Flocking control for multi-agent systems with stream-based obstacle avoidance
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Abstract:
The problems of flocking with both connectivity maintenance and obstacle avoidance for the network of dynamic agents are addressed. In the case where the initial network is connected, a decentralized flocking control protocol is proposed to enable the group to asymptotically achieve the desired stable flocking motion using artificial potential functions combined with stream functions, which could not only maintain the network connectivity of the dynamic multi-agent systems for all time but also make all the agents avoid obstacles smoothly without trapping into local minima. Finally, nontrivial simulations and experiments are worked out to verify the effectiveness of the theoretical methods.Keywords:
Flocking (texture)
Obstacle avoidance
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When agents make decisions, they have to deal with norms regulating the system. In this paper we therefore propose a rule-based qualitative decision and game theory combining ideas from multiagent systems and normative systems. Whereas normative systems are typically modelled as a single authority that imposes obligations and permissions on the agents, our theory is based on a multiagent structure of the normative system. We distinguish between agents whose behavior is governed by norms, so-called defender agents who have the duty to monitor violations of these norms and apply sanctions, and autonomous normative systems that issue norms and watch over the behavior of defender agents. We show that autonomous normative systems can delegate monitoring and sanctioning of violations to defender agents, when bearers of obligations model defender agents, which in turn model autonomous normative systems.
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The paper deals with distributed planning in a Multi-Agent System (MAS) constituted by several intelligent agents each one has to interact with the other autonomous agents. The problem faced is how to ensure a distributed planning through the cooperation in our multi-agent system. Finally, we use JADE platform to create agents and ensure the communication between them. A Benchmark Production System is used as a running example to explain our contribution.
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This paper presents an adaptive artificial potential field method for robot's obstacle avoidance path planning. Despite the obstacle avoidance path planning based on the artificial potential field method is very popular, but there is local minima problem with this approach. As a result, this paper proposes an improved obstacle potential field function model considering for the size of the robot and the obstacles and changes the weight of the obstacle potential field function adaptively to make the robot escape from the local minima. Three simulations have been done and the simulation results show: the improved algorithm can make the robot escape from the local minima and accomplish the robot collision avoidance path planning well.
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A formulation of the artificial potential approach to the obstacle avoidance problem for a mobile robot or a manipulator in a known environment is presented. Previous formulations of artificial potentials, for obstacle avoidance, have exhibited local minima in a cluttered environment. To build an artificial potential field, harmonic functions that completely eliminate the local minima even for a cluttered environment were used. The panel method was used to represent arbitrarily shaped obstacles and to derive the potential function over the whole space. Based on this potential function and elegant control strategy for the real-time control of a robot is proposed.< >
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Flocking of multi-agents with obstacle avoidance is a significant problem in the research of flocking. Olfati-Saber proposed a flocking algorithm with obstacle avoidance in [13]. In this paper we extend Olfati-Saber algorithm to 3D flocking with conic obstacle avoidance. The α-agents are guided by β-agents which are determined by the shortest route on the cone. We also present the dissipative analysis of flocking with obstacle avoidance. Finally, simulation results are provided to demonstrate the effectiveness of our proposed methods.
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Current interest in cooperative control of groups of mobile autonomous agents has led to the emergence of a broad range of exciting technical problem calling for the application of new and well-established control theoretic concepts together with other concepts devised originally for problems within entirely different fields. For example, the problem of maintaining a formation of autonomous agents by locally controlling the distances between each agent and its nearby neighbors cannot be formulated, let along fully understood, without a 100+ year old concept from the theory of structures, namely the notion of a rigid graph. To understand how a group of agents moving in the plane at the same speed can eventually all end up moving in the same direction by simply adjusting their individual headings to the averages of their neighbors' headings, requires the application of ideas from the theory of non-homogeneous Markov chains, a branch of probability theory which on the surface has nothing to do with the so-called flocking phenomenon just described. Flocking ing is also a good example of a topic originally motivated by a problem from an entirely different field, in this case statistical physics. Some problems can be addressed using ideas from control which one might at first not expect to be relevant.
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