We consider the problem of moving a point robot through a two dimensional workspace containing polygonal obstacles moving on unknown trajectories. We propose to use sensor information to predict the trajectories of the obstacles, and interleave path planning and execution. In this paper, we present preliminary work in which we propose our basic algorithm and define a locally minimum velocity path as an optimal robot trajectory, given only local information about obstacle trajectories. In the sequel (part II) to this paper we will show that the complexity of a path planning problem can be characterized by how frequently the robot must change directions to approximate the locally minimum velocity path.

In a measurement system with intelligent, distributed sensor processes, complementary observations from different sensor need be combined with each other. This paper describes a method based on fuzzy measures, in which a global ‘fusion algorithm’ questions the sensors as to their support and opinion of a hypothesis. The sensor opinions are clustered into groups based on their support of each others' opinions, and fused using a new fuzzy operator.

Continuing the work presented in part I, ‡ we consider the problem of moving a point robot through a two dimensional workspace containing polygonal obstacles moving on unknown trajectories. We propose to use sensor information to predict the trajectories of the obstacles, and interleave path planning and execution. We define a locally minimum velocity path as an optimal robot trajectory, given only local information about obstacle trajectories. We show that the complexity of a path planning problem can be characterized by how frequently the robot must change directions to approximate the locally minimum velocity path. Our results apply to both robots with and without maximum velocity limits.