arXiv subjects

Quang-Cuong Pham

Publications and source records attributed to Quang-Cuong Pham.

At least 37 records · Page 2Linked to original sources

arXiv2023

Task-Space Clustering for Mobile Manipulator Task Sequencing

Mobile manipulators have gained attention for the potential in performing large-scale tasks which are beyond the reach of fixed-base manipulators. The Robotic Task Sequencing Problem for mobile manipulators often requires optimizing the motion sequence of the robot to visit multiple targets while reducing the number of base placements. A two-step approach to this problem is clustering the task-space into clusters of targets before sequencing the robot motion. In this paper, we propose a task-space clustering method which formulates the clustering step as a Set Cover Problem using bipartite graph and reachability analysis, then solves it to obtain the minimum number of target clusters with corresponding base placements. We demonstrated the practical usage of our method in a mobile drilling experiment containing hundreds of targets. Multiple simulations were conducted to benchmark the algorithm and also showed that our proposed method found, in practical time, better solutions than the existing state-of-the-art methods.

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arXiv2022

Realtime Trajectory Smoothing with Neural Nets

In order to safely and efficiently collaborate with humans, industrial robots need the ability to alter their motions quickly to react to sudden changes in the environment, such as an obstacle appearing across a planned trajectory. In Realtime Motion Planning, obstacles are detected in real time through a vision system, and new trajectories are planned with respect to the current positions of the obstacles, and immediately executed on the robot. Existing realtime motion planners, however, lack the smoothing post-processing step -- which are crucial in sampling-based motion planning -- resulting in the planned trajectories being jerky, and therefore inefficient and less human-friendly. Here we propose a Realtime Trajectory Smoother based on the shortcutting technique to address this issue. Leveraging fast clearance inference by a novel neural network, the proposed method is able to consistently smooth the trajectories of a 6-DOF industrial robot arm within 200 ms on a commercial GPU. We integrate the proposed smoother into a full Vision--Motion Planning--Execution loop and demonstrate a realtime, smooth, performance of an industrial robot subject to dynamic obstacles.

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arXiv2022

DFBVS: Deep Feature-Based Visual Servo

Classical Visual Servoing (VS) rely on handcrafted visual features, which limit their generalizability. Recently, a number of approaches, some based on Deep Neural Networks, have been proposed to overcome this limitation by comparing directly the entire target and current camera images. However, by getting rid of the visual features altogether, those approaches require the target and current images to be essentially similar, which precludes the generalization to unknown, cluttered, scenes. Here we propose to perform VS based on visual features as in classical VS approaches but, contrary to the latter, we leverage recent breakthroughs in Deep Learning to automatically extract and match the visual features. By doing so, our approach enjoys the advantages from both worlds: (i) because our approach is based on visual features, it is able to steer the robot towards the object of interest even in presence of significant distraction in the background; (ii) because the features are automatically extracted and matched, our approach can easily and automatically generalize to unseen objects and scenes. In addition, we propose to use a render engine to synthesize the target image, which offers a further level of generalization. We demonstrate these advantages in a robotic grasping task, where the robot is able to steer, with high accuracy, towards the object to grasp, based simply on an image of the object rendered from the camera view corresponding to the desired robot grasping pose.

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arXiv2021

MoboTSP: Solving the Task Sequencing Problem for Mobile Manipulators

We introduce a new approach to tackle the mobile manipulator task sequencing problem. We leverage computational geometry, graph theory and combinatorial optimization to yield a principled method to segment the task-space targets into clusters, analytically determine reachable base pose for each cluster, and find task sequences that minimize the number of base movements and robot execution time. By clustering targets first and by doing so from first principles, our solution is more general and computationally efficient when compared to existing methods.

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arXiv2021

Learning Sequences of Manipulation Primitives for Robotic Assembly

This paper explores the idea that skillful assembly is best represented as dynamic sequences of Manipulation Primitives, and that such sequences can be automatically discovered by Reinforcement Learning. Manipulation Primitives, such as "Move down until contact", "Slide along x while maintaining contact with the surface", have enough complexity to keep the search tree shallow, yet are generic enough to generalize across a wide range of assembly tasks. Moreover, the additional "semantics" of the Manipulation Primitives make them more robust in sim2real and against model/environment variations and uncertainties, as compared to more elementary actions. Policies are learned in simulation, and then transferred onto a physical platform. Direct sim2real transfer (without retraining in real) achieves excellent success rates on challenging assembly tasks, such as round peg insertion with 0.04 mm clearance or square peg insertion with large hole position/orientation estimation errors.

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arXiv2020

Development of a Robotic System for Automated Decaking of 3D-Printed Parts

With the rapid rise of 3D-printing as a competitive mass manufacturing method, manual "decaking" - i.e. removing the residual powder that sticks to a 3D-printed part - has become a significant bottleneck. Here, we introduce, for the first time to our knowledge, a robotic system for automated decaking of 3D-printed parts. Combining Deep Learning for 3D perception, smart mechanical design, motion planning, and force control for industrial robots, we developed a system that can automatically decake parts in a fast and efficient way. Through a series of decaking experiments performed on parts printed by a Multi Jet Fusion printer, we demonstrated the feasibility of robotic decaking for 3D-printing-based mass manufacturing.

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arXiv2020

Robotic Assembly across Multiple Contact Stiffnesses with Robust Force Controllers

Active Force Control (AFC) is an important scheme for tackling high-precision robotic assembly. Classical force controllers are highly surface-dependent: the controller must be carefully tuned for each type of surface in contact, in order to avoid instabilities and to achieve a reasonable performance level. Here, we build upon the recently-developed Convex Controller Synthesis (CCS) to enable high-precision assembly across a wide range of surface stiffnesses without any surface-dependent tuning. Specifically, we demonstrate peg-in-hole assembly with 100 micron clearance, initial position uncertainties up to 2 cm, and for four types of peg and hole materials -- rubber, plastic, wood, aluminum -- whose stiffnesses range from 10 to 100 N/mm, using a single controller.

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arXiv2020

Convex Controller Synthesis for Robot Contact

Controlling contacts is truly challenging, and this has been a major hurdle to deploying industrial robots into unstructured/human-centric environments. More specifically, the main challenges are: (i) how to ensure stability at all times; (ii) how to satisfy task-specific performance specifications; (iii) how to achieve (i) and (ii) under environment uncertainty, robot parameters uncertainty, sensor and actuator time delays, external perturbations, etc. Here, we propose a new approach -- Convex Controller Synthesis (CCS) -- to tackle the above challenges based on robust control theory and convex optimization. In two physical interaction tasks -- robot hand guiding and sliding on surfaces with different and unknown stiffnesses -- we show that CCS controllers outperform their classical counterparts in an essential way.

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arXiv2019

Tight estimates of exit and containment probabilities for nonlinear stochastic systems

Tight estimates of exit/containment probabilities are of particular importance in many control problems. Yet, estimating the exit/containment probabilities is non-trivial: even for linear systems (Ornstein-Uhlenbeck processes), the containment probability can be computed exactly for only some particular values of the system parameters. In this paper, we derive tight bounds on the containment probability for a class of nonlinear stochastic systems. The core idea is to compare the "pull strength" (how hard the deterministic part of the system dynamics pulls towards the origin) experienced by the nonlinear system at hand with that of a well-chosen process for which tight estimates of the containment probability are known or can be numerically obtained (e.g. an Ornstein-Uhlenbeck process). Specifically, the main technical contribution of this paper is to define a suitable dominance relationship between the pull strengths of two systems and to prove that this dominance relationship implies an order relationship between their containment probabilities. We also discuss the link with contraction theory and highlight some examples of applications.

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arXiv2019

SCALAR: Simultaneous Calibration of 2D Laser and Robot Kinematic Parameters Using Planarity and Distance Constraints

In this paper, we propose SCALAR, a calibration method to simultaneously calibrate the kinematic parameters of a 6-DoF robot and the extrinsic parameters of a 2D Laser Range Finder (LRF) attached to the robot's flange. The calibration setup requires only a flat plate with two small holes carved on it at a known distance from each other, and a sharp tool-tip attached to the robot's flange. The calibration is formulated as a nonlinear optimization problem where the laser and the tool-tip are used to provide planar and distance constraints, and the optimization problem is solved using Levenberg-Marquardt algorithm. We demonstrate through experiments that SCALAR can reduce the mean and the maximum tool position error from 0.44 mm to 0.19 mm and from 1.41 mm to 0.50 mm, respectively.

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arXiv2019

Development of a robotic system for automatic organic chemistry synthesis

Automated chemical synthesis carries great promises of safety, efficiency and reproducibility for both research and industry laboratories. Current approaches are based on specifically-designed automation systems, which present two major drawbacks: (i) existing apparatus must be modified to be integrated into the automation systems; (ii) such systems are not flexible and would require substantial re-design to handle new reactions or procedures. In this paper, we propose a system based on a robot arm which, by mimicking the motions of human chemists, is able to perform complex chemical reactions without any modifications to the existing setup used by humans. The system is capable of precise liquid handling, mixing, filtering, and is flexible: new skills and procedures could be added with minimum effort. We show that the robot is able to perform a Michael reaction, reaching a yield of 34%, which is comparable to that obtained by a junior chemist (undergraduate student in Chemistry).

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arXiv2019

Planning coordinated motions for tethered planar mobile robots

This paper considers the motion planning problem for multiple tethered planar mobile robots. Each robot is attached to a fixed base by a flexible cable. Since the robots share a common workspace, the interactions amongst the robots, cables, and obstacles pose significant difficulties for planning. Previous works have studied the problem of detecting whether a target cable configuration is intersecting (or entangled). Here, we are interested in the motion planning problem: how to plan and coordinate the robot motions to realize a given non-intersecting target cable configuration. We identify four possible modes of motion, depending on whether (i) the robots move in straight lines or following their cable lines; (ii) the robots move sequentially or concurrently. We present an in-depth analysis of Straight & Concurrent, which is the most practically-interesting mode of motion. In particular, we propose algorithms that (a) detect whether a given target cable configuration is realizable by a Straight & Concurrent motion, and (b) return a valid coordinated motion plan. The algorithms are analyzed in detail and validated in simulations and in a hardware experiment.

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arXiv2019

Siamese Convolutional Neural Network for Sub-millimeter-accurate Camera Pose Estimation and Visual Servoing

Visual Servoing (VS), where images taken from a camera typically attached to the robot end-effector are used to guide the robot motions, is an important technique to tackle robotic tasks that require a high level of accuracy. We propose a new neural network, based on a Siamese architecture, for highly accurate camera pose estimation. This, in turn, can be used as a final refinement step following a coarse VS or, if applied in an iterative manner, as a standalone VS on its own. The key feature of our neural network is that it outputs the relative pose between any pair of images, and does so with sub-millimeter accuracy. We show that our network can reduce pose estimation errors to 0.6 mm in translation and 0.4 degrees in rotation, from initial errors of 10 mm / 5 degrees if applied once, or of several cm / tens of degrees if applied iteratively. The network can generalize to similar objects, is robust against changing lighting conditions, and to partial occlusions (when used iteratively). The high accuracy achieved enables tackling low-tolerance assembly tasks downstream: using our network, an industrial robot can achieve 97.5% success rate on a VGA-connector insertion task without any force sensing mechanism.

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arXiv2019

Locating Transparent Objects to Millimetre Accuracy

Transparent surfaces, such as glass, transmit most of the visible light that falls on them, making accurate pose estimation challenging. We propose a method to locate glass objects to millimetre accuracy using a simple Laser Range Finder (LRF) attached to the robot end-effector. The method, derived from a physical understanding of laser-glass interactions, consists of (i) sampling points on the glass border by looking at the glass surface from an angle of approximately 45 degrees, and (ii) performing Iterative Closest Point registration on the sampled points. We verify experimentally that the proposed method can locate a transparent, non-planar, side car glass to millimetre accuracy.

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arXiv2019

Critically fast pick-and-place with suction cups

Fast robotics pick-and-place with suction cups is a crucial component in the current development of automation in logistics (factory lines, e-commerce, etc.). By "critically fast" we mean the fastest possible movement for transporting an object such that it does not slip or fall from the suction cup. The main difficulties are: (i) handling the contact between the suction cup and the object, which fundamentally involves kinodynamic constraints; and (ii) doing so at a low computational cost, typically a few hundreds of milliseconds. To address these difficulties, we propose (a) a model for suction cup contacts, (b) a procedure to identify the contact stability constraint based on that model, and (c) a pipeline to parameterize, in a time-optimal manner, arbitrary geometric paths under the identified contact stability constraint. We experimentally validate the proposed pipeline on a physical robot system: the cycle time for a typical pick-and-place task was less than 5 seconds, planning and execution times included. The full pipeline is released as open-source for the robotics community.

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arXiv2019

A probabilistic framework for tracking uncertainties in robotic manipulation

Precisely tracking uncertainties is crucial for robots to successfully and safely operate in unstructured and dynamic environments. We present a probabilistic framework to precisely keep track of uncertainties throughout the entire manipulation process. In agreement with common manipulation pipelines, we decompose the process into two subsequent stages, namely perception and physical interaction. Each stage is associated with different sources and types of uncertainties, requiring different techniques. We discuss which representation of uncertainties is the most appropriate for each stage (e.g. as probability distributions in SE(3) during perception, as weighted particles during physical interactions), how to convert from one representation to another, and how to initialize or update the uncertainties at each step of the process (camera calibration, image processing, pushing, grasping, etc.). Finally, we demonstrate the benefit of this fine-grained knowledge of uncertainties in an actual assembly task.

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arXiv2018

3D Convolution on RGB-D Point Clouds for Accurate Model-free Object Pose Estimation

The conventional pose estimation of a 3D object usually requires the knowledge of the 3D model of the object. Even with the recent development in convolutional neural networks (CNNs), a 3D model is often necessary in the final estimation. In this paper, we propose a two-stage pipeline that takes in raw colored point cloud data and estimates an object's translation and rotation by running 3D convolutions on voxels. The pipeline is simple yet highly accurate: translation error is reduced to the voxel resolution (around 1 cm) and rotation error is around 5 degrees. The pipeline is also put to actual robotic grasping tests where it achieves above 90% success rate for test objects. Another innovation is that a motion capture system is used to automatically label the point cloud samples which makes it possible to rapidly collect a large amount of highly accurate real data for training the neural networks.

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arXiv2018

Printing-while-moving: a new paradigm for large-scale robotic 3D Printing

Building and Construction have recently become an exciting application ground for robotics. In particular, rapid progress in materials formulation and in robotics technology has made robotic 3D Printing of concrete a promising technique for in-situ construction. Yet, scalability remains an important hurdle to widespread adoption: the printing systems (gantry- based or arm-based) are often much larger than the structure to be printed, hence cumbersome. Recently, a mobile printing system - a manipulator mounted on a mobile base - was proposed to alleviate this issue: such a system, by moving its base, can potentially print a structure larger than itself. However, the proposed system could only print while being stationary, imposing thereby a limit on the size of structures that can be printed in a single take. Here, we develop a system that implements the printing-while-moving paradigm, which enables printing single-piece structures of arbitrary sizes with a single robot. This development requires solving motion planning, localization, and motion control problems that are specific to mobile 3D Printing. We report our framework to address those problems, and demonstrate, for the first time, a printing-while-moving experiment, wherein a 210 cm x 45 cm x 10 cm concrete structure is printed by a robot arm that has a reach of 87 cm.

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