FATROP : A Fast Constrained Optimal Control Problem Solver for Robot Trajectory Optimization and Control

Trajectory optimization is a powerful tool for robot motion planning and control. State-of-the-art general-purpose nonlinear programming solvers are versatile, handle constraints in an effective way and provide a high numerical robustness, but they are slow because they do not fully exploit the optimal control problem structure at hand. Existing structure-exploiting solvers are fast but they often lack techniques to deal with nonlinearity or rely on penalty methods to enforce (equality or inequality) path constraints. This works presents FATROP: a trajectory optimization solver that is fast and benefits from the salient features of general-purpose nonlinear optimization solvers. The speed-up is mainly achieved through the use of a specialized linear solver, based on a Riccati recursion that is generalized to also support stagewise equality constraints. To demonstrate the algorithm's potential, it is benchmarked on a set of robot problems that are challenging from a numerical perspective, including problems with a minimum-time objective and no-collision constraints. The solver is shown to solve problems for trajectory generation of a quadrotor, a robot manipulator and a truck-trailer problem in a few tens of milliseconds. The algorithm's C++-code implementation accompanies this work as open source software, released under the GNU Lesser General Public License (LGPL). This software framework may encourage and enable the robotics community to use trajectory optimization in more challenging applications

An optimal open-loop strategy for handling a flexible beam with a robot manipulator

Fast and safe manipulation of flexible objects with a robot manipulator necessitates measures to cope with vibrations. Existing approaches either increase the task execution time or require complex models and/or additional instrumentation to measure vibrations. This paper develops a model-based method that overcomes these limitations. It relies on a simple pendulum-like model for modeling the beam, open-loop optimal control for suppressing vibrations, and does not require any exteroceptive sensors. We experimentally show that the proposed method drastically reduces residual vibrations -- at least 90% -- and outperforms the commonly used input shaping (IS) for the same execution time. Besides, our method can also execute the task faster than IS with a minor reduction in vibration suppression performance. The proposed method facilitates the development of new solutions to a wide range of tasks that involve dynamic manipulation of flexible objects.Comment: Submitted to ICRA 202

Ginger

https://digitalcommons.otterbein.edu/production_1999-2000/1004/thumbnail.jp

Improving productivity and worker conditions in assembly : part 2 : rapid deployment of learnable robot skills

Collaborative robots (cobots) have a strong potential to improve both productivity as well as the working conditions of assembly operators by assisting in their tasks and by decreasing their physical and cognitive stress. The use of cobots in factories however introduces multiple challenges: how should the overall assembly architecture look like? How to allocate specific (sub)tasks to the operator or the cobot? How to program and deploy the cobot? How to make changes to the robot program? In this paper dilogy, we briefly highlight our recent contributions to this field. In part I we presented our collaborative architecture for human-robot assembly tasks and discussed the working principles of our task allocation framework, based upon agent capabilities and ergonomic measurements. In this second part we focus on our programming by demonstration approach targeted at expediting the deployment of learnable robot skills

Optimization-Based Robot Programming with Application to Human-Robot Interaction (Optimalisatiegebaseerd robotprogrammeren met toepassing op mens-robot interactie)

Existing commercial robot control systems are designed for classical large-scale high-productivity industrial robot tasks, which are typically of low geometric complexity (e.g. point-to-point motions or tool trajectory tracking) and involve limited sensor data processing. In new robotic applications, more complex geometric motions and sensor-based control are becoming more and more relevant, and existing approaches reach their limit.As a first contribution, a new paradigm for representing and implementing sensor-based robot tasks is proposed, both for the task modeling as well as for the robot control and the estimation of task uncertainties. The new method is denoted Task Specification using Constraints or TaSC. The key element in this paradigm is that robot motions are viewed as solutions of constrained optimization problems, with sets of constraints and one or more objective functions. The selection of these constraints and objective functions is made on a higher discrete-decision level, the skill level. Three numerical problem formulations are proposed: a generic formulation and two specific cases. In the generic formulation, non-instantaneous constraints and objective functions (such as the time to execute a robot task) are supported, giving the robot programmer a lot of freedom in specifying robot tasks. In the two specific formulations, which are extended reformulations of existing approaches, the possible constraints and objective functions are restricted to instantaneous functions and known geometric paths respectively. In contrast to the generic case, these formulations however result in convex optimization problems that can be solved efficiently online and with guaranteed global optimality.Human-robot interaction is desired or even essential in many modern robotics applications such as robot-assisted rehabilitation. As a second contribution, the thesis presents an adaptive feedforward control approach for actively assisting humans in human-robot cooperation tasks. The control architecture can be fully integrated in the TaSC framework. In order to model motion trajectories, an invariant description-based parametric modeling approach for six degree-of-freedom motion trajectories is used. This approach facilitates building a library of motion models in a systematic way. A constrained-optimization-based parameter estimation technique is developed to solve the model parameters, both in a batch and in a recursive scheme.As a third contribution, the thesis discusses `best practices' for the concrete implementation of the developed techniques and their integration in existing robot controllers. This contribution is exemplified using a state-of-the-art robot system: the KUKA light-weight robot.Finally, as a fourth contribution, the thesis presents ample simulation as well as experimental results: time-optimal point-to-point motions with kinematic, operational-space and/or dynamic constraints, a laser tracing task using a redundant robot system, a bolt-nut assembly task using a humanoid robot, a box picking task involving a human and two 7 degree-of-freedom (DOF) robot arms, a teleoperation system based on two 7 DOF impedance-controlled robots, and last but not least, a robot assistant for the experimental analysis of the functioning/kinesiology of the upper extremity.nrpages: 176status: publishe

Knowledge-based Skill Programming Framework

The abstract was submitted and presented at the 38th Benelux Meeting on Systems and Control, Lommel, Belgium, on March 19 - 21, 2019.status: publishe

A composable skill programming framework for sensor-based robot tasks

presented orally at the 3rd Workshop on Semantic Policy and Action Representations for Autonomous Robots (SPAR)status: accepte

Composable Skill Programming Framework for Complex Sensor-Based Robot Tasks

status: publishe

Extending the Itasc Constraint-Based Robot Task Specification Framework to Time-Independent Trajectories and User-Configurable Task Horizons

In constraint-based programming, robot tasks are specified and solved as optimization problems with sets of constraints and one or multiple objective functions. In our previous work, we presented (i) a generic modeling approach for geometrically complex robot tasks, including the modeling of parametric uncertainty, in order to allow the robot task programmer to specify the optimization problem without explicitly writing down the different (possibly numerous and involved) constraint equations, and (ii) methods for solving these optimization problem online in the instantaneous case (reactive control), and offline in the non-instantaneous case (trajectory planning). This paper has two contributions. First, it extends our framework to include task constraints (e.g. tracking a curve) that are not given as explicit functions of time. These constraints are highly relevant in practice, for example to facilitate time-optimal path planning combined with other constraints. Second, it extends our framework to user-configurable task horizons when solving the optimization problem, to allow task programmers to make a trade-off between computational speed and (global) task optimality. Both of these novel framework extensions are illustrated by a time-optimal laser tracing experiment. © 2013 IEEE.status: publishe

CHORROBOT: framework to learn CHallenging Operations using Reactive ROBOT control

In this paper, we aim to expedite the deployment of challenging manipulation tasks in which motion and contact wrenches (forces and moments) are involved. To this end, we acquire motion and wrench signals with a platform-independent tool from a small set of demonstrations. This information is then parameterized using relevant features of the task, such as the Tool Center Point (TCP) and the trajectory degree-of-advancement. Subsequently, variability between the demonstrations is compactly encoded using Probabilistic Principal Component Analysis (PPCA). Finally, the task is transferred to a robot setup by specifying the robot behavior using a constraint-based task specification and control approach. This framework results in increasing the robustness of the system against different sources of uncertainties: imprecise sensors, the adaptation of the tool, and changes in the execution speed.status: publishe