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

Human Motion Trajectory Prediction: A Survey

With growing numbers of intelligent autonomous systems in human environments, the ability of such systems to perceive, understand and anticipate human behavior becomes increasingly important. Specifically, predicting future positions of dynamic agents and planning considering such predictions are key tasks for self-driving vehicles, service robots and advanced surveillance systems. This paper provides a survey of human motion trajectory prediction. We review, analyze and structure a large selection of work from different communities and propose a taxonomy that categorizes existing methods based on the motion modeling approach and level of contextual information used. We provide an overview of the existing datasets and performance metrics. We discuss limitations of the state of the art and outline directions for further research.Comment: Submitted to the International Journal of Robotics Research (IJRR), 37 page

Human Motion Behaviour Aware Planner (HMBAP) for path planning in dynamic human environments

For a robot navigating in a human inhabited dynamic environment, the knowledge of how the robot’s movement can influence the trajectory of people around it can be very valuable. In this work we present a Human Motion Behaviour Aware Planner (HMBAP) which incorporates a Human Motion Behaviour Model (HMBM) in its planning stage to take advantage of this. HMBM is a potential field based obstacle avoidance model for people and the proposed planner uses it to give the robot a prediction of how people would react to its planned path. This information is useful for the robot to avoid imminent collisions with people in constricted spaces and the planner finds solutions in situations - called freezing robot problem - where past methods fail to find a solution. The resulting robot behaviour is also similar to how a human would move (in terms of avoidance behaviour) in a similar situation. We believe that this is a desirable feature for a robot navigating in a human inhabited environment. We have implemented HMBAP in simulation and also on the real robot in the RAMP Lab. Both simulations and experiments show the effectiveness of HMBAP