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This paper presents a trajectory planning algorithm for a robot operating in dynamic human environments. Environments such as pedestrian streets, hospital corridors, train stations or airports. We formulate the problem as planning a minimal cost trajectory through a potential field, defined from the perceived position and motion of persons in the environment. A Rapidly-exploring Random Tree (RRT)...
Robot path planning has traditionally concentrated on collision-free paths. For robots that collaborate closely with humans, however, the situation is different in two respects: 1) the humans in the robot's environment are not randomly moving objects, but cognitive beings who can deliberately make way for a robot to pass and 2) the quality of a navigation plan depends less on quantitative efficiency...
We present a computational framework and experimental platform for robot navigation that allows for a user-friendly, graphical and haptic interaction with the human operator during the deployment process. The operator can see, feel, and manipulate the artificial potential field that drives the robot through an environment cluttered with obstacles. We present a case study in which the operator rescues...
The unique ability of humanoid robots to step over or upon obstacles is left unexploited if ordinary mobile robot navigation strategies are used for humanoids as well. Recently presented path planning strategies that make use of this capability, however, are very time consuming due to high computational complexity. We have presented a novel approach to humanoid robot footstep planning in obstacle...
This paper introduces the COMPANION framework: a constraint-optimizing method for person-acceptable navigation. In this framework, human social conventions, such as personal space and tending to one side of hallways, are represented as constraints on the robot's navigation. These constraints are accounted for at the global planning level. In this paper, we present the rationale for, and implementation...
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