53 research outputs found
Collisionless Pattern Discovery in Robot Swarms Using Deep Reinforcement Learning
We present a deep reinforcement learning-based framework for automatically
discovering patterns available in any given initial configuration of fat robot
swarms. In particular, we model the problem of collision-less gathering and
mutual visibility in fat robot swarms and discover patterns for solving them
using our framework. We show that by shaping reward signals based on certain
constraints like mutual visibility and safe proximity, the robots can discover
collision-less trajectories leading to well-formed gathering and visibility
patterns
Pattern Formation for Fat Robots with Memory
Given a set of autonomous, anonymous, indistinguishable, silent,
and possibly disoriented mobile unit disk (i.e., fat) robots operating
following Look-Compute-Move cycles in the Euclidean plane, we consider the
Pattern Formation problem: from arbitrary starting positions, the robots must
reposition themselves to form a given target pattern. This problem arises under
obstructed visibility, where a robot cannot see another robot if there is a
third robot on the straight line segment between the two robots. We assume that
a robot's movement cannot be interrupted by an adversary and that robots have a
small -sized memory that they can use to store information, but that
cannot be communicated to the other robots. To solve this problem, we present
an algorithm that works in three steps. First it establishes mutual visibility,
then it elects one robot to be the leader, and finally it forms the required
pattern. The whole algorithm runs in rounds, where
is related to leader election, which takes rounds with
probability at least . The algorithms are collision-free and do not
require the knowledge of the number of robots.Comment: arXiv admin note: text overlap with arXiv:2306.1444
Distributed Systems and Mobile Computing
The book is about Distributed Systems and Mobile Computing. This is a branch of Computer Science devoted to the study of systems whose components are in different physical locations and have limited communication capabilities. Such components may be static, often organized in a network, or may be able to move in a discrete or continuous environment. The theoretical study of such systems has applications ranging from swarms of mobile robots (e.g., drones) to sensor networks, autonomous intelligent vehicles, the Internet of Things, and crawlers on the Web. The book includes five articles. Two of them are about networks: the first one studies the formation of networks by agents that interact randomly and have the ability to form connections; the second one is a study of clustering models and algorithms. The three remaining articles are concerned with autonomous mobile robots operating in continuous space. One article studies the classical gathering problem, where all robots have to reach a common location, and proposes a fast algorithm for robots that are endowed with a compass but have limited visibility. The last two articles deal with the evacuations problem, where two robots have to locate an exit point and evacuate a region in the shortest possible time
Practical Considerations and Applications for Autonomous Robot Swarms
In recent years, the study of autonomous entities such as unmanned vehicles has begun to revolutionize both military and civilian devices. One important research focus of autonomous entities has been coordination problems for autonomous robot swarms. Traditionally, robot models are used for algorithms that account for the minimum specifications needed to operate the swarm. However, these theoretical models also gloss over important practical details. Some of these details, such as time, have been considered before (as epochs of execution). In this dissertation, we examine these details in the context of several problems and introduce new performance measures to capture practical details. Specifically, we introduce three new metrics: (1) the distance complexity (reflecting power usage and wear-and-tear of robots), (2) the spatial complexity (reflecting the space needed for the algorithm to work), and (3) local computational complexity (reflecting the computational requirements for each robot in the swarm).
We apply these metrics in the study of some well-known and important problems, such as Complete Visibility and Arbitrary Pattern Formation. We also introduce and study a new problem, Doorway Egress, that captures the essence of a swarm’s navigation through restricted spaces. First, we examine the distance and spatial complexity used across a class of Complete Visibility algorithms. Second, we provide algorithms for Complete Visibility on an integer plane, including some that are asymptotically optimal in terms of time, distance complexity, and spatial complexity. Third, we introduce the problem of Doorway Egress and provide algorithms for a variety of robot swarm models with various optimalities. Finally, we provide an optimal algorithm for Arbitrary Pattern Formation on the grid
Visibility and Pattern Formation Problems in Robotics
We study four problems related to robotics and computational geometry. We consider the Mutual
Visibility problem for fat robots with lights: given a set of n ≥ 1 unit disk robots in the Euclidean plane,
the robots must reposition themselves to reach a configuration where they all see each other. We
present an algorithm that requires only 2 colors and O(n) rounds. The number of colors is optimal
since at least two colors are required for point robots [28]. We consider the Pattern Formation
problem for fat robots: given a set of n ≥ 1 unit disk robots in the Euclidean plane, the robots must
reposition themselves to form a given target pattern. We consider this problem for robots with lights
and reduce the number of colors needed. In particular, we present an algorithm requiring 7 colors
when scaling the target pattern is allowed and an 8-color algorithm if scaling is not allowed. Our
algorithms run in O(n) rounds plus the time needed for the robots to elect a leader. We also consider
robots with memory and present an algorithm that runs in O(n) + O(q log n) rounds, where q > 0 is
related to Leader Election. We assume that the robots have a small O(1)-sized memory that they can
use to store information, but that cannot be communicated to the other robots. We consider the
shortest paths of mutually visible robots: given a set of n point robots inside a simple polygon P, the
task is to move the robots from their starting positions to their target positions along their shortest
paths, while the mutual visibility of these robots is preserved. We present an O(mn) time algorithm,
where m is the complexity of the polygon, when all the starting positions lie on a line segment S, all
the target positions lie on a line segment T, and S and T do not intersect. We also argue that there is
no polynomial-time algorithm, whose running time depends only on n and m, that uses a single
strategy for the case where S and T intersect
Multi-Robot Learning with Bat Algorithm With Mutation (Bam)
The mobile robotics is an active area of research. Several methods are under study to increase and optimize the working capabilities of multi robotic systems. These multi robotic systems or robot swarms have vast applications in industry as a human assistant to carry goods and can-do variety of jobs. Multiple techniques like swarm optimization, cuckoo algorithm and other such algorithms are under study for multi robotic systems. In this research, a biological bat inspired algorithm is implemented to achieve the target. BAT algorithm is implemented to achieve the target. BAT algorithm uses echolocation technique like bats to generate bat population and random data is generated, the robot then traverses and the distance is calculated which is compared to the distance from the obstacle. The loop continues and robot keeps moving. For more than one robot, robots have statistic as well as dynamic obstacles. So, the traversal speed and efficiency of bad algorithm reduces slightly
1992 NASA/ASEE Summer Faculty Fellowship Program
For the 28th consecutive year, a NASA/ASEE Summer Faculty Fellowship Program was conducted at the Marshall Space Flight Center (MSFC). The program was conducted by the University of Alabama and MSFC during the period June 1, 1992 through August 7, 1992. Operated under the auspices of the American Society for Engineering Education, the MSFC program, was well as those at other centers, was sponsored by the Office of Educational Affairs, NASA Headquarters, Washington, DC. The basic objectives of the programs, which are the 29th year of operation nationally, are (1) to further the professional knowledge of qualified engineering and science faculty members; (2) to stimulate and exchange ideas between participants and NASA; (3) to enrich and refresh the research and teaching activities of the participants' institutions; and (4) to contribute to the research objectives of the NASA centers
Research reports: 1990 NASA/ASEE Summer Faculty Fellowship Program
Reports on the research projects performed under the NASA/ASEE Summer Faculty Fellowship Program are presented. The program was conducted by The University of Alabama and MSFC during the period from June 4, 1990 through August 10, 1990. Some of the topics covered include: (1) Space Shuttles; (2) Space Station Freedom; (3) information systems; (4) materials and processes; (4) Space Shuttle main engine; (5) aerospace sciences; (6) mathematical models; (7) mission operations; (8) systems analysis and integration; (9) systems control; (10) structures and dynamics; (11) aerospace safety; and (12) remote sensin
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