Heuristics and Rescheduling in Prioritised Multi-Robot Path Planning: A Literature Review

The benefits of multi-robot systems are substantial, bringing gains in efficiency, quality, and cost, and they are useful in a wide range of environments from warehouse automation, to agriculture and even extend in part to entertainment. In multi-robot system research, the main focus is on ensuring efficient coordination in the operation of the robots, both in task allocation and navigation. However, much of this research seldom strays from the theoretical bounds; there are many reasons for this, with the most prominent and -impactful being resource limitations. This is especially true for research in areas such as multi-robot path planning (MRPP) and navigation coordination. This is a large issue in practice as many approaches are not designed with meaningful real-world implications in mind and are not scalable to large multi-robot systems. This survey aimed to look into the coordination and path-planning issues and challenges faced when working with multi-robot systems, especially those using a prioritised planning approach and identify key areas that are not well-explored and the scope of applying existing MRPP approaches to real-world settings

Multi AGV coordination tolerant to communication failures

Nowadays, in many industrial environments that use several robots, there is the problem of controlling traffic. To control traffic, it is necessary to plan safe paths, avoid deadlocks and be immune to network failures. The objective of this project is to implement a system to control this traffic, that is, plan and control one fleet of robots that have multiple tasks to fulfill. This system will be based on implementing TEA *, an algorithm based on A * but that goes with the notion of time. At the same time, the system attempts to supervise, to see if the robots are fulfilling the plan and to guard against any communication failures

静的・動的環境における通信なしマルチエージェント強化学習の協調行動を導く合理的目的設定

電気通信大学201

An Approach Based on Particle Swarm Optimization for Inspection of Spacecraft Hulls by a Swarm of Miniaturized Robots

The remoteness and hazards that are inherent to the operating environments of space infrastructures promote their need for automated robotic inspection. In particular, micrometeoroid and orbital debris impact and structural fatigue are common sources of damage to spacecraft hulls. Vibration sensing has been used to detect structural damage in spacecraft hulls as well as in structural health monitoring practices in industry by deploying static sensors. In this paper, we propose using a swarm of miniaturized vibration-sensing mobile robots realizing a network of mobile sensors. We present a distributed inspection algorithm based on the bio-inspired particle swarm optimization and evolutionary algorithm niching techniques to deliver the task of enumeration and localization of an a priori unknown number of vibration sources on a simplified 2.5D spacecraft surface. Our algorithm is deployed on a swarm of simulated cm-scale wheeled robots. These are guided in their inspection task by sensing vibrations arising from failure points on the surface which are detected by on-board accelerometers. We study three performance metrics: (1) proximity of the localized sources to the ground truth locations, (2) time to localize each source, and (3) time to finish the inspection task given a 75% inspection coverage threshold. We find that our swarm is able to successfully localize the present so

Coordenação de multi-robots num ambiente industrial

Atualmente, uma grande diversidade dos ambientes industriais recorrem à utilização de vários robôs móveis para executar as tarefas a si associadas. Pela grande mobilidade que lhes é conferida surge o problema de controlo de tráfego, dentro de um ambiente limitado. Para que tal seja possível é necessário implementar um sistema capaz de efetuar a coordenação entre os diferentes veículos, evitando colisões e bloqueios mútuos, também designados por deadlocks. O principal foco desta dissertação prende-se, portanto, na implementação desse sistema, tendo como base o algoritmo de planeamento de trajetórias TEA*. Tendo como base o algoritmo A*, este promove uma pesquisa dos caminhos ótimos e livres de colisão ao longo de diversas camadas temporais. A ideia fundamental do algoritmo passa por planear a trajetória de cada robô tendo como ponto de partida as posições correntes e futuras de cada um dos seus veículos concorrentes. Após a sua implementação pretende-se validar o sistema em ambiente real, através da utilização de 3 a 4 robôs inseridos numa plataforma de testes coma geometria de um labirinto

Co-Generative: A Generative Design Paradigm for Fostering Regenerative Communities

Holistic sustainability literature argues that many of the most serious problems we face today are a result of innovation that is disconnected from its local ecological context. One of the innovations being explored to address the challenges of designing complex built environments is generative design; a collaborative design process that augments human design capabilities with computational power to explore a multitude of design alternatives. Regenerative design is a holistic design approach that builds on understanding ecosystem patterns that regenerate a system’s health and vitality. In this Major Research Project, I explore the bridge between generative design and regenerative design to propose a computationally-augmented design approach that contributes to fostering the health of the system as a whole. Using a strategic foresight framework, I deconstruct the current computational generative design paradigm and construct a new one based on a metaphor of perpetuity and a worldview that values collective flourishing, abundance, and appropriate participation. Building on this new paradigm, I propose a revised generative design workflow that emphasizes collaboration, connectedness with the land, participatory foresight, and emergence. I conclude that a regenerative generative design approach is community, context, and complexity-sensitive

Agents for educational games and simulations

This book consists mainly of revised papers that were presented at the Agents for Educational Games and Simulation (AEGS) workshop held on May 2, 2011, as part of the Autonomous Agents and MultiAgent Systems (AAMAS) conference in Taipei, Taiwan. The 12 full papers presented were carefully reviewed and selected from various submissions. The papers are organized topical sections on middleware applications, dialogues and learning, adaption and convergence, and agent applications