A Review of Social-Aware Navigation Frameworks for Service Robot in Dynamic Human Environments

The emergence of service robot into human daily life in the past years has opened up various challenges including human-robot interaction, joint-goal achievement and machine learning. Social-aware navigation also gains vast research attention in enhancing the social capabilities of service robots. Human motions are stochastic and social conventions are very complex. Sophisticated approaches are needed for a robot to abide to these social rules and perform obstacle avoidance. To maintain the level of social comfort and achieve a given task, the robot navigation is now no longer a search for a shortest collision-free path, but a multi-objective problem that requires a unified social-aware navigation framework. A careful selection of navigation components including global planner, local planner, the prediction model and a suitable robot platform is also required to offer an effective navigation amidst the dynamic human environment. Hence, this review paper aims to offer insights for service robot implementation by highlighting four varieties of navigation frameworks, various navigation components and different robot platforms

Motion planning in crowds using statistical model checking to enhance the social force model

International audienceCrowded environments pose a challenge to the comfort and safety of those with impaired ability. To address this challenge we have developed an efficient algorithm that may be embedded in a portable device. The algorithm anticipates undesirable circumstances in real time, by verifying simulation traces of local crowd dynamics against temporal logical formulae. The model incorporates the objectives of the user, pre-existing knowledge of the environment and real time sensor data. The algorithm is thus able to suggest a course of action to achieve the user's changing goals, while minimising the probability of problems for the user and others in the environment. To demonstrate our algorithm we have implemented it in an autonomous computing device that we show is able to negotiate complex virtual environments. The performance of our implementation demonstrates that our technology can be successfully applied in a portable device or robot