1,636 research outputs found
Special issue on smart interactions in cyber-physical systems: Humans, agents, robots, machines, and sensors
In recent years, there has been increasing interaction between humans and non‐human systems as we move further beyond the industrial age, the information age, and as we move into the fourth‐generation society. The ability to distinguish between human and non‐human capabilities has become more difficult to discern. Given this, it is common that cyber‐physical systems (CPSs) are rapidly integrated with human functionality, and humans have become increasingly dependent on CPSs to perform their daily routines.The constant indicators of a future where human and non‐human CPSs relationships consistently interact and where they allow each other to navigate through a set of non‐trivial goals is an interesting and rich area of research, discovery, and practical work area. The evidence of con- vergence has rapidly gained clarity, demonstrating that we can use complex combinations of sensors, artificial intelli- gence, and data to augment human life and knowledge. To expand the knowledge in this area, we should explain how to model, design, validate, implement, and experiment with these complex systems of interaction, communication, and networking, which will be developed and explored in this special issue. This special issue will include ideas of the future that are relevant for understanding, discerning, and developing the relationship between humans and non‐ human CPSs as well as the practical nature of systems that facilitate the integration between humans, agents, robots, machines, and sensors (HARMS).Fil: Kim, Donghan. Kyung Hee University;Fil: Rodriguez, Sebastian Alberto. Universidad Tecnológica Nacional; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tucumán; ArgentinaFil: Matson, Eric T.. Purdue University; Estados UnidosFil: Kim, Gerard Jounghyun. Korea University
Particle Swarm Optimization (PSO) for Simulating Robot Movement on Two-Dimensional Space Based on Odor Sensing
Nowadays, researches in robotic field have grown increasingly. There are several types of research categories in the field of robotic. Recently, one of the famous research works recently was odor sensing. Within the technology that grows rapidly, this topic has become an interest among researchers. An odor sensing is not only applied in the medical field, but it has also been widely used in the industry. The gradient of concentration of an odor is measured by diluting some amount to reach the threshold of an odor. This paper focused on the implementation of the Particle Swarm Optimization (PSO) method based on odor sensing in two (2) dimensional space. However, it only discusses and focuses on applying in ideal condition. An ideal condition here means that there is no disturbance included in this simulation. The main idea of this paper was to observe how the particle agents make the movement based on concentration by applying the PSO method. The real sensor cannot be implemented in this simulation because the value of concentration is measured due to the distance from the particles agent to the goal of agents. Higher gradient concentration is shown at the shorter distance to the goal. The contributions in this paper are mainly to create an algorithms model by using Particle Swarm Optimization (PSO) to calculate the paths of movement of mobile robot until they reach the goals (source of odor) with respect to the concepts of odor sensing
Study of Cooperative Control System for Multiple Mobile Robots Using Particle Swarm Optimization
The idea of using multiple mobile robots for tracking targets in an unknown environment can be realized with Particle Swarm Optimization proposed by Kennedy and Eberhart in 1995. The actual implementation of an efficient algorithm like Particle Swarm Optimization (PSO) is required when robots need to avoid the randomly placed obstacles in unknown environment and reach the target point. However, ordinary methods of obstacle avoidance have not proven good results in route planning. PSO is a self-adaptive population-based method in which behavior of the swarm is iteratively generated from the combination of social and cognitive behaviors and is an effective technique for collective robotic search problem. When PSO is used for exploration, this algorithm enables robots to travel on trajectories that lead to total
swarm convergence on some target
Multiple robot co-ordination using particle swarm optimisation and bacteria foraging algorithm
The use of multiple robots to accomplish a task is certainly preferable over the use of specialised individual robots. A major problem with individual specialized robots is the idle-time, which can be reduced by the use of multiple general robots, therefore making the process economical. In case of infrequent tasks, unlike the ones like assembly line, the use of dedicated robots is not cost-effective. In such cases, multiple robots become essential. This work involves path-planning and co-ordination between multiple mobile agents in a static-obstacle environment. Multiple small robots (swarms) can work together to accomplish the designated tasks that are difficult or impossible for a single robot to accomplish. Here Particle Swarm Optimization (PSO) and Bacteria Foraging Algorithm (BFA) have been used for coordination and path-planning of the robots. PSO is used for global path planning of all the robotic agents in the workspace. The calculated paths of the robots are further optimized using a localised BFA optimization technique. The problem considered in this project is coordination of multiple mobile agents in a predefined environment using multiple small mobile robots. This work demonstrates the use of a combinatorial PSO algorithm with a novel local search enhanced by the use of BFA to help in efficient path planning limiting the chances of PSO getting trapped in the local optima. The approach has been simulated on a graphical interface
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