This paper investigates the problem of multi-UAV uniform sweep coverage, where a homogeneous swarm of UAVs must collectively and evenly visit every portion of an unknown environment for a sampling task without having access to their own location and orientation. Random walk-based exploration strategies are practical for such a coverage scenario as they do not rely on localization and are easily implementable in robot swarms. We demonstrate that the Mergeable Nervous System (MNS) framework, which enables a robot swarm to self-organize into a hierarchical ad-hoc communication network using local communication, is a promising control approach for random exploration in unknown environments by UAV swarms. To this end, we propose an MNS-based random walk approach where UAVs self-organize into a line formation using the MNS framework and then follow a random walk strategy to cover the environment while maintaining the formation. Through simulations, we test the efficiency of our approach against several decentralized random walk-based strategies as benchmarks. Our results show that the MNS-based random walk outperforms the benchmarks in terms of the time required to achieve full coverage and the coverage uniformity at that time, assessed across both the entire environment and within local regions.

In swarm robotics, decentralized control is often proposed as a more scalable and fault-tolerant alternative to centralized control. However, centralized behaviors are often faster and more efficient than their decentralized counterparts. In any given application, the goals and constraints of the task being solved should guide the choice to use centralized control, decentralized control, or a combination of the two. Currently, the tradeoffs that exist between centralization and decentralization have not been thoroughly studied. In this paper, we investigate these tradeoffs for multi-robot coverage, and find that they are more nuanced than expected. For instance, our findings reinforce the expectation that more decentralized control will provide better scalability, but contradict the expectation that more decentralized control will perform better in environments with randomized obstacles. Beginning with a group of fully independent ground robots executing coverage, we add unmanned aerial vehicles as supervisors and progressively increase the degree to which the supervisors use centralized control, in terms of access to global information and a central coordinating entity. We compare, using the multi-robot physics-based simulation environment ARGoS, the following four control approaches: decentralized control, hybrid control, centralized control, and predetermined control. In comparing the ground robots performing the coverage task, we assess the speed and efficiency advantages of centralization -- in terms of coverage completeness and coverage uniformity -- and we assess the scalability and fault tolerance advantages of decentralization. We also assess the energy expenditure disadvantages of centralization due to different energy consumption rates of ground robots and unmanned aerial vehicles, according to the specifications of robots available off-the-shelf.

The coordination of autonomous vehicles is an open field that is addressed by different researches comprising many different techniques. In this paper we focus on decentralized approaches able to provide adaptability to different infrastructural and traffic conditions. We formalize an Emergent Behavior Approach that, as per our knowledge, has never been performed for this purpose, and a Decentralized Auction approach. We compare them against existing centralized negotiation approaches based on auctions and we determine under which conditions each approach is preferable to the others.

Coordination of radars can be performed in various ways. To be more resilient radar networks can be coordinated in a decentralized way. In this paper, we introduce a highly resilient algorithm for radar coordination based on decentralized and collaborative bundle auctions. We first formalize our problem as a constrained optimization problem and apply a market-based algorithm to provide an approximate solution. Our approach allows to track simultaneously multiple targets, and to use up to two radars tracking the same target to improve accuracy. We show that our approach performs sensibly as well as a centralized approach relying on a MIP solver, and depending on the situations, may outperform it or be outperformed.

The Chinese government has already jumped on the metaverse bandwagon, that immersive digital world being developed by companies like Meta. But the country's leaders don't intend to compete with the US for primacy in this new race – they want to build a domestic metaverse tailored to Chinese Communist Party (CCP) objectives. It's a vision that enables the private sector to develop key technology for the Asian giant, but also maintains what the government euphemistically calls "social peace." The state machinery's wheels are already turning. In 2021, more than 10,000 metaverse-related trademarks were registered in China, compared to less than 1,000 in 2020 and 2019. So far in 2022, 16,000 trademark applications have been submitted.

Experimental advances enabling high-resolution external control create new opportunities to produce materials with exotic properties. In this work, we investigate how a multi-agent reinforcement learning approach can be used to design external control protocols for self-assembly. We find that a fully decentralized approach performs remarkably well even with a "coarse" level of external control. More importantly, we see that a partially decentralized approach, where we include information about the local environment allows us to better control our system towards some target distribution. We explain this by analyzing our approach as a partially-observed Markov decision process. With a partially decentralized approach, the agent is able to act more presciently, both by preventing the formation of undesirable structures and by better stabilizing target structures as compared to a fully decentralized approach.

Full-stack AI solution SingularityNET is switching the Ethereum blockchain for peer-reviewed rival Cardano. SingularityNET is a decentralised AI marketplace which has the ultimate goal of forming the basis for the emergence of the world's first true Artificial General Intelligence (AGI). One of the brightest and most respected minds in AI leads the SingularityNET project, Dr Ben Goertzel. "Current speed and cost issues with the Ethereum blockchain have increased the urgency of exploring alternatives for SingluarityNET's blockchain underpinning," says Goertzel. "The ambitious Ethereum 2.0 design holds promise but the timing of rollout of different aspects of this next-generation Ethereum remains unclear, along with many of the practical particulars."

A rail company organizes its fleet to accommodate expected demands, maximizing revenue and coverage, so that the service is provided to customers as far as possible. From a practical point of view, companies have to make decisions for two different time horizons: offline and online. Offline decisions deal with the problem of routing trains in advance, so that the basic path for each train is decided and in normal conditions, these are the one that will be followed. Decisions in this sense are made sporadically in a year, typically once every three to six months. The planned routes and schedules are usually hand-engineered according to regulation, safety measures, and demand requirements. As said, planned routes are the ones preferred in normal conditions, but this rarely happens since disruptions occur daily in the network. A broken train, a not working switch, delays in the preparation of the train, and many more real-life problems may affect the overall network. Sometimes the delay introduced is small and the planned schedule can still be used, but on other occasions, online rerouting and rescheduling have to be applied. In literature, this online decision making is called the Train Dispatching problem (TD), a real-time variant of the Train Timetabling problem (known to be NPhard [3]).

Decentralized agent groups typically require complex mechanisms to accomplish coordinated tasks. In contrast, biological systems can achieve intelligent group behaviors with each agent performing simple sensing and actions. We summarize our recent papers on a biologically-inspired control framework for multi-agent tasks that is based on a simple and iterative control law. We theoretically analyze important aspects of this decentralized approach, such as the convergence and scalability, and further demonstrate how this approach applies to real-world applications with a diverse set of multi-agent applications. These results provide a deeper understanding of the contrast between centralized and decentralized algorithms in multi-agent tasks and autonomous robot control.