Alternating pressure control system for hydraulic robots

Hydraulics is a promising technology for robots. However, traditional hydraulic infrastructures are often large and power-inefficient, with large power sources that hinder mobility. In contrast, electro-hydrostatic actuators are relatively power efficient, but their cost and weight can be excessive in systems with a higher number of degrees of freedom. In this paper, we propose a new alternating pressure control system for hydraulic systems with a higher number of degrees of freedom based on an alternating pressure source system. In this system, the valves open and close in synchronization with a pump with sensor feedback, allowing either pressure or position in each actuator to be controlled independently. With the proposed system, a centralized pump can be used with simplified tubing and simple on–off valves. Moreover, we developed a dynamic duty ratio system that improves performance and reduces pump utilization time. The experimental results confirmed that both the position and pressure of each actuator can be controlled in parallel on a multi-degree-of-freedom system.     

The snowblower problem

We introduce the snowblower problem (SBP), a new optimization problem that is closely related to milling problems and to some material-handling problems. The objective in the SBP is to compute a short tour for the snowblower to follow to remove all the snow from a domain (driveway, sidewalk, etc.). When a snowblower passes over each region along the tour, it displaces snow into a nearby region. The constraint is that if the snow is piled too high, then the snowblower cannot clear the pile.We give an algorithmic study of the SBP. We show that in general, the problem is NP-complete, and we present polynomial-time approximation algorithms for removing snow under various assumptions about the operation of the snowblower. Most commercially available snowblowers allow the user to control the direction in which the snow is thrown. We differentiate between the cases in which the snow can be thrown in any direction, in any direction except backwards, and only to the right. For all cases, we give constant-factor approximation algorithms; the constants increase as the throw direction becomes more restricted. Our results are also applicable to robotic vacuuming (or lawnmowing) with bounded-capacity dust bin.     

Calibration of a multiple axes 3-D laser scanning system consisting of robot, portable laser scanner and turntable

A multiple axes 3-D laser scanning system consisting of a portable 3-D laser scanner, a industrial robot and a turntable is demonstrated. By using a criterion sphere, a robot tool center point (TCP) calibration approach is proposed to calibrate the relation between the laser 3-D scanner and the robot end-effector. In this approach, two different translational motions of robot are first made to determine the rotation part, and then at least three different rotational motions are made to determine the translation part. Meanwhile, by using the criterion sphere, a turntable approach is proposed to calibrate the pose of the turntable relative to the robot. In this approach, several rotational angles of turntable and two different heights of the sphere are made to determine the rotational axis of turntable. Experiment is performed on a portable laser scanner mounted on an industrial robot ABB IRB4400 with a turntable. The experiment results show that the two proposed calibration algorithms are stable and flexible. The application of 3-D measurement is also given to demonstrate the effectiveness and stability of the multiple axes 3-D laser scanning system.     

Passivity-based reinforcement learning control of a 2-DOF manipulator arm

Passivity-based control (PBC) is commonly used for the stabilization of port-Hamiltonian (PH) systems. The PH framework is suitable for multi-domain systems, for example mechatronic devices or micro-electro-mechanical systems. Passivity-based control synthesis for PH systems involves solving partial differential equations, which can be cumbersome. Rather than explicitly solving these equations, in our approach the control law is parameterized and the unknown parameter vector is learned using an actor–critic reinforcement learning algorithm. The key advantages of combining learning with PBC are: (i) the complexity of the control design procedure is reduced, (ii) prior knowledge about the system, given in the form of a PH model, speeds up the learning process, (iii) physical meaning can be attributed to the learned control law. In this paper we extended the learning-based PBC method to a regulation problem and present the experimental results for a two-degree-of-freedom manipulator. We show that the learning algorithm is capable of achieving feedback regulation in the presence of model uncertainties.     

Exploring cold regions autonomous operations

The US Army must update its vehicle fleet to be better equipped for potential future military conflicts in northern climates (US Army, 2017). This process involves considering manned, optionally manned, and unmanned vehicles as viable options in the future. Optionally manned and unmanned vehicles in the armed forces have substantial benefits because they can operate without direct driver input or are able to perform missions deemed too dangerous for troops. Optionally manned vehicles allow the driver to shift some, or all, focus away from the task of driving the vehicle. In some cases, these autonomous vehicles may perform better than a human driver by rapidly sensing and reacting to terrain changes. Onboard sensing and decision making are equally applicable to both fully autonomous and teleoperated vehicles. This work will focus on the terrain sensing, waypoint navigation, and teleoperation potential of an optionally manned or unmanned vehicle. Results from a vehicle demonstration on two different terrain conditions will provide the basis for additional terrain sensing and autonomous vehicle development work in the coming year.     

Dynamics modelling and control of the 3-RCC translational platform

The article presents the inverse dynamics model of a novel translating parallel machine and proposes the structure of a force controller for the execution of tasks characterised by interaction with the environment. The task space model of machine’s dynamics is obtained in an efficient and compact form by means of the principle of virtual work. A virtual prototyping environment has been set up to test by computer simulation the potential of such kinematic architecture: the resulting dynamics is rather poor, mainly due to the high moving masses, but it is shown that hybrid position/force control schemes should be able to provide good performances, including the case of rather difficult operations, such as the peg-in-hole assembly.     

A novel 3D path following control framework for robots performing surface finishing tasks

We describe a novel 3D path following control framework for articulated robots in applications where constant speed travel along a path is desirable, such as robotic surface finishing tasks. Given the desired robot configuration sequence with a list of waypoints along a path, a trajectory optimization scheme based on direct collocation is proposed to determine the Cartesian path and the maximum constant translation speed that are dynamically feasible. Employing the Hermite–Simpson collocation method, a Cartesian path is developed that not only preserves the characteristics of the original motion sequence but also satisfies the physical requirements of the robot kinematics and dynamics. Since joint velocity control is quite common in many industrial robots, we consider a 3D kinematic control in the robot tool frame with control inputs as rate of change of orientation. The objective for the translation motion is to achieve constant speed along the path tangent direction, and that of the orientation control is to orient the robot properly based on the path provided by a converging path planner. We describe the optimization procedure employed with the direct collocation method to obtain the desired Cartesian path, an arc-length based re-parametrization of the desired path, and a path planner that provides a converging path to the desired path. To perform the surface finishing operation, we further present the joint space control law that is converted from the synthesis of the proposed path following and impedance force control in the tool frame. To verify and evaluate the performance of the proposed framework, we have conducted extensive experiments with a six degrees-of-freedom (DOF) industrial robot for several paths that can be employed for surface finishing of a variety of industrial parts.     

Benchmarking of adjoint sensitivity-based optimization techniques using a vehicle ride case study

Several studies on multibody dynamics optimization have been conducted. One important limitation of these studies is their computational e?ciency, especially when optimizing a complex system’s performance. The co-authors developed a very e?cient optimization technique based on an adjoint sensitivity analysis methodology. The scope of this article is to validate this technique by conducting a benchmark analysis against some of the most popular optimization methods, including gradient-based optimization using finite differences, design of experiment using optimal Latin hypercube, and design of experiment using full factorial design matrix. A vehicle system is used as a case study for optimizing its ride comfort.     

Robot learning schemes that trade motion accuracy for command simplification

This study was inspired by the human motor control system in its ability to accommodate a wide variety of motions. By contrast, the biologically inspired robot learning controller usually encounters huge learning space problems in many practical applications. A hypothesis for the superiority of the human motor control system is that it may have simplified the motion command at the expense of motion accuracy. This tradeoff provides an insight into how fast and simple control can be achieved when a robot task does not demand high accuracy. Two motion command simplification schemes are proposed in this paper based on the equilibrium-point hypothesis for human motion control. Investigation into the tradeoff between motion accuracy and command simplification reported in this paper was conducted using robot manipulators to generate signatures. Signature generation involves fast handwriting, and handwriting is a human skill acquired via practice. Because humans learn how to sign their names after they learn how to write, in the second learning process, they somehow learn to trade motion accuracy for motion speed and command simplicity, since signatures are simplified forms of original handwriting. Experiments are reported that demonstrate the effectiveness of the proposed schemes.