1,000 research outputs found
Space Science Opportunities Augmented by Exploration Telepresence
Since the end of the Apollo missions to the lunar surface in December 1972, humanity has exclusively conducted scientific studies on distant planetary surfaces using teleprogrammed robots. Operations and science return for all of these missions are constrained by two issues related to the great distances between terrestrial scientists and their exploration targets: high communication latencies and limited data bandwidth.
Despite the proven successes of in-situ science being conducted using teleprogrammed robotic assets such as Spirit, Opportunity, and Curiosity rovers on the surface of Mars, future planetary field research may substantially overcome latency and bandwidth constraints by employing a variety of alternative strategies that could involve: 1) placing scientists/astronauts directly on planetary surfaces, as was done in the Apollo era; 2) developing fully autonomous robotic systems capable of conducting in-situ field science research; or 3) teleoperation of robotic assets by humans sufficiently proximal to the exploration targets to drastically reduce latencies and significantly increase bandwidth, thereby achieving effective human telepresence.
This third strategy has been the focus of experts in telerobotics, telepresence, planetary science, and human spaceflight during two workshops held from October 3–7, 2016, and July 7–13, 2017, at the Keck Institute for Space Studies (KISS). Based on findings from these workshops, this document describes the conceptual and practical foundations of low-latency telepresence (LLT), opportunities for using derivative approaches for scientific exploration of planetary surfaces, and circumstances under which employing telepresence would be especially productive for planetary science. An important finding of these workshops is the conclusion that there has been limited study of the advantages of planetary science via LLT. A major recommendation from these workshops is that space agencies such as NASA should substantially increase science return with greater investments in this promising strategy for human conduct at distant exploration sites
Space Science Opportunities Augmented by Exploration Telepresence
Since the end of the Apollo missions to the lunar surface in December 1972, humanity has exclusively conducted scientific studies on distant planetary surfaces using teleprogrammed robots. Operations and science return for all of these missions are constrained by two issues related to the great distances between terrestrial scientists and their exploration targets: high communication latencies and limited data bandwidth.
Despite the proven successes of in-situ science being conducted using teleprogrammed robotic assets such as Spirit, Opportunity, and Curiosity rovers on the surface of Mars, future planetary field research may substantially overcome latency and bandwidth constraints by employing a variety of alternative strategies that could involve: 1) placing scientists/astronauts directly on planetary surfaces, as was done in the Apollo era; 2) developing fully autonomous robotic systems capable of conducting in-situ field science research; or 3) teleoperation of robotic assets by humans sufficiently proximal to the exploration targets to drastically reduce latencies and significantly increase bandwidth, thereby achieving effective human telepresence.
This third strategy has been the focus of experts in telerobotics, telepresence, planetary science, and human spaceflight during two workshops held from October 3–7, 2016, and July 7–13, 2017, at the Keck Institute for Space Studies (KISS). Based on findings from these workshops, this document describes the conceptual and practical foundations of low-latency telepresence (LLT), opportunities for using derivative approaches for scientific exploration of planetary surfaces, and circumstances under which employing telepresence would be especially productive for planetary science. An important finding of these workshops is the conclusion that there has been limited study of the advantages of planetary science via LLT. A major recommendation from these workshops is that space agencies such as NASA should substantially increase science return with greater investments in this promising strategy for human conduct at distant exploration sites
Towards Semi-Autonomous Robotic Arm Manipulation Operator Intention Detection from Forces Feedback
In harsh environments such as those found in nuclear facilities, the use of
robotic systems is crucial for performing tasks that would otherwise require
human intervention. This is done to minimize the risk of human exposure to
dangerous levels of radiation, which can have severe consequences for health
and even be fatal. However, the telemanipulation systems employed in these
environments are becoming increasingly intricate, relying heavily on
sophisticated control methods and local master devices. Consequently, the
cognitive burden on operators during labor-intensive tasks is growing. To
tackle this challenge, operator intention detection based on task learning can
greatly enhance the performance of robotic tasks while reducing the reliance on
human effort in teleoperation, particularly in a glovebox environment. By
accurately predicting the operator's intentions, the robot can carry out tasks
more efficiently and effectively, with minimal input from the operator. In this
regard, we propose the utilization of Convolutional Neural Networks, a machine
learning approach, to learn and forecast the operator's intentions using raw
force feedback spatiotemporal data. Through our experimental study on glovebox
tasks for nuclear applications, such as radiation survey and object grasping,
we have achieved promising outcomes. Our approach holds the potential to
enhance the safety and efficiency of robotic systems in harsh environments,
thus diminishing the risk of human exposure to radiation while simultaneously
improving the precision and speed of robotic operations
Supervised Autonomous Locomotion and Manipulation for Disaster Response with a Centaur-like Robot
Mobile manipulation tasks are one of the key challenges in the field of
search and rescue (SAR) robotics requiring robots with flexible locomotion and
manipulation abilities. Since the tasks are mostly unknown in advance, the
robot has to adapt to a wide variety of terrains and workspaces during a
mission. The centaur-like robot Centauro has a hybrid legged-wheeled base and
an anthropomorphic upper body to carry out complex tasks in environments too
dangerous for humans. Due to its high number of degrees of freedom, controlling
the robot with direct teleoperation approaches is challenging and exhausting.
Supervised autonomy approaches are promising to increase quality and speed of
control while keeping the flexibility to solve unknown tasks. We developed a
set of operator assistance functionalities with different levels of autonomy to
control the robot for challenging locomotion and manipulation tasks. The
integrated system was evaluated in disaster response scenarios and showed
promising performance.Comment: In Proceedings of IEEE/RSJ International Conference on Intelligent
Robots and Systems (IROS), Madrid, Spain, October 201
Autonomous Systems, Robotics, and Computing Systems Capability Roadmap: NRC Dialogue
Contents include the following: Introduction. Process, Mission Drivers, Deliverables, and Interfaces. Autonomy. Crew-Centered and Remote Operations. Integrated Systems Health Management. Autonomous Vehicle Control. Autonomous Process Control. Robotics. Robotics for Solar System Exploration. Robotics for Lunar and Planetary Habitation. Robotics for In-Space Operations. Computing Systems. Conclusion
Machine Learning Meets Advanced Robotic Manipulation
Automated industries lead to high quality production, lower manufacturing
cost and better utilization of human resources. Robotic manipulator arms have
major role in the automation process. However, for complex manipulation tasks,
hard coding efficient and safe trajectories is challenging and time consuming.
Machine learning methods have the potential to learn such controllers based on
expert demonstrations. Despite promising advances, better approaches must be
developed to improve safety, reliability, and efficiency of ML methods in both
training and deployment phases. This survey aims to review cutting edge
technologies and recent trends on ML methods applied to real-world manipulation
tasks. After reviewing the related background on ML, the rest of the paper is
devoted to ML applications in different domains such as industry, healthcare,
agriculture, space, military, and search and rescue. The paper is closed with
important research directions for future works
Adaptive and intelligent navigation of autonomous planetary rovers - A survey
The application of robotics and autonomous systems in space has increased dramatically. The ongoing Mars rover mission involving the Curiosity rover, along with the success of its predecessors, is a key milestone that showcases the existing capabilities of robotic technology. Nevertheless, there has still been a heavy reliance on human tele-operators to drive these systems. Reducing the reliance on human experts for navigational tasks on Mars remains a major challenge due to the harsh and complex nature of the Martian terrains. The development of a truly autonomous rover system with the capability to be effectively navigated in such environments requires intelligent and adaptive methods fitting for a system with limited resources. This paper surveys a representative selection of work applicable to autonomous planetary rover navigation, discussing some ongoing challenges and promising future research directions from the perspectives of the authors
Context-aware learning for robot-assisted endovascular catheterization
Endovascular intervention has become a mainstream treatment of cardiovascular diseases. However, multiple challenges remain such as unwanted radiation exposures, limited two-dimensional image guidance, insufficient force perception and haptic cues. Fast evolving robot-assisted platforms improve the stability and accuracy of instrument manipulation. The master-slave system also removes radiation to the operator. However, the integration of robotic systems into the current surgical workflow is still debatable since repetitive, easy tasks have little value to be executed by the robotic teleoperation. Current systems offer very low autonomy, potential autonomous features could bring more benefits such as reduced cognitive workloads and human error, safer and more consistent instrument manipulation, ability to incorporate various medical imaging and sensing modalities. This research proposes frameworks for automated catheterisation with different machine learning-based algorithms, includes Learning-from-Demonstration, Reinforcement Learning, and Imitation Learning. Those frameworks focused on integrating context for tasks in the process of skill learning, hence achieving better adaptation to different situations and safer tool-tissue interactions. Furthermore, the autonomous feature was applied to next-generation, MR-safe robotic catheterisation platform. The results provide important insights into improving catheter navigation in the form of autonomous task planning, self-optimization with clinical relevant factors, and motivate the design of intelligent, intuitive, and collaborative robots under non-ionizing image modalities.Open Acces
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