首页|Reports from Wuhan University of Technology Highlight Recent Findings in Robotic s (Obstacle Avoidance Planning for Industrial Robots Based On Singular Manifold Splitting Configuration Space)
Reports from Wuhan University of Technology Highlight Recent Findings in Robotic s (Obstacle Avoidance Planning for Industrial Robots Based On Singular Manifold Splitting Configuration Space)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Data detailed on Robotics have been pr esented. According to news originating from Wuhan, People's Republic of China, b y NewsRx correspondents, research stated, "Obstacle avoidance planning is the pr imary element in ensuring safe robot applications such as welding, assembly, and drilling. The states in the configuration space (C-space) provide the pose info rmation of any part of the manipulator and are preferentially considered in moti on planning." Financial support for this research came from National Natural Science Foundatio n of China (NSFC). Our news journalists obtained a quote from the research from the Wuhan University of Technology, "However, it is difficult to express the environmental informat ion directly in the high dimensional C-space, limiting the application of C-spac e obstacle avoidance planning. This paper proposes a singular manifold splitting C-space method and designs a compatible obstacle avoidance strategy. The specif ic method is as follows: first, according to the specific structure of industria l robots, arm-wrist separation obstacle avoidance planning is proposed to fix th e robot as a 3R manipulator to reduce the dimension of C-space. Next, the C-spac e is segmented according to the singular manifolds, and the unique domain is del ineated to complete the streamlining of the volume of the C-space. Then, with th e help of the point cloud, the obstacles are enveloped and mapped to the unique domain to construct the pseudo-obstacle map. Industrial robots' obstacle avoidan ce planning is completed based on the pseudo-obstacle map combined with an impro ved Rapidly-Exploring Random Trees (RRT) algorithm. This method dramatically imp roves the efficiency of obstacle avoidance planning in the C-space and avoids th e effect of singularities on industrial robots."
WuhanPeople's Republic of ChinaAsiaEmerging TechnologiesMachine LearningNano-robotRobotRoboticsWuhan Uni versity of Technology
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