Robot navigation in large-scale social maps: An action recognition approach

Abstract

As robots tend to establish their presence in every day human environments the necessity for them to attain socially acceptable behavior is a condition sine qua non. Consequently, robots need to learn and react appropriately, should they be able to share the same space with people and to reconcile their operation to man’s activity. This work proposes an integrated robot framework that allows navigation in a human populated environment. This is the first work that employs the performed actions of individuals so as to re-plan and design a collision-free and at the same time a socially acceptable trajectory. Expandability is another feature of the suggested mapping module since it is capable of incorporating an unconstrained number of actions and subsequently responses, according to the needs of the task in hand and the environment in which the robot operates. Moreover, the paper addresses the integration of the proposed mapping module with the rest of the robot framework in order to operate in a seamless fashion. The generic design of this architecture allows the replacement of modules with other similar ones, thus providing adaptability with respect to the environment and so on. The method utilizes off-line constructed 3D metric maps organized in terms of a topological graph. During its perambulation the robot is ample to detect humans while it exploits deep learning strategies to recognize their activities. The memorized actions are seamlessly associated with specific rules –deriving from the proxemics theory– and are organized in an efficient manner to be recalled during robot’s navigation. Moreover, the paper exhibits the differences of the robot navigation in inhabited and uninhabited environments and demonstrates the alteration of the robot’s trajectory with respect to the recognized actions and poses of the individuals. The system has been evaluated on a robot able to acquire RGB-D data in domestic environments. The human detection and the action recognition modules exhibited remarkable performance, the human detection one was flawless about its decision while the action recognition one confused actions regarding the number of individuals that participate in them. Last, the robot navigation component was proved capable of extracting safe trajectories in human populated environments.