Searcharxiv⌕ Search

arXiv · 0705.1038

The Design of Parallel Kinematic Machine Tools Using Kinetostatic Performance Criteria

Abstract

Most industrial machine tools have a serial kinematic architecture, which means that each axis has to carry the following one, including its actuators and joints. High Speed Machining highlights some drawbacks of such architectures: heavy moving parts require from the machine structure high stiffness to limit bending problems that lower the machine accuracy, and limit the dynamic performances of the feed axes. That is why PKMs attract more and more researchers and companies, because they are claimed to offer several advantages over their serial counterparts, like high structural rigidity and high dynamic capacities. Indeed, the parallel kinematic arrangement of the links provides higher stiffness and lower moving masses that reduce inertia effects. Thus, PKMs have better dynamic performances. However, the design of a parallel kinematic machine tool (PKMT) is a hard task that requires further research studies before wide industrial use can be expected. Many criteria need to be taken into account in the design of a PKMT. We pay special attention to the description of kinetostatic criteria that rely on the conditioning of the Jacobian matrix of the mechanism. The organisation of this paper is as follows: next section introduces general remarks about PKMs, then is explained why PKMs can be interesting alternative machine tool designs. Then are presented existing PKMTs. An application to the design of a small-scale machine tool prototype developed at IRCCyN is presented at the end of this paper.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Félix Majou, Philippe Wenger, Damien Chablat. 2007-05-08. The Design of Parallel Kinematic Machine Tools Using Kinetostatic Performance Criteria. https://arxiv.org/abs/0705.1038

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

Development of a Low-Cost Prosthetic Hand Using Electromyography and Machine Learning

Electromyography (EMG) is a measure of muscular electrical activity and is used in many clinical/biomedical disciplines and modern human computer interaction. Myo-electric prosthetics analyze and classify the electrical signals recorded from the residual limb. The classified output is then used to control the position of motors in a robotic hand and a movement is produced. The aim of this project is to develop a low-cost and effective myo-electric prosthetic hand that would meet the needs of amputees in developing countries. The proposed prosthetic hand should be able to accurately classify five different patterns (gestures) using EMG recordings from three muscles and control a robotic hand accordingly. The robotic hand is composed of two servo motors allowing for two degrees of freedom. After establishing an efficient signal acquisition and amplification system, EMG signals were thoroughly analyzed in the frequency and time domains. Features were extracted from both domains and a shallow neural network was trained on the two sets of data. Results yielded an average classification accuracy of 94.8% and 93.2% for the time and frequency domains respectively. Furthermore, results showed a faster computation and response for the time domain analysis; hence, it was adopted for the classification system. A wrist rotation mechanism was designed and tested to add significant functionality to the prosthetic. The mechanism is controlled by two of the five gestures, one for each direction. Which added a third degree of freedom to the overall design. Finally, a tactile sensory feedback system which uses force sensors and vibration motors was developed to enable sensation of the force inflicted on the hand for the user.

cs.RO↗

Injecting Hallucinations in Autonomous Vehicles: A Component-Agnostic Safety Evaluation Framework

Perception failures in autonomous vehicles (AV) remain a major safety concern because they can directly compromise decision-making and contribute to unsafe behavior. To study how these failures affect safety, researchers typically inject artificial faults into real-world or simulated components and observe the resulting outcomes. However, existing fault injection (FI) studies often target a single sensor within the machine perception (MP) module, resulting in siloed frameworks that are difficult to generalize or integrate into unified simulation environments. This work addresses that limitation by reframing perception failures as hallucinations, that is, false perceptions that distort an AV's situational awareness and may trigger unsafe control actions. Building on this concept, we propose a configurable, component-agnostic hallucination injection framework that induces six plausible hallucination types in an open-source simulation environment. A total of 18,356 simulations (8,695 baseline and 9,661 with hallucinations injected) were executed while AVs crossed an unsignalized intersection with transversal traffic. The results statistically validate the framework and quantify the impact of each hallucination type on collisions and on the minimum distance between the AV and the nearest vehicle, used as a continuous proxy for the safety margin. Certain hallucinations, especially missed detection, blind region, angular drift, phantom perception, and latency, significantly increase the odds of collision, while linear drift significantly reduces minimum distances without producing a statistically significant increase in collision odds in the evaluated scenario. The proposed framework provides a scalable and statistically grounded method for evaluating AV safety under perception distortions and supports future research on fault tolerance and resilient AV design.

cs.RO↗

DeFM: Learning Foundation Representations from Depth for Robotics

Depth sensors are widely deployed across robotic platforms, and advances in fast, high-fidelity depth simulation have enabled robotic policies trained on depth observations to achieve robust sim-to-real transfer for a wide range of tasks. Despite this, representation learning for depth modality remains underexplored compared to RGB, where large-scale foundation models now define the state of the art. To address this gap, we present DeFM, a self-supervised foundation model trained entirely on depth images for robotic applications. Using a DINO-style self-distillation objective on a curated dataset of 60M depth images, DeFM learns geometric and semantic representations that generalize to diverse environments, tasks, and sensors. To retain metric awareness across multiple scales, we introduce a novel input normalization strategy. We further distill DeFM into compact models suitable for resource-constrained robotic systems. When evaluated on depth-based classification, segmentation, navigation, locomotion, and manipulation benchmarks, DeFM achieves state-of-the-art performance and demonstrates strong generalization from simulation to real-world environments. We release all our pretrained models, which can be adopted off-the-shelf for depth-based robotic learning without task-specific fine-tuning. Webpage: https://de-fm.github.io/

cs.RO↗