129 research outputs found
Virtual spring damper method for nonholonomic robotic swarm self-organization and leader following
In this paper, we demonstrate a method for self-organization and leader following of nonholonomic robotic swarm based on spring damper mesh. By self-organization of swarm robots we mean the emergence of order in a swarm as the result of interactions among the single robots. In other words the self-organization of swarm robots mimics some natural behavior of social animals like ants among others. The dynamics of two-wheel robot is derived, and a relation between virtual forces and robot control inputs is defined in order to establish stable swarm formation. Two cases of swarm control are analyzed. In the first case the swarm cohesion is achieved by virtual spring damper mesh connecting nearest neighboring robots without designated leader. In the second case we introduce a swarm leader interacting with nearest and second neighbors allowing the swarm to follow the leader. The paper ends with numeric simulation for performance evaluation of the proposed control method
Adaptive and learning-based formation control of swarm robots
Autonomous aerial and wheeled mobile robots play a major role in tasks such as search and rescue, transportation, monitoring, and inspection. However, these operations are faced with a few open challenges including robust autonomy, and adaptive coordination based on the environment and operating conditions, particularly in swarm robots with limited communication and perception capabilities. Furthermore, the computational complexity increases exponentially with the number of robots in the swarm. This thesis examines two different aspects of the formation control problem. On the one hand, we investigate how formation could be performed by swarm robots with limited communication and perception (e.g., Crazyflie nano quadrotor). On the other hand, we explore human-swarm interaction (HSI) and different shared-control mechanisms between human and swarm robots (e.g., BristleBot) for artistic creation. In particular, we combine bio-inspired (i.e., flocking, foraging) techniques with learning-based control strategies (using artificial neural networks) for adaptive control of multi- robots. We first review how learning-based control and networked dynamical systems can be used to assign distributed and decentralized policies to individual robots such that the desired formation emerges from their collective behavior. We proceed by presenting a novel flocking control for UAV swarm using deep reinforcement learning. We formulate the flocking formation problem as a partially observable Markov decision process (POMDP), and consider a leader-follower configuration, where consensus among all UAVs is used to train a shared control policy, and each UAV performs actions based on the local information it collects. In addition, to avoid collision among UAVs and guarantee flocking and navigation, a reward function is added with the global flocking maintenance, mutual reward, and a collision penalty. We adapt deep deterministic policy gradient (DDPG) with centralized training and decentralized execution to obtain the flocking control policy using actor-critic networks and a global state space matrix. In the context of swarm robotics in arts, we investigate how the formation paradigm can serve as an interaction modality for artists to aesthetically utilize swarms. In particular, we explore particle swarm optimization (PSO) and random walk to control the communication between a team of robots with swarming behavior for musical creation
A fuzzified systematic adjustment of the robotic Darwinian PSO
The Darwinian Particle Swarm Optimization (DPSO) is an evolutionary algorithm that extends the Particle
Swarm Optimization using natural selection to enhance the ability to escape from sub-optimal solutions.
An extension of the DPSO to multi-robot applications has been recently proposed and denoted as Robotic
Darwinian PSO (RDPSO), benefiting from the dynamical partitioning of the whole population of robots,
hence decreasing the amount of required information exchange among robots. This paper further extends
the previously proposed algorithm adapting the behavior of robots based on a set of context-based
evaluation metrics. Those metrics are then used as inputs of a fuzzy system so as to systematically
adjust the RDPSO parameters (i.e., outputs of the fuzzy system), thus improving its convergence rate,
susceptibility to obstacles and communication constraints. The adapted RDPSO is evaluated in groups
of physical robots, being further explored using larger populations of simulated mobile robots within a
larger scenario
A Bioinspired Neural Network-Based Approach for Cooperative Coverage Planning of UAVs
This paper describes a bioinspired neural-network-based approach to solve a coverage
planning problem for a fleet of Unmanned Aerial Vehicles exploring critical areas. The main goal is
to fully cover the map, maintaining a uniform distribution of the fleet on the map, and avoiding collisions
between vehicles and other obstacles. This specific task is suitable for surveillance applications,
where the uniform distribution of the fleet in the map permits them to reach any position on the
map as fast as possible in emergency scenarios. To solve this problem, a bioinspired neural network
structure is adopted. Specifically, the neural network consists of a grid of neurons, where each neuron
has a local cost and has a local connection only with neighbor neurons. The cost of each neuron
influences the cost of its neighbors, generating an attractive contribution to unvisited neurons. We
introduce several controls and precautions to minimize the risk of collisions and optimize coverage
planning. Then, preliminary simulations are performed in different scenarios by testing the algorithm
in four maps and with fleets consisting of 3 to 10 vehicles. Results confirm the ability of the proposed
approach to manage and coordinate the fleet providing the full coverage of the map in every tested
scenario, avoiding collisions between vehicles, and uniformly distributing the fleet on the map
Intelligent Robotics Navigation System: Problems, Methods, and Algorithm
This paper set out to supplement new studies with a brief and comprehensible review of the advanced development in the area of the navigation system, starting from a single robot, multi-robot, and swarm robots from a particular perspective by taking insights from these biological systems. The inspiration is taken from nature by observing the human and the social animal that is believed to be very beneficial for this purpose. The intelligent navigation system is developed based on an individual characteristic or a social animal biological structure. The discussion of this paper will focus on how simple agent’s structure utilizes flexible and potential outcomes in order to navigate in a productive and unorganized surrounding. The combination of the navigation system and biologically inspired approach has attracted considerable attention, which makes it an important research area in the intelligent robotic system. Overall, this paper explores the implementation, which is resulted from the simulation performed by the embodiment of robots operating in real environments
Using haptic feedback in human swarm interaction
A swarm of robots is a large group of individual agents that autonomously coordinate via local control laws. Their emergent behavior allows simple robots to accomplish complex tasks. Since missions may have complex objectives that change dynamically due to environmental and mission changes, human control and influence over the swarm is needed. The field of Human Swarm Interaction (HSI) is young, with few user studies, and even fewer papers focusing on giving non-visual feedback to the operator. The authors will herein present a background of haptics in robotics and swarms and two studies that explore various conditions under which haptic feedback may be useful in HSI. The overall goal of the studies is to explore the effectiveness of haptic feedback in the presence of other visual stimuli about the swarm system. The findings show that giving feedback about nearby obstacles using a haptic device can improve performance, and that a combination of feedback from obstacle forces via the visual and haptic channels provide the best performance
Scale-Invariant Specifications for Human-Swarm Systems
We present a method for controlling a swarm using its spectral decomposition
-- that is, by describing the set of trajectories of a swarm in terms of a
spatial distribution throughout the operational domain -- guaranteeing scale
invariance with respect to the number of agents both for computation and for
the operator tasked with controlling the swarm. We use ergodic control,
decentralized across the network, for implementation. In the DARPA OFFSET
program field setting, we test this interface design for the operator using the
STOMP interface -- the same interface used by Raytheon BBN throughout the
duration of the OFFSET program. In these tests, we demonstrate that our
approach is scale-invariant -- the user specification does not depend on the
number of agents; it is persistent -- the specification remains active until
the user specifies a new command; and it is real-time -- the user can interact
with and interrupt the swarm at any time. Moreover, we show that the
spectral/ergodic specification of swarm behavior degrades gracefully as the
number of agents goes down, enabling the operator to maintain the same approach
as agents become disabled or are added to the network. We demonstrate the
scale-invariance and dynamic response of our system in a field relevant
simulator on a variety of tactical scenarios with up to 50 agents. We also
demonstrate the dynamic response of our system in the field with a smaller team
of agents. Lastly, we make the code for our system available.Comment: Journal of Field Robotics, Accepted for Publication. 25 page
Autonomous construction agents: An investigative framework for large sensor network self-management
Recent technological advances have made it cost effective to utilize massive, heterogeneous sensor networks. To gain appreciable value from these informational systems, there must be a control scheme that coordinates information flow to produce meaningful results. This paper will focus on tools developed to manage the coordination of autonomous construction agents using stigmergy, in which a set of basic low-level rules are implemented through various environmental cues. Using VE-Suite, an open-source virtual engineering software package, an interactive environment is created to explore various informational configurations for the construction problem. A simple test case is developed within the framework, and construction times are analyzed for possible functional relationships pertaining to performance of a particular set of parameters and a given control process. Initial experiments for the test case show sensor saturation occurs relatively quickly with 5-7 sensors, and construction time is generally independent of sensor range except for small numbers of sensors. Further experiments using this framework are needed to define other aspects of sensor performance. These trends can then be used to help decide what kinds of sensing capabilities are required to simultaneously achieve the most cost-effective solution and provide the required value of information when applied to the development of real world sensor applications
Intelligent Escape of Robotic Systems: A Survey of Methodologies, Applications, and Challenges
Intelligent escape is an interdisciplinary field that employs artificial
intelligence (AI) techniques to enable robots with the capacity to
intelligently react to potential dangers in dynamic, intricate, and
unpredictable scenarios. As the emphasis on safety becomes increasingly
paramount and advancements in robotic technologies continue to advance, a wide
range of intelligent escape methodologies has been developed in recent years.
This paper presents a comprehensive survey of state-of-the-art research work on
intelligent escape of robotic systems. Four main methods of intelligent escape
are reviewed, including planning-based methodologies, partitioning-based
methodologies, learning-based methodologies, and bio-inspired methodologies.
The strengths and limitations of existing methods are summarized. In addition,
potential applications of intelligent escape are discussed in various domains,
such as search and rescue, evacuation, military security, and healthcare. In an
effort to develop new approaches to intelligent escape, this survey identifies
current research challenges and provides insights into future research trends
in intelligent escape.Comment: This paper is accepted by Journal of Intelligent and Robotic System
Computational intelligence approaches to robotics, automation, and control [Volume guest editors]
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