13 accepted papers
This letter introduces a novel probabilistic mapping algorithm, LatentBKI, which enables open-vocabulary mapping with quantifiable uncertainty. Traditionally, semantic mapping algorithms focus on a fixed set of semantic categories which limits their applicability for complex robotic tasks. Vision-La
In the highly complex and stochastic global, supply chain environments, local enterprise agents seek distributed and dynamic strategies for agile responses to disruptions. Existing literature explores both centralized and distributed approaches, while most work neglects temporal dynamics and the het
Robotic manipulation of deformable linear objects (DLOs) is an active area of research, though emerging applications, like automotive wire harness installation, introduce constraints that have not been considered in prior work. Confined workspaces and limited visibility complicate prior assumptions…
Robotic perception is currently at a cross-roads between modern methods, which operate in an efficient latent space, and classical methods, which are mathematically founded and provide interpretable, trustworthy results. In this paper, we introduce a Convolutional Bayesian Kernel Inference (Con-vBKI…
In previous studies, researchers have successfully measured walking in healthy able-bodied humans to create safe control strategies for lower body assistive devices. measurements used to establish design requirements often come from testing and evaluation that takes place in laboratory settings duri…
This work addresses a gap in semantic scene completion (SSC) data by creating a novel outdoor data set with accurate and complete dynamic scenes. Our data set is formed from randomly sampled views of the world at each time step, which supervises generalizability to complete scenes without occlusions
For robot swarms operating on complex missions in an uncertain environment, it is important that the decision-making algorithm considers both heterogeneity and uncertainty. This paper presents a stochastic programming framework for the vehicle routing problem with stochastic travel energy costs and…
Ground robot power consumption often varies significantly throughout an off-road environment, depending on the terrain. Previous traversals over the environment can inform future predictions through spatial mapping of collected power data. However, such predictions are particular to a single robot.
Mass customization and global competition require Cyber-Physical Systems of Systems (CPSoS) to become increasingly flexible. Modern CPSoS have to be able to create a wide and versatile variety of products, which takes centralized approaches to their limits. In addition, they have to produce these pr
Distributed multi-agent control has been proposed to improve the flexibility of manufacturing systems. Two important components of this control strategy are product agents, representing physical parts in the system, and resource agents, representing the system resources. Product agents request actio
Tracking of uncontrollable agents in a controlled environment is an important research question for the coordination of controllable and uncontrollable agents and bio-inspired multi-agent control. This paper presents a framework that approaches the multiagent tracking problem from a localization per…
Accurate biological agent localization offers the opportunity for both researchers and institutions to gain new knowledge about individual and group behaviors of biosystems. This paper presents a sensor-fusion approach for tracking biological agents, combining the data from automated video logging w…
An important issue in anomaly detection in smart manufacturing systems is the lack of consistency in the formal definitions of anomalies, faults, and attacks. The term anomaly is used to cover a wide range of situations that are addressed by different types of solutions. In this letter, we categoriz