首页|Findings from School of Mechanical Engineering in Robotics Reported (Behaviour-d efined Navigation Framework for Dynamical Obstacle Avoidance In Multi-robot Syst ems Consisting of Holonomic Robots)

Findings from School of Mechanical Engineering in Robotics Reported (Behaviour-d efined Navigation Framework for Dynamical Obstacle Avoidance In Multi-robot Syst ems Consisting of Holonomic Robots)

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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New research on Robotics is the subjec t of a report. According to news reporting out of Vellore, India, by NewsRx edit ors, research stated, “Dynamical obstacle avoidance is a challenging problem in the field of autonomous robot navigation. Current research in this field has bee n mostly limited to single robots, thus, there exists a gap in research in the f ield of dynamical obstacle avoidance in multi robot systems.” Our news journalists obtained a quote from the research from the School of Mecha nical Engineering, “While rich literature is available on multirobot systems, th is paper attempts to propose a novel navigation framework for an environment whi ch includes multiple robots. The proposed navigation framework applies certain b ehaviours to ensure a safe trajectory for the multi-robot systems. As opposed to other reported literature which focused on implementing their algorithms on non -holonomic robots, the proposed navigation framework is implemented on several h olonomic robots. Simulations and real-life experiments were carried out using th e proposed framework. Dynamic obstacles are considered in the environment and Kh ep era IV robots are used to conduct real-life experiments. Two dynamic obstacle s were placed at different positions in the workspace. These obstacles had linea r movements, whereby each robot could move horizontally and vertically across th e workspace. Three experimental trials were performed.”

VelloreIndiaAsiaEmerging Technolog iesMachine LearningNano-robotRobotRoboticsSchool of Mechanical Enginee ring

2024

Robotics & Machine Learning Daily News

Robotics & Machine Learning Daily News

ISSN:
年,卷(期):2024.(Oct.15)