1,418 research outputs found
Distributed control of multi-robot systems using bifurcating potential fields
The distributed control of multi-robot systems has been shown to have advantages over conventional single robot systems. These include scalability, flexibility and robustness to failures. This paper considers pattern formation and reconfigurability in a multi-robot system using bifurcating potential fields. It is shown how various patterns can be achieved through a simple free parameter change. In addition the stability of the system of robots is proven to ensure that desired behaviours always occur
Position and Orientation Based Formation Control of Multiple Rigid Bodies with Collision Avoidance and Connectivity Maintenance
This paper addresses the problem of position- and orientation-based formation
control of a class of second-order nonlinear multi-agent systems in a D
workspace with obstacles. More specifically, we design a decentralized control
protocol such that each agent achieves a predefined geometric formation with
its initial neighbors, while using local information based on a limited sensing
radius. The latter implies that the proposed scheme guarantees that the
initially connected agents remain always connected. In addition, by introducing
certain distance constraints, we guarantee inter-agent collision avoidance as
well as collision avoidance with the obstacles and the boundary of the
workspace. The proposed controllers employ a novel class of potential functions
and do not require a priori knowledge of the dynamical model, except for
gravity-related terms. Finally, simulation results verify the validity of the
proposed framework
Probabilistic and Distributed Control of a Large-Scale Swarm of Autonomous Agents
We present a novel method for guiding a large-scale swarm of autonomous
agents into a desired formation shape in a distributed and scalable manner. Our
Probabilistic Swarm Guidance using Inhomogeneous Markov Chains (PSG-IMC)
algorithm adopts an Eulerian framework, where the physical space is partitioned
into bins and the swarm's density distribution over each bin is controlled.
Each agent determines its bin transition probabilities using a
time-inhomogeneous Markov chain. These time-varying Markov matrices are
constructed by each agent in real-time using the feedback from the current
swarm distribution, which is estimated in a distributed manner. The PSG-IMC
algorithm minimizes the expected cost of the transitions per time instant,
required to achieve and maintain the desired formation shape, even when agents
are added to or removed from the swarm. The algorithm scales well with a large
number of agents and complex formation shapes, and can also be adapted for area
exploration applications. We demonstrate the effectiveness of this proposed
swarm guidance algorithm by using results of numerical simulations and hardware
experiments with multiple quadrotors.Comment: Submitted to IEEE Transactions on Robotic
Planning And Control Of Swarm Motion As Continua
In this thesis, new algorithms for formation control of multi agent systems (MAS) based on continuum mechanics principles will be investigated. For this purpose agents of the MAS are treated as particles in a continuum, evolving in an n-D space, whose desired configuration is required to satisfy an admissible deformation function. Considered is a specific class of mappings that is called homogenous where the Jacobian of the mapping is only a function of time and is not spatially varying. The primary objectives of this thesis are to develop the necessary theory and its validation via simulation on a mobile-agent based swarm test bed that includes two primary tasks: 1) homogenous transformation of MAS and 2) deployment of a random distribution of agents on to a desired configuration. Developed will be a framework based on homogenous transformations for the evolution of a MAS in an n-D space (n=1, 2, and 3), under two scenarios: 1) no inter-agent communication (predefined motion plan); and 2) local inter-agent communication. Additionally, homogenous transformations based on communication protocols will be used to deploy an arbitrary distribution of a MAS on to a desired curve. Homogenous transformation with no communication: A homogenous transformation of a MAS, evolving in an space, under zero inter agent communication is first considered. Here the homogenous mapping, is characterized by an n x n Jacobian matrix ( ) and an n x 1 rigid body displacement vector ( ), that are based on positions of n+1 agents of the MAS, called leader agents. The designed Jacobian ( ) and rigid body displacement vector ( ) are passed onto rest of the agents of the MAS, called followers, who will then use that information to update their positions under a pre- iv defined motion plan. Consequently, the motion of MAS will evolve as a homogenous transformation of the initial configuration without explicit communication among agents. Homogenous Transformation under Local Communication: We develop a framework for homogenous transformation of MAS, evolving in , under a local inter agent communication topology. Here we assume that some agents are the leaders, that are transformed homogenously in an n-D space. In addition, every follower agent of the MAS communicates with some local agents to update its position, in order to grasp the homogenous mapping that is prescribed by the leader agents. We show that some distance ratios that are assigned based on initial formation, if preserved, lead to asymptotic convergence of the initial formation to a final formation under a homogenous mapping. Deployment of a Random Distribution on a Desired Manifold: Deployment of agents of a MAS, moving in a plane, on to a desired curve, is a task that is considered as an application of the proposed approach. In particular, a 2-D MAS evolution problem is considered as two 1-D MAS evolution problems, where x or y coordinates of the position of all agents are modeled as points confined to move on a straight line. Then, for every coordinate of MAS evolution, bulk motion is controlled by two agents considered leaders that move independently, with rest of the follower agents motions evolving through each follower agent communicating with two adjacent agents
A Framework for Scalable Cooperative Navigation of Autonomous Vehicles
We describe a general framework for controlling and coordinating a group of non-holonomic mobile robots equipped with range sensors, with applications ranging from scouting and reconnaissance, to search and rescue and manipulation tasks. We first describe a set of control laws that allows each robot to control its position and orientation with respect to neighboring robots or obstacles in the environment. We then develop a coordination protocol that allows the robots to automatically switch between the control laws to follow a specified trajectory. Finally, we describe two simple trajectory generators that are derived from potential field theory. The first allows each robot to plan its reference trajectory based on the information available to it. The second scheme requires sharing of information and results in a trajectory for the designated leader. Numerical simulations illustrate the application of these ideas and demonstrate the scalability of the proposed framework for a large group of robots
Time-Energy Optimal Cluster Space Motion Planning for Mobile Robot Formations
The motions of a formation of mobile robots along predetermined paths are optimized according to a tunable time-energy cost function using the cluster space approach to multiagent system specification and control. Upon path-parameterizing cluster state variables describing the geometry and pose of a multirobot group, an optimal control problem is formulated that incorporates formation dynamics and state constraints. The optimal trajectory is derived numerically via a gradient search, iterating over the initial value of one costate. A multirobot formation control simulation is then used to demonstrate the effectiveness of the technique. Results indicate that a substantial tradeoff is made between energy expenditure and motion time when considered as minimization criteria in varying proportions, allowing the operator to tailor mission trajectories according to desired levels of each
A Survey on Aerial Swarm Robotics
The use of aerial swarms to solve real-world problems has been increasing steadily, accompanied by falling prices and improving performance of communication, sensing, and processing hardware. The commoditization of hardware has reduced unit costs, thereby lowering the barriers to entry to the field of aerial swarm robotics. A key enabling technology for swarms is the family of algorithms that allow the individual members of the swarm to communicate and allocate tasks amongst themselves, plan their trajectories, and coordinate their flight in such a way that the overall objectives of the swarm are achieved efficiently. These algorithms, often organized in a hierarchical fashion, endow the swarm with autonomy at every level, and the role of a human operator can be reduced, in principle, to interactions at a higher level without direct intervention. This technology depends on the clever and innovative application of theoretical tools from control and estimation. This paper reviews the state of the art of these theoretical tools, specifically focusing on how they have been developed for, and applied to, aerial swarms. Aerial swarms differ from swarms of ground-based vehicles in two respects: they operate in a three-dimensional space and the dynamics of individual vehicles adds an extra layer of complexity. We review dynamic modeling and conditions for stability and controllability that are essential in order to achieve cooperative flight and distributed sensing. The main sections of this paper focus on major results covering trajectory generation, task allocation, adversarial control, distributed sensing, monitoring, and mapping. Wherever possible, we indicate how the physics and subsystem technologies of aerial robots are brought to bear on these individual areas
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