allows the robot to realize wide ranges of complex maneuvers. The proposed control algorithm has been tested on iMoro(our four-wheeled independently steerable mobile manipulator), and the presented results show the efficacy of our method.

We consider solving telescopic manipulator link length using rate gyros and linear accelerometers. The research is built upon micro-electro-mechanical systems (MEMS) components for low-cost “strap-down” implementation. By formulating a standard inversion problem, the telescopic manipulator's link length is solved with the well-known Levenberg-Marquard algorithm in real-time. The inversion is based on linear accelerations of angular motion sensed by a triaxial MEMS accelerometer, which is attached to the tip of the telescopic extension. By fusing the operator control commands for the link extension actuator with the inverted length estimate of the telescopic link, experiments on a hydraulic heavy-duty manipulator demonstrate feasibility of our novel approach.

This paper presents closed-loop state feedback motion control of a heavy-duty hydraulic manipulator using solely micro-electro-mechanical systems (MEMS) rate gyroscopes and linear accelerometers for joint angular position, velocity and acceleration feedback. For benchmarking, incremental encoders with 2 million counts per revolution are also used to supply the joint motion state feedback. The two motion state estimation methods are compared using Cartesian path trajectory closed-loop control experiments with both position feedback-based proportional control and motion state-based feedback control. The experiments show that the proposed MEMS-based state feedback control yields comparable tracking results compared with the high accuracy encoder. Furthermore, the MEMS-based angular acceleration estimation in particular is free from typical differentiation induced noise amplification and post-filtering phase-lag.

Hydraulic actuators are well-known for their high power-to-weight ratio, rapid responses, compactness and reliable performance. However, one of the drawbacks of fluid power systems have been large energy losses. In this paper, our research objective is to develop both energy-efficient and high performance motion controller for heavy-duty hydraulic manipulators. We apply an unconventional Servo Meter-In Meter-Out (SMIMO) hydraulic valve control setup that is used to decouple hydraulic actuator load pressure level from load force to improve energy efficiency. The developed control system is based on the Virtual Decomposition Control (VDC) approach to guarantee the closed-loop system stability of the multi degree of freedom heavy-duty hydraulic crane driven by the proposed novel SMIMO VDC controller. Capability for approximately 42% lower energy consumption was achieved in the Cartesian motion trajectory experiments with the proposed novel controller compared with a conventional 4-way servo valve setup, without significant control performance deterioration.

Mobile robots with independently steerable wheels possess many high maneuverability features of omnidirectional robots while benefiting from better performance and capability of moving on rough terrains. However, motion control of such robots is a challenging task due to presence of singular configurations and unboundedly large steering velocities in the neighborhood of those singularities. Many proposed approaches rely on numerical solutions that keep the robot out of bulky regions around the singular points and hence lose some of the robot maneuverability. Based on a class of traditional path followers we design a new globally stable path following controller that exploits the high maneuverability of the platform. This design allows us to derive a set of closed-form analytical functions that describe the robot base velocity as a function of the wheels driving and steering velocities while abide to the robot non-holonomic constraints. Those functions are then utilized to find the maximum instantaneous velocity of the body that keeps the wheels velocities under the pre-specified bounds no matter how much the robot gets close or far from its singular configurations. The control algorithms developed in this paper have been evaluated on iMoro, a four wheel independently steered mobile manipulator designed and developed at IHA/TUT. Experimental data is also shown that show efficacy of the method.

We consider full motion state sensing of a rigid open-chain multi-body linkage assembly using rate gyros and linear accelerometers. The research is built upon micro-electromechanical systems (MEMS) components for low-cost “strap-down” implementation. Our emphasis is on direct lag-free joint angular acceleration sensing, for which a novel multi-MEMS configuration is motivated by motion control requirements. By using the multi-MEMS configuration, the bandwidth of the angular acceleration sensed is mostly proportional to the physical distances of linear accelerometers. The related joint position sensing, which is robust against linear and angular motion, is founded on the complementary and Kalman filtering principles for exclusive low delay. Experiments on a robotic vertically mounted three-link planar arm demonstrate the advantage of our key theoretical finding.

In this paper a novel hydraulic closed-loop motion control system has been proposed, designed and implemented on a heavy-duty 2-DOF hydraulic manipulator. A new unconventional hydraulic drive is introduced to remove the complex nonlinear interconnection between cylinder pressure levels, supply pressure and load force. The remaining nonlinear coupling of force and velocity is then removed by nonlinear controller design. New hardware combined with this proposed controller design is able to improve the controllability of the load with lower supply pressure values than conventional controllers. This leads to improved energy efficiency and is therefore of great practical and economic importance. This is a significant result since energy efficiency of closed-loop controlled hydraulics is generally known to be very low and improvements very difficult to obtain.