首页|Findings from Delft University of Technology Reveals New Findingson Robotics (A Novel Mpc Formulation for Dynamic Target TrackingWith Increased Area Coverage for Search-and-rescue Robots)
Findings from Delft University of Technology Reveals New Findingson Robotics (A Novel Mpc Formulation for Dynamic Target TrackingWith Increased Area Coverage for Search-and-rescue Robots)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News – A new study on Robotics is now availab le. According to news reporting originating inDelft, Netherlands, by NewsRx jou rnalists, research stated, “Robots are increasingly deployed for searchand-resc ue (SaR), in order to speed up rescuing the victims in the aftermath of disaster s. These robotsrequire effective mission planning approaches to determine time and space-efficient trajectories that steerthem faster towards (moving) victims , while dealing with uncertainties.”Financial supporters for this research include TU Delft AILabs program, Netherla nds Organization forScientific Research (NWO).The news reporters obtained a quote from the research from the Delft University of Technology, “Modelpredictive control (MPC) is an effective optimization-base d control approach that has been used to steerrobots along reference trajectori es determined by higher level controllers. Determining the trajectory ofthe rob ots directly via MPC has the advantage of optimizing multiple SaR criteria while handling theconstraints. We, thus, introduce a path planning approach based on MPC for indoor SaR robots thatallows the robot to systematically chase the mov ing victims, when no reference trajectory is provided.The proposed approach com bines target-oriented and coverage-oriented search, and allows for systematicha ndling of environmental uncertainties, by deploying a robust tube-based version of the introduced MPCformulation. In addition, we model the movements of the vi ctims for MPC, by adopting an existingevacuation model. We present a case study , using Gazebo, MATLAB, and ROS, where the performanceof the proposed MPC contr oller is evaluated compared to four state-of-the-art methods (two target-oriented methods based on MPC and A* and two heuristic algorithms for area coverage).”
DelftNetherlandsEuropeEmerging Tec hnologiesMachineLearningNano-robotRoboticsDelft University of Technolog y
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