相关论文: Biologically Inspired Design Principles for Scalab…
The Natural Immune System (NIS) is a distributed system that solves challenging search and response problems while operating under constraints imposed by physical space and resource availability. Remarkably, NIS search and response times do…
We inhabit a world that is not only small but supports efficient decentralized search - an individual using local information can establish a line of communication with another completely unknown individual. Here we augment a hierarchical…
Inspired by biological swarms, robotic swarms are envisioned to solve real-world problems that are difficult for individual agents. Biological swarms can achieve collective intelligence based on local interactions and simple rules; however,…
A bio-inspired robust adaptive random search algorithm (BioRARSA), designed for distributed beamforming for sensor and relay networks, is proposed in this work. It has been shown via a systematic framework that BioRARSA converges in…
Natural Immune system plays a vital role in the survival of the all living being. It provides a mechanism to defend itself from external predates making it consistent systems, capable of adapting itself for survival incase of changes. The…
This paper introduces a novel bio-mimetic approach for distributed control of robotic swarms, inspired by the collective behaviors of swarms in nature such as schools of fish and flocks of birds. The agents are assumed to have limited…
Using sensors as a means to achieve self-awareness and artificial intelligence for decision-making, may be a way to make complex systems self-adaptive, autonomous and resilient. Investigating the combination of distributed artificial…
We discuss how distributed designs that draw from biological network metaphors can largely improve the current state of information retrieval and knowledge management of distributed information systems. In particular, two adaptive…
The increasing complexity of marine operations has intensified the need for intelligent robotic systems to support ocean observation, exploration, and resource management. Underwater swarm robotics offers a promising framework that extends…
Self-organizing complex systems typically are comprised of a large number of frequently similar components or events. Through their process, a pattern at the global-level of a system emerges solely from numerous interactions among the…
The pursuit of creating artificial intelligence (AI) mirrors our longstanding fascination with understanding our own intelligence. From the myths of Talos to Aristotelian logic and Heron's inventions, we have sought to replicate the marvels…
Bio-inspired algorithms utilize natural processes such as evolution, swarm behavior, foraging, and plant growth to solve complex, nonlinear, high-dimensional optimization problems. However, a plethora of these algorithms require a more…
Evolutionary and bioinspired computation are crucial for efficiently addressing complex optimization problems across diverse application domains. By mimicking processes observed in nature, like evolution itself, these algorithms offer…
Minimalistic robot swarms offer a scalable, robust, and cost-effective approach to performing complex tasks with the potential to transform applications in healthcare, disaster response, and environmental monitoring. However, coordinating…
This paper presents the development of a distributed application that facilitates the understanding and application of swarm intelligence in solving optimization problems. The platform comprises a search space of customizable random…
Simultaneous localization and mapping (SLAM) is one of the essential techniques and functionalities used by robots to perform autonomous navigation tasks. Inspired by the rodent hippocampus, this paper presents a biologically inspired SLAM…
Although response-adaptive randomisation (RAR) has gained substantial attention in the literature, it still has limited use in clinical trials. Amongst other reasons, the implementation of RAR in real world trials raises important practical…
The acquisition of large-scale physical interaction data, a critical prerequisite for modern robot learning, is severely bottlenecked by the prohibitive cost and scalability limits of human-in-the-loop collection paradigms. To break this…
Many practical applications require solving an optimization over large and high-dimensional data sets, which makes these problems hard to solve and prohibitively time consuming. In this paper, we propose a parallel distributed algorithm…
This project proposes a bioinspired multi-robot system using Distributed Optimization for efficient exploration and mapping of unknown environments. Each robot explores its environment and creates a map, which is afterwards put together to…