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Visual-RRT*: Generating Asymptotically Optimal Trajectories with Vision-based Controllers
In this talk, we will present a planning method for robotic systems that, by construction, guarantees the planned trajectories to be executable in closed-loop with a visual servoing controller, in addition to assuring their asymptotically optimality in the state space. The design of this method requires one to obtain a locally optimal steering method, locally stable closed-loop controllers used to track the optimal trajectories, and a mapping between the motion of the robot in the state space and the corresponding motion of the visual features in the image space. As a result, the proposed approach allows the system to navigate in a closed-loop manner among non-convex obstacles, following asymptotically optimal trajectories. The method is validated using two systems: a nonholonomic robot and a robotic arm. Real-world experiments are carried out for the first system.
Mots-clés : Motion Planning; Visual servoing
By Visual-RRT*: Generating Asymptotically Optimal Trajectories with Vision-based Controllers
In this talk, we will present a planning method for robotic systems that, by construction, guarantees the planned trajectories to be executable in closed-loop with a visual servoing controller, in addition to assuring their asymptotically optimality in the state space. The design of this method requires one to obtain a locally optimal steering method, locally stable closed-loop controllers used to track the optimal trajectories, and a mapping between the motion of the robot in the state space and the corresponding motion of the visual features in the image space. As a result, the proposed approach allows the system to navigate in a closed-loop manner among non-convex obstacles, following asymptotically optimal trajectories. The method is validated using two systems: a nonholonomic robot and a robotic arm. Real-world experiments are carried out for the first system.
Mots-clés : Motion Planning; Visual servoing