摘要
This paper presents a novel movement planning algorithm for a guard robot in an indoor environment, imitating the job of human security. A movement planner is employed by the guard robot to continuously observe a certain person. This problem can be distinguished from the person following problem which continuously follows the object. Instead, the movement planner aims to reduce the movement and the energy while keeping the target person under its visibility. The proposed algorithm exploits the topological features of the environment to obtain a set of viewpoint candidates, and it is then optimized by a cost-based set covering problem. Both the robot and the target person are modeled using geodesic motion model which considers the environment shape. Subsequently, a particle model-based planner is employed, considering the chance constraints over the robot visibility, to choose an optimal action for the robot. Simulation results using 3D simulator and experiments on a real environment are provided to show the feasibility and effectiveness of our algorithm.
This paper presents a novel movement planning algorithm for a guard robot in an indoor environment,imitating the job of human security.A movement planner is employed by the guard robot to continuously observe a certain person.This problem can be distinguished from the person following problem which continuously follows the object.Instead,the movement planner aims to reduce the movement and the energy while keeping the target person under its visibility.The proposed algorithm exploits the topological features of the environment to obtain a set of viewpoint candidates,and it is then optimized by a cost-based set covering problem.Both the robot and the target person are modeled using geodesic motion model which considers the environment shape.Subsequently,a particle model-based planner is employed,considering the chance constraints over the robot visibility,to choose an optimal action for the robot.Simulation results using 3 D simulator and experiments on a real environment are provided to show the feasibility and effectiveness of our algorithm.
