首页|Reports Outline Robotics Study Results from University of Auckland (Trajectory P lanning and Tracking of Multiple Objects On a Soft Robotic Table Using a Hierarc hical Search On Time-varying Potential Fields)
Reports Outline Robotics Study Results from University of Auckland (Trajectory P lanning and Tracking of Multiple Objects On a Soft Robotic Table Using a Hierarc hical Search On Time-varying Potential Fields)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Fresh data on Robotics are presented i n a new report. According to news reporting out of Auckland, New Zealand, by New sRx editors, research stated, "This article presents a control strategy to carry out multiobject manipulation on a novel soft robotic table (SoTa), which is a n ew form of the planar distributed manipulator. Manipulating multiple delicate ob jects simultaneously is an attractive feature of SoTa." Financial support for this research came from China Scholarship Council. Our news journalists obtained a quote from the research from the University of A uckland, "The challenge here is to coordinate multiple objects in a confined pla nar space while avoiding interference with each other. The SoTa system adopts a manipulation strategy that includes a planning and a tracking stage for the purp ose of sorting objects. The planning stage consists of two phases: 1) discrete p ath planning to find a path for each object on a grid map with respect to time; 2) trajectory generation to optimize and produce workable trajectories for SoTa. In the discrete path planning phase, a hierarchical searching method based on t he time-varying potential field is proposed. Constraints of the SoTa system are modeled and incorporated into the path searching process. In the trajectory gene ration phase, a piecewise B-spline method is adopted to generate trajectories ba sed on previously found discrete paths. Next, in the tracking stage, the objects are led to their goals along the trajectories ensuring safety and SoTa's capabi lity. The performances of the proposed algorithm were simulated, analyzed, and c ompared with the conflict-based search method, which is optimal for multiagent p ath finding. A multiobject manipulation experiment of three objects on a 4 x 4 g rid was conducted on the SoTa."
AucklandNew ZealandAustralia and New ZealandEmerging TechnologiesMachine LearningRoboticsRobotsUniversity of Auckland
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