1,124 research outputs found
Intelligent Multi-Modal Sensing-Communication Integration: Synesthesia of Machines
In the era of sixth-generation (6G) wireless communications, integrated
sensing and communications (ISAC) is recognized as a promising solution to
upgrade the physical system by endowing wireless communications with sensing
capability. Existing ISAC is mainly oriented to static scenarios with
radio-frequency (RF) sensors being the primary participants, thus lacking a
comprehensive environment feature characterization and facing a severe
performance bottleneck in dynamic environments. To date, extensive surveys on
ISAC have been conducted but are limited to summarizing RF-based radar sensing.
Currently, some research efforts have been devoted to exploring multi-modal
sensing-communication integration but still lack a comprehensive review.
Therefore, we generalize the concept of ISAC inspired by human synesthesia to
establish a unified framework of intelligent multi-modal sensing-communication
integration and provide a comprehensive review under such a framework in this
paper. The so-termed Synesthesia of Machines (SoM) gives the clearest cognition
of such intelligent integration and details its paradigm for the first time. We
commence by justifying the necessity of the new paradigm. Subsequently, we
offer a definition of SoM and zoom into the detailed paradigm, which is
summarized as three operation modes. To facilitate SoM research, we overview
the prerequisite of SoM research, i.e., mixed multi-modal (MMM) datasets. Then,
we introduce the mapping relationships between multi-modal sensing and
communications. Afterward, we cover the technological review on
SoM-enhance-based and SoM-concert-based applications. To corroborate the
superiority of SoM, we also present simulation results related to dual-function
waveform and predictive beamforming design. Finally, we propose some potential
directions to inspire future research efforts.Comment: This paper has been accepted by IEEE Communications Surveys &
Tutorial
Human-Robot Perception in Industrial Environments: A Survey
Perception capability assumes significant importance for human–robot interaction. The
forthcoming industrial environments will require a high level of automation to be flexible and
adaptive enough to comply with the increasingly faster and low-cost market demands. Autonomous
and collaborative robots able to adapt to varying and dynamic conditions of the environment,
including the presence of human beings, will have an ever-greater role in this context. However, if
the robot is not aware of the human position and intention, a shared workspace between robots and
humans may decrease productivity and lead to human safety issues. This paper presents a survey on
sensory equipment useful for human detection and action recognition in industrial environments.
An overview of different sensors and perception techniques is presented. Various types of robotic
systems commonly used in industry, such as fixed-base manipulators, collaborative robots, mobile
robots and mobile manipulators, are considered, analyzing the most useful sensors and methods to
perceive and react to the presence of human operators in industrial cooperative and collaborative
applications. The paper also introduces two proofs of concept, developed by the authors for future
collaborative robotic applications that benefit from enhanced capabilities of human perception and
interaction. The first one concerns fixed-base collaborative robots, and proposes a solution for human
safety in tasks requiring human collision avoidance or moving obstacles detection. The second
one proposes a collaborative behavior implementable upon autonomous mobile robots, pursuing
assigned tasks within an industrial space shared with human operators
Actuators and sensors for application in agricultural robots: A review
In recent years, with the rapid development of science and technology, agricultural robots have gradually begun to replace humans, to complete various agricultural operations, changing traditional agricultural production methods. Not only is the labor input reduced, but also the production efficiency can be improved, which invariably contributes to the development of smart agriculture. This paper reviews the core technologies used for agricultural robots in non-structural environments. In addition, we review the technological progress of drive systems, control strategies, end-effectors, robotic arms, environmental perception, and other related systems. This research shows that in a non-structured agricultural environment, using cameras and light detection and ranging (LiDAR), as well as ultrasonic and satellite navigation equipment, and by integrating sensing, transmission, control, and operation, different types of actuators can be innovatively designed and developed to drive the advance of agricultural robots, to meet the delicate and complex requirements of agricultural products as operational objects, such that better productivity and standardization of agriculture can be achieved. In summary, agricultural production is developing toward a data-driven, standardized, and unmanned approach, with smart agriculture supported by actuator-driven-based agricultural robots. This paper concludes with a summary of the main existing technologies and challenges in the development of actuators for applications in agricultural robots, and the outlook regarding the primary development directions of agricultural robots in the near future
First Responders' Localization and Health Monitoring During Rescue Operations
Currently, first responders’ coordination and decision-making during res-cue, firefighting or police operations is performed via radio/GSM channels with some support of video streaming. In unknown premises, officers have no global situational awareness on operation status, which reduces coordination efficiency and increases decision making mistakes. This paper pro-poses a solution enabling the situational awareness by introducing an integrated operation workflow for actors localization and health monitoring. The solution will provide global situational awareness to both coordinators and actors, thereby increasing efficiency of coordination, reducing mistakes in decision making and diminishing risks of unexpected situations to appear. This will result in faster operation progress, lower number of human casualties and financial losses and, the most important, saved human lives in calamity situations
An Integrated Testbed for Cooperative Perception with Heterogeneous Mobile and Static Sensors
Cooperation among devices with different sensing, computing and communication capabilities provides interesting possibilities in a growing number of problems and applications including domotics (domestic robotics), environmental monitoring or intelligent cities, among others. Despite the increasing interest in academic and industrial communities, experimental tools for evaluation and comparison of cooperative algorithms for such heterogeneous technologies are still very scarce. This paper presents a remote testbed with mobile robots and Wireless Sensor Networks (WSN) equipped with a set of low-cost off-the-shelf sensors, commonly used in cooperative perception research and applications, that present high degree of heterogeneity in their technology, sensed magnitudes, features, output bandwidth, interfaces and power consumption, among others. Its open and modular architecture allows tight integration and interoperability between mobile robots and WSN through a bidirectional protocol that enables full interaction. Moreover, the integration of standard tools and interfaces increases usability, allowing an easy extension to new hardware and software components and the reuse of code. Different levels of decentralization are considered, supporting from totally distributed to centralized approaches. Developed for the EU-funded Cooperating Objects Network of Excellence (CONET) and currently available at the School of Engineering of Seville (Spain), the testbed provides full remote control through the Internet. Numerous experiments have been performed, some of which are described in the paper
Improving Efficiency of DNN-based Relocalization Module for Autonomous Driving with Server-side Computing
In this work, we present a novel framework for camera relocation in
autonomous vehicles, leveraging deep neural networks (DNN). While existing
literature offers various DNN-based camera relocation methods, their deployment
is hindered by their high computational demands during inference. In contrast,
our approach addresses this challenge through edge cloud collaboration.
Specifically, we strategically offload certain modules of the neural network to
the server and evaluate the inference time of data frames under different
network segmentation schemes to guide our offloading decisions. Our findings
highlight the vital role of server-side offloading in DNN-based camera
relocation for autonomous vehicles, and we also discuss the results of data
fusion. Finally, we validate the effectiveness of our proposed framework
through experimental evaluation
Robots learn to behave: improving human-robot collaboration in flexible manufacturing applications
L'abstract è presente nell'allegato / the abstract is in the attachmen
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