Teleoperated robotic manipulators can be used to remotely operate within hazardous, hard to reach or dangerous environments. In tasks requiring handling of heavy objects with high forces, hydraulic manipulators have remained the most practical solution. Contrary to the previous research on teleoperation of hydraulic manipulators based on linearization and linear control theory, the present study proposes a full-dynamics-based bilateral force-reflected teleoperation, designed between a multiple degrees-of-freedom (n-DOF) electrical master manipulator and an n-DOF hydraulic slave manipulator. The used teleoperation method allows arbitrary motion and force scaling between the n-DOF manipulators, effectively enabling the use of two greatly dissimilar manipulators. The proposed teleoperation system is demonstrated with a full-scale two-DOF hydraulic slave manipulator (having 475 kg payload attached to the tip) in a free-space motion task, and in a constrained motion task including both real and virtual constraints in the environment. Despite the inherent highly nonlinear dynamic behaviour of hydraulic systems and challenges in realizing a bilateral teleoperation, the experimental results demonstrate that the proposed controller for full-dynamics-based teleoperation 1) can rigorously address the system nonlinearities, 2) can realize a high-performance bilateral teleoperation with hydraulic slave manipulators, and 3) is capable to operate in constrained motion with the environment having both real and virtual (i.e., artificially rendered) constraints.

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This study designs a high-precision bilateral teleoperation control for a dissimilar master-slave system. The proposed nonlinear control design takes advantage of a novel subsystem-dynamics-based control method that allows designing of individual (decentralized) model-based controllers for the manipulators locally at the subsystem level. Very importantly, a dynamic model of the human operator is incorporated into the control of the master manipulator. The individual controllers for the dissimilar master and slave manipulators are connected in a specific communication channel for the bilateral teleoperation to function. Stability of the overall control design is rigorously guaranteed with arbitrary time delays. Novel features of this study include the completely force-sensor-less design for the teleoperation system with a solution for a uniquely introduced computational algebraic loop, a method of estimating the exogenous operating force of an operator and the use of a commercial haptic manipulator. Most importantly, we conduct experiments on a dissimilar system in two degrees of freedom (DOFs). As an illustration of the performance of the proposed system, a force scaling factor of up to 800 and position scaling factor of up to 4 was used in the experiments. The experimental results show an exceptional tracking performance, verifying the real-world performance of the proposed concept.

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.

Crushing of blasted ore is an essential phase in extraction of valuable minerals in mining industry. It is typically performed in multiple stages with each stage producing finer fragmentation. Performance and throughput of the first stage of crushing is highly dependent on the size distribution of the blasted ore. In the crushing plant, a metal grate prevents oversized boulders from getting into the crusher jaws, and a human-controlled hydraulic manipulator equipped with a rock hammer is required to break oversized boulders and ensure continuous material flow. This secondary breaking task is event-based in the sense that ore trucks deliver boulders at irregular intervals, thus requiring constant human supervision to ensure continuous material flow and prevent blockages. To automatize such breaking tasks, an intelligent robotic control system along with a visual perception system (VPS) is essential. In this manuscript, we propose an autonomous breaker system that includes a VPS capable of detecting multiple irregularly shaped rocks, a robotic control system featuring a decision-making mechanism for determining the breaking order when dealing with multiple rocks, and a comprehensive manipulator control system. We present a proof of concept for an autonomous robotic boulder breaking system, which consists of a stereo-camera-based VPS and an industrial rock-breaking manipulator robotized with our retrofitted system design. The experiments in this study were conducted in a real-world setup, and the results were evaluated based on the success rates of breaking. The experiments yielded an average success rate of 34% and a break pace of 3.3 attempts per minute.