Minimally Invasive Social Navigation
This addresses social navigation for mobile robots in human environments, but appears incremental as it builds on existing flow field representations.
The paper tackled the problem of robot navigation in crowds by proposing a path quality definition based on invasiveness, with experimental results indicating its effectiveness.
Integrating mobile robots into human society involves the fundamental problem of navigation in crowds. This problem has been studied by considering the behaviour of humans at the level of individuals, but this representation limits the computational efficiency of motion planning algorithms. We explore the idea of representing a crowd as a flow field, and propose a formal definition of path quality based on the concept of invasiveness; a robot should attempt to navigate in a way that is minimally invasive to humans in its environment. We develop an algorithmic framework for path planning based on this definition and present experimental results that indicate its effectiveness. These results open new algorithmic questions motivated by the flow field representation of crowds and are a necessary step on the path to end-to-end implementations.