Swarm robotics is a growing field that explores the implementation potential for multi-agent robotic systems completing tasks in a decentralized manor. Reinforcement Learning is a sub-field of Machine Learning that us...
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ISBN:
(数字)9781728168616
ISBN:
(纸本)9781728168623
Swarm robotics is a growing field that explores the implementation potential for multi-agent robotic systems completing tasks in a decentralized manor. Reinforcement Learning is a sub-field of Machine Learning that uses a feedback reward system to optimize the control laws on a given system. The Reinforcement Learning Adversarial Swarm Dynamics project will implement reinforcement learning into a simple game executed by adversarial homogeneous swarms for exploration into the feasibility and optimality of reinforcement learning in swarm robotic systems.
In this paper, we study the problem of assessing the effectiveness of a proactive defense-by-detection policy with a network-based moving target defense. We model the network system using a probabilistic attack graph-...
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We present an approach to the distributed storage of data across a swarm of mobile robots that forms a shared global memory. We assume that external storage infrastructure is absent, and that each robot is capable of ...
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ISBN:
(数字)9781728173955
ISBN:
(纸本)9781728173962
We present an approach to the distributed storage of data across a swarm of mobile robots that forms a shared global memory. We assume that external storage infrastructure is absent, and that each robot is capable of devoting a quota of memory and bandwidth to distributed storage. Our approach is motivated by the insight that in many applications data is collected at the periphery of a swarm topology, but the periphery also happens to be the most dangerous location for storing data, especially in exploration missions. Our approach is designed to promote data storage in the locations in the swarm that best suit a specific feature of interest in the data, while accounting for the constantly changing topology due to individual motion. We analyze two possible features of interest: the data type and the data item position in the environment. We assess the performance of our approach in a large set of simulated experiments. The evaluation shows that our approach is capable of storing quantities of data that exceed the memory of individual robots, while maintaining near-perfect data retention in high-load conditions.
Due to the potentially large number of units involved, the interaction with a multi-robot system is likely to exceed the limits of the span of apprehension of any individual human operator. In previous work, we studie...
ISBN:
(数字)9781728160757
ISBN:
(纸本)9781728160764
Due to the potentially large number of units involved, the interaction with a multi-robot system is likely to exceed the limits of the span of apprehension of any individual human operator. In previous work, we studied how this issue can be tackled by interacting with the robots in two modalities - environment-oriented and robot-oriented. In this paper, we study how this concept can be applied to the case in which multiple human operators perform supervisory control on a multirobot system. While the presence of extra operators suggests that more complex tasks could be accomplished, little research exists on how this could be achieved efficiently. In particular, one challenge arises - the out-of-the-loop performance problem caused by a lack of engagement in the task, awareness of its state, and trust in the system and in the other operators. Through a user study involving 28 human operators and 8 real robots, we study how the concept of mixed granularity in multi-human multi-robot interaction affects user engagement, awareness, and trust while balancing the workload between multiple operators.
This paper presents the tribological properties and bioactivity of nanostructured hydroxyapatite (HA) from biowaste sources and coated onto Ti-6Al-4V substrates using a novel pack cementation method. The process intro...
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We consider the probabilistic planning problem where the agent (called Player 1, or P1) can jointly plan the control actions and sensor queries in a sensor network and an attacker (called player 2, or P2) can carry ou...
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Over the past decade, Robot-Assisted Surgeries (RAS), have become more prevalent in facilitating successful operations. Of the various types of RAS, the domain of collaborative surgery has gained traction in medical r...
ISBN:
(数字)9781728160757
ISBN:
(纸本)9781728160764
Over the past decade, Robot-Assisted Surgeries (RAS), have become more prevalent in facilitating successful operations. Of the various types of RAS, the domain of collaborative surgery has gained traction in medical research. Prominent examples include providing haptic feedback to sense tissue consistency, and automating sub-tasks during surgery such as cutting or needle hand-off - pulling and reorienting the needle after insertion during suturing. By fragmenting suturing into automated and manual tasks the surgeon could essentially control the process with one hand and also circumvent workspace restrictions imposed by the control interface present at the surgeon's side during the operation. This paper presents an exploration of a discrete reinforcement learning-based approach to automate the needle hand-off task. Users were asked to perform a simple running suture using the da Vinci Research Kit. The user trajectory was learnt by generating a sparse reward function and deriving an optimal policy using Q-learning. Trajectories obtained from three learnt policies were compared to the user defined trajectory. The results showed a root-mean-square error of [0.0044mm, 0.0027mm, 0.0020mm] in ℝ 3 . Additional trajectories from varying initial positions were produced from a single policy to simulate repeated passes of the hand-off task.
In this paper, we propose an approach to the distributed storage and fusion of data for collective perception in resource-limited robot swarms. We demonstrate our approach in a distributed semantic classification scen...
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It is often desirable to capture and map semantic information of an environment during simultaneous localization and mapping (SLAM). Such semantic information can enable a robot to better distinguish places with simil...
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Attention control is a key cognitive ability for humans to select information relevant to the current task. This paper develops a computational model of attention and an algorithm for attention-based probabilistic pla...
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