Dynamic Consensus Research Articles

Distributed Role Assignment in Multi-Robot Formation

In this paper, we study the problem of distributed role assignment for multiple mobile robots. This problem arises when a mobile robot in the team must decide what role to take on in a desired formation configuration. In some applications, in which the center and orientation of a desired formation are not predetermined, the rotation and translation of the formation can be computed by using average consensus protocols. A conflict arises when the same role is assigned to more than one robot. This problem is resolved by using a negotiation strategy, while each assigned robot is traveling to the target position. Furthermore, dynamic consensus is used to measure the degree of task completion in this work. Once the consensus reaches a preset threshold, implying that robots should abandon the current task and start the next one. We verify our proposed framework through simulations on a team of nonholonomic mobile robots performing distributed formation switching.

Consensus based overlapping decentralized estimation with missing observations and communication faults

In this paper a new algorithm for discrete-time overlapping decentralized state estimation of large scale systems is proposed in the form of a multi-agent network based on a combination of local estimators of Kalman filtering type and a dynamic consensus strategy, assuming intermittent observations and communication faults. Under general conditions concerning the agent resources and the network topology, conditions are derived for the convergence to zero of the estimation error mean and for the mean-square estimation error boundedness. A centralized strategy based on minimization of the steady-state mean-square estimation error is proposed for selection of the consensus gains; these gains can also be adjusted by local adaptation schemes. It is also demonstrated that there exists a connection between the network complexity and efficiency of denoising, i.e., of suppression of the measurement noise influence. Several numerical examples serve to illustrate characteristic properties of the proposed algorithm and to demonstrate its applicability to real problems.

Consensus Based Overlapping Decentralized Estimator

In this technical note a new algorithm for state estimation is proposed in the form of a multi-agent network based on a synergy between local Kalman filters and a dynamic consensus strategy between the agents. It is shown that it is possible, under general conditions concerning local resources and the network topology, to achieve asymptotic stability of the whole estimation algorithm by a proper choice of the consensus gains. It is demonstrated that the consensus gains can be obtained by minimizing the total mean-square estimation error. Capabilities of the network to achieve reduction of the measurement noise influence are also discussed.

Stabilization of collective motion on a sphere

We provide a Lyapunov-based design of decentralized control laws that stabilize relative equilibria in a model of self-propelled particles that travel on the surface of a sphere. Such control laws have applications in planetary-scale mobile sensing networks in air, sea, and space. Relative equilibria of the closed-loop model include formations in which all of the particles travel around a common circular trajectory. Particle interaction can be time-invariant or time-varying and directed or undirected. The algorithm for time-invariant and undirected particle interaction uses a gradient-like control induced from the associated Laplacian matrix. The algorithm for time-varying and directed interaction replaces average quantities in the control law with dynamic consensus variables. An augmented Laplacian algorithm is also proposed to stabilize symmetric circular formations.

Towards a molecular dynamics consensus view of B-DNA flexibility.

We present a systematic study of B-DNA flexibility in aqueous solution using long-scale molecular dynamics simulations with the two more recent versions of nucleic acids force fields (CHARMM27 and parmbsc0) using four long duplexes designed to contain several copies of each individual base pair step. Our study highlights some differences between pambsc0 and CHARMM27 families of simulations, but also extensive agreement in the representation of DNA flexibility. We also performed additional simulations with the older AMBER force fields parm94 and parm99, corrected for non-canonical backbone flips. Taken together, the results allow us to draw for the first time a consensus molecular dynamics picture of B-DNA flexibility.

Coordination of Multi-agent Systems with Communication Delays

In this paper, we investigate coordination of a network of second-order dynamic agents under communication delays. Based on the frequency-domain analysis and matrix theory, the necessary and sufficient conditions for the system converging to stationary consensus and dynamic consensus are obtained, respectively. The conditions depend on the communication delay, the eigenvalues of the Laplacian matrix, and the interconnection topology of the network. Moreover, we apply the consensus algorithm to the formation control of the multi-agent system with communication delays. The agents in the system can achieve arbitrary desired formation pattern, and the formation moves in a desired velocity. Simulation results illustrate the correctness of the results.

Consensus Based Overlapping Decentralized Estimation With Missing Observations and Communication Faults

In this paper a new algorithm for discrete-time overlapping decentralized state estimation of large scale systems is proposed in the form of a multi-agent network based on a combination of local Kalman filters and a dynamic consensus strategy, assuming intermittent observations and communication faults. Conditions are derived for the algorithm to provide, under general conditions concerning the agent resources and the network topology, asymptotic stability in the sense of bounded mean-square estimation error. It is also demonstrated how the consensus gains can be chosen by minimizing the total steady-state mean-square estimation error. Numerical examples illustrate some properties of the proposed algorithm.

Innovation and Metastability: a Systems Model

The culture trap is tendency to put cultural markers and habits above demands of reason or compassion. It can reduce receptivity to new ideas and trigger Phoenix Cycles of catastrophe and renaissance. System research is then complicated by historiographic problem of continuity and change, because there are no objective criteria for deciding whether the survived or was destroyed by catastrophe. This paper explores differences between uncertainty emergence and self-organizing emergence using concept of a to clarify relationship between anti-causal events and causal states, i.e., meso-history of conjuncture. Conjunctures are interpreted ex post in context of deep time. The paper distinguishes autopoiesis, i.e., a new instance of a species, from elaboration, i.e., origin of new species. It also describes a spectrum ranging from metastability, in which possibility space is time invariant, to innovation, in which possibility space is a synergetic artefact of changing habits and beliefs. Metastable systems are computably complex; we can make predictions across boundaries of successive conjunctures, subject to statistical uncertainties. Innovative systems are uncomputably complex; although we can make predictions within a conjuncture and anticipate bottlenecks, we cannot predict beyond conjuncture because conceptual taxonomies will not be conserved. The relationship between adaptive potential and constraint remains fundamental, although debates about whether system is resilient are meaningless. System boundaries are artefacts of a dynamic consensus. The analytical sciences use methods designed for metastable systems, whereas discursive sciences presume innovation. These methods predispose researchers to different space-time perspectives that, in turn, make it possible to generalize about their roles in different policy arenas. Tensions between analytical and discursive scientists become more understandable and manageable if these historiographic issues are clarified. The relationship between innovation and metastability is illustrated in an appendix on emergence of social complexity in Europe.

A decision making approach to relevance feedback in information retrieval: A model based on soft consensus dynamics

Information retrieval (IR) can be regarded as a natural instance of multicriteria decision making (MCDM). Queries are formulated as selection criteria aggregated by means of appropriate operators. Retrieval is then performed as a MCDM process by evaluating the degrees of satisfaction of the criteria by each document, and then aggregating them. Another decisional instance in IR concerns the problem of improving retrieval performance by taking into account user indications on documents relevance. Relevance feedback mechanisms exploit user-system interaction in order to improve retrieval results by means of an iterative process of query refinement. In this process the main decisional issue is that of finding new concepts (terms) with which to expand–modify the initial query so that it better reflects the user's information needs. In this paper we introduce a relevance feedback mechanism based on a dynamical consensus model originally proposed in the framework of group decision making. In the relevance feedback context the consensual interaction highlights associations among the most significant terms in the relevant retrieved documents selected by the user. The resulting associative structure can then be used to expand the original query by including new terms which result strongly associated with those in the query. ©1999 John Wiley & Sons, Inc.

A decision making approach to relevance feedback in information retrieval: A model based on soft consensus dynamics

Information retrieval (IR) can be regarded as a natural instance of multicriteria decision making (MCDM). Queries are formulated as selection criteria aggregated by means of appropriate operators. Retrieval is then performed as a MCDM process by evaluating the degrees of satisfaction of the criteria by each document, and then aggregating them. Another decisional instance in IR concerns the problem of improving retrieval performance by taking into account user indications on documents relevance. Relevance feedback mechanisms exploit user-system interaction in order to improve retrieval results by means of an iterative process of query refinement. In this process the main decisional issue is that of finding new concepts (terms) with which to expand–modify the initial query so that it better reflects the user's information needs. In this paper we introduce a relevance feedback mechanism based on a dynamical consensus model originally proposed in the framework of group decision making. In the relevance feedback context the consensual interaction highlights associations among the most significant terms in the relevant retrieved documents selected by the user. The resulting associative structure can then be used to expand the original query by including new terms which result strongly associated with those in the query. ©1999 John Wiley & Sons, Inc.