Viewing Robot Navigation in Human Environment as a Cooperative Activity

Abstract

We claim that navigation in human environments can be viewed as cooperative activity especially in constrained situations. Humans concurrently aid and comply with each other while moving in a shared space. Cooperation helps pedestrians to efficiently reach their own goals and respect conventions such as the personal space of others. To meet human comparable efficiency, a robot needs to predict the human trajectories and plan its own trajectory correspondingly in the same shared space. In this work, we present a navigation planner that is able to plan such cooperative trajectories, simultaneously enforcing the robot's kinematic constraints and avoiding other non-human dynamic obstacles. Using robust social constraints of projected time to a possible future collision, compatibility of human-robot motion direction, and proxemics, our planner is able to replicate human-like navigation behavior not only in open spaces but also in confined areas. Besides adapting the robot trajectory, the planner is also able to proactively propose co-navigation solutions by jointly computing human and robot trajectories within the same optimization framework. We demonstrate richness and performance of the cooperative planner with simulated and real world experiments on multiple interactive navigation scenarios.

HATEB-2: Reactive Planning and Decision making in Human-Robot Co-navigation

“…All these approaches are effective in densely crowded environments as a virtue of remaining purely reactive but could lead to needless detours in intricate situations. Our previous work [4] is specifically developed to handle such intricate situations in semi-crowded environments. Such planning for humans along with the robot is usually required in robot-human handover scenarios, to know where to perform a task, and who performs a task [17], [18].…”

“…Consequently, human motion predictions and estimations were introduced into human-aware navigation to have a better planning system. Our previous work, Human Aware Timed Elastic Band (HATEB) based co-navigation planner [4] falls into this category, including human estimations and predictions. HATEB includes human predictions by simultaneously planning for humans and the robot.…”

Human-Like Motion Planning Based on Game Theoretic Decision Making

“…They evaluated their approach by counting how often the social rule of passing on the appropriate side was fulfilled in simulated scenarios. Khambhaita and Alami [34] analyzed whether their approach results in a mutual avoidance with an unequal amount of shared effort by plotting the trajectories recorded during different avoidance scenarios. They implemented the social convention that the robot takes "most of the load" during the avoidance by combining a time elastic band approach with graph optimization that can manage kinodynamic and social constraints.…”

Reasoning on Shared Visual Perspective to Improve Route Directions

“…We focus here on the selection of a shared visual perspective for providing route directions, but this work is part of an overall project that aims to implement and evaluate a complete system for the guiding task. This involves the development of a number of other components such as a human perception system [12], a human-aware reactive motion planner [13], a Human-Robot joint action supervision [14] and the associated dialogue [15].…”