Non-linear protocols for optimal distributed consensus in networks of dynamic agents.

We consider stationary consensus protocols for networks of dynamic agents with fixed topologies. At each time instant, each agent knows only its and its neighbors’ state, but must reach consensus on a group decision value that is function of all the agents’ initial state. We show that the agents can reach consensus if the value of such a function is time-invariant when computed over the agents’ state trajectories. We use this basic result to introduce a non-linear protocol design rule allowing consensus on a quite general set of values. Such a set includes, e.g., any generalized mean of order p of the agents’ initial states. As a second contribution we show that our protocol design is the solution of individual optimizations performed by the agents. This notion suggests a game theoretic interpretation of consensus problems as mechanism design problems. Under this perspective a supervisor entails the agents to reach a consensus by imposing individual objectives. We prove that such objectives can be chosen so that rational agents have a unique optimal protocol, and asymptotically reach consensus on a desired group decision value. We use a Lyapunov approach to prove that the asymptotical consensus can be reached when the communication links between nearby agents define a time-invariant undirected network. Finally we perform a simulation study concerning the vertical alignment maneuver of a team of unmanned air vehicles

Quantized Dissensus in Networks of Agents subject to Death and Duplication

Dissensus is a modeling framework for networks of dynamic agents in competition for scarce resources. Originally inspired by biological cells behaviors, it fits also marketing, finance and many other application areas. Competition is often unstable in the sense that strong agents, those having access to large resources, gain more and more resources at the expense of weak agents. Thus, strong agents duplicate when reaching a critical amount of resources, whereas weak agents die when loosing all their resources. To capture all these phenomena we introduce systems with a discrete time gossip and unstable state dynamics interrupted by discrete events affecting the network topology. Invariancy of states and topologies and network connectivity are explored

Sensor-Based Task Ergonomics Feedback for a Passive Low-Back Exoskeleton

Low-back exoskeletons are a wide-spreading technology tackling low-back pain, the leading work-related musculoskeletal disorder in many work sectors. Currently, spring-based (i.e., passive) exoskeletons are the mostly adopted in the industry, being cheaper and generally less complex and more intuitive to use. We introduce a system of interconnected wireless sensing units to provide online ergonomics feedback to the wearer. We integrate the system into our passive low-back exoskeleton and evaluate its usability with healthy volunteers and potential end users. In this way, we provide the exoskeleton with a tool aimed both at monitoring the interaction of the system with the user, providing them with an ergonomics feedback during task execution. The sensor system can also be integrated with a custom-developed Unity3D application which can be used to interface with Augmented- or Virtual-Reality applications with higher potential for improved user feedback, ergonomics training, and offline ergonomics evaluation of the workplace. We believe that providing ergonomics feedback to exoskeleton users in the industrial sector could help further reduce the drastic impact of low-back pain and prevent its onset

Neuro-dynamic programming for cooperative inventory control

In Multi-Retailer Inventory Control the possibility of sharing set up costs motivates communication and coordination among the retailers. We solve the problem of finding suboptimal distributed reordering policies which minimize set up, ordering, storage and shortage costs, incurred by the retailers over a finite horizon. Neuro-Dynamic Programming (NDP) reduces the computational complexity of the solution algorithm from exponential to polynomial on the number of retailers

Dissensus, death and division

The modeling of switching systems describing networks where death and duplication processes occur is described. A dissensus protocol, complementary to consensus protocol, is introduced and the convergence or divergence of the agents' state evolution is studied. We discuss some properties of the topology reached by the network when different rules of duplication and inheritance are implemented. © 2009 AACC

Multiple UAV cooperative path planning via neuro-dynamic programming

In this paper, a team of n Unmanned Air-Vehicles (UAVs) in cooperative path planning is given the task of reaching the assigned target while i) avoiding threat zones ii) synchronizing minimum time arrivals on the target, and iii) ensuring arrivals coming from different directions. We highlight three main contributions. First we develop a novel hybrid model and suit it to the problem at hand. Second, we design consensus protocols for the management of information. Third, we synthesize local predictive controllers through a distributed, scalable and suboptimal neuro-dynamic programming (NDP) algorithm