467 research outputs found
BittyBuzz: A Swarm Robotics Runtime for Tiny Systems
Swarm robotics is an emerging field of research which is increasingly
attracting attention thanks to the advances in robotics and its potential
applications. However, despite the enthusiasm surrounding this area of
research, software development for swarm robotics is still a tedious task. That
fact is partly due to the lack of dedicated solutions, in particular for
low-cost systems to be produced in large numbers and that can have important
resource constraints. To address this issue, we introduce BittyBuzz, a novel
runtime platform: it allows Buzz, a domain-specific language, to run on
microcontrollers while maintaining dynamic memory management. BittyBuzz is
designed to fit a flash memory as small as 32 kB (with usable space for
scripts) and work with as little as 2 kB of RAM. In this work, we introduce the
BittyBuzz implementation, its differences from the original Buzz virtual
machine, and its advantages for swarm robotics systems. We show that BittyBuzz
is successfully integrated with three robotic platforms with minimal memory
footprint and conduct experiments to show computation performance of BittyBuzz.
Results show that BittyBuzz can be effectively used to implement common swarm
behaviors on microcontroller-based systems.Comment: 6 page
Swarm-inspired solution strategy for the search problem of unmanned aerial vehicles
Learning from the emergent behaviour of social insects, this research studies the influences of environment to collective problem-solving of insect behaviour and distributed intelligent systems. Literature research has been conducted to understand the emergent paradigms of social insects, and to investigate current research and development of distributed intelligent systems. On the basis of the literature investigation, the environment is considered to have significant impact on the effectiveness and efficiency of collective problem-solving. A framework of collective problem-solving is developed in an interdisciplinary context to describe the influences of the environment to insect behaviour and problem-solving of distributed intelligent systems. The environment roles and responsibilities are transformed into and deployed as a problem-solving mechanism for distributed intelligent systems.
A swarm-inspired search strategy is proposed as a behaviour-based cooperative search solution. It is applied to the cooperative search problem of Unmanned Aerial Vehicles (UAVs) with a series of experiments implemented for evaluation. The search environment represents the specification and requirements of the search problem; defines tasks to be achieved and maintained; and it is where targets are locally observable and accessible to UAVs. Therefore, the information provided through the search environment is used to define rules of behaviour for UAVs. The initial detection of target signal refers to modified configurations of the search environment, which mediates local communications among UAVs and is used as a means of coordination. The experimental results indicate that, the swarm-inspired search strategy is a valuable alternative solution to current approaches of cooperative search problem of UAVs. In the proposed search solution, the diagonal formation of two UAVs is able to produce superior performance than the triangular formation of three UAVs for the average detection time and the number of targets located within the maximum time length
Aerospace medicine and biology: A continuing bibliography with indexes (supplement 323)
This bibliography lists 125 reports, articles and other documents introduced into the NASA Scientific and Technical Information System during April, 1989. Subject coverage includes; aerospace medicine and psychology, life support systems and controlled environments, safety equipment exobiology and extraterrestrial life, and flight crew behavior and performance
Multi-Robot Systems: Challenges, Trends and Applications
This book is a printed edition of the Special Issue entitled “Multi-Robot Systems: Challenges, Trends, and Applications” that was published in Applied Sciences. This Special Issue collected seventeen high-quality papers that discuss the main challenges of multi-robot systems, present the trends to address these issues, and report various relevant applications. Some of the topics addressed by these papers are robot swarms, mission planning, robot teaming, machine learning, immersive technologies, search and rescue, and social robotics
Efficient Mission Planning for Robot Networks in Communication Constrained Environments
Many robotic systems are remotely operated nowadays that require uninterrupted connection and safe mission planning. Such systems are commonly found in military drones, search and rescue operations, mining robotics, agriculture, and environmental monitoring. Different robotic systems may employ disparate communication modalities such as radio network, visible light communication, satellite, infrared, Wi-Fi. However, in an autonomous mission where the robots are expected to be interconnected, communication constrained environment frequently arises due to the out of range problem or unavailability of the signal. Furthermore, several automated projects (building construction, assembly line) do not guarantee uninterrupted communication, and a safe project plan is required that optimizes collision risks, cost, and duration. In this thesis, we propose four pronged approaches to alleviate some of these issues: 1) Communication aware world mapping; 2) Communication preserving using the Line-of-Sight (LoS); 3) Communication aware safe planning; and 4) Multi-Objective motion planning for navigation.
First, we focus on developing a communication aware world map that integrates traditional world models with the planning of multi-robot placement. Our proposed communication map selects the optimal placement of a chain of intermediate relay vehicles in order to maximize communication quality to a remote unit. We also vi propose an algorithm to build a min-Arborescence tree when there are multiple remote units to be served. Second, in communication denied environments, we use Line-of-Sight (LoS) to establish communication between mobile robots, control their movements and relay information to other autonomous units. We formulate and study the complexity of a multi-robot relay network positioning problem and propose approximation algorithms that restore visibility based connectivity through the relocation of one or more robots. Third, we develop a framework to quantify the safety score of a fully automated robotic mission where the coexistence of human and robot may pose a collision risk. A number of alternate mission plans are analyzed using motion planning algorithms to select the safest one. Finally, an efficient multi-objective optimization based path planning for the robots is developed to deal with several Pareto optimal cost attributes
A General Framework for Multi-Agent Task Selection
Ph.DDOCTOR OF PHILOSOPH
Coordination of Cooperative Multi-Robot Teams
This thesis is about cooperation of multiple robots that have a common
task they should fulfill, i.e., how multi-robot systems behave in cooperative
scenarios. Cooperation is a very important aspect in robotics, because
multiple robots can solve a task more quickly or efficiently in many situations.
Specific points of interest are, how the effectiveness of the group of
robots completing a task can be improved and how the amount of communication
and computational requirements can be reduced. The importance
of this topic lies in applications like search and rescue scenarios, where
time can be a critical factor and a certain robustness and reliability are
required. Further the communication can be limited by various factors
and operating (multiple) robots can be a highly complicated task.
A typical search and rescue mission as considered in this thesis begins
with the deployment of the robot team in an unknown or partly known
environment. The team can be heterogeneous in the sense that it consists
of pairs of air and ground robots that assist each other. The air vehicle –
abbreviated as UAV – stays within vision range of the ground vehicle or
UGV. Therefrom, it provides sensing information with a camera or similar
sensor that might not be available to the UGV due to distance, perspective
or occlusion. A new approach to fully use the available movement range
is presented and analyzed theoretically and in simulations. The UAV
moves according to a dynamic coverage algorithm which is combined with
a tracking controller to guarantee the visibility limitation is kept.
Since the environment is at least partly unknown, an exploration method
is necessary to gather information about the situation and possible targets
or areas of interest. Exploring the unknown regions in a short amount
of time is solved by approaching points on the frontier between known
and unknown territory. To this end, a basic approach for single robot
exploration that uses the traveling salesman problem is extended to multirobot
exploration. The coordination, which is a central aspect of the
cooperative exploration process, is realized with a pairwise optimization
procedure. This new algorithm uses minimum spanning trees for cost
estimation and is inspired by one of the many multi-robot coordination
methods from the related literature. Again, theoretical and simulated as
well as statistical analysis are used as methods to evaluate the approach.
After the exploration is complete, a map of the environment with possible
regions of higher importance is known by the robot team. To stay
useful and ready for any further events, the robots now switch to a monitoring
state where they spread out to cover the area in an optimal manner.
The optimality is measured with a criterion that can be derived into a distributed
control law. This leads to splitting of the robots into areas of
Voronoi cells where each robot has a maximum distance to other robots
and can sense any events within its assigned cell. A new variant of these
Voronoi cells is introduced. They are limited by visibility and depend on
a delta-contraction of the environment, which leads to automatic collision
avoidance. The combination of these two aspects leads to a coverage
control algorithm that works in nonconvex environments and has advantageous
properties compared to related work
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