Robot navigation in crowded public spaces is a complex task that requires addressing a variety of engineering and human factors challenges. These challenges have motivated a great amount of research resulting in important developments for the fields of robotics and human-robot interaction over the past three decades. Despite the significant progress and the massive recent interest, we observe a number of significant remaining challenges that prohibit the seamless deployment of autonomous robots in public pedestrian environments. In this survey article, we organize existing challenges into a set of categories related to broader open problems in motion planning, behavior design, and evaluation methodologies. Within these categories, we review past work, and offer directions for future research. Our work builds upon and extends earlier survey efforts by a) taking a critical perspective and diagnosing fundamental limitations of adopted practices in the field and b) offering constructive feedback and ideas that we aspire will drive research in the field over the coming decade.

In this paper, we demonstrate through user studies that mobile robot trajectories that imitate human-to-human approach trajectories are perceived more socially acceptable in the face-to-face interaction scenario than those imitating point-to-point trajectories. We generate robot trajectories to/from a human standing at an arbitrary location by applying inverse optimal control to a human-to-human trajectory dataset. The cost function used in a previous work for modeling human point-to-point trajectories does not represent human-to-human trajectories due to the circular paths often observed around the target human. We therefore propose a new cost function motivated by the social force model. The user study confirms that the resulting trajectories are more preferred with statistical significance than baseline.

Constructing realistic and real time human-robot interaction models is a core challenge in crowd navigation. In this paper we derive a robot-agent interaction density from first principles of probability theory; we call our approach “first order interacting Gaussian processes” (foIGP). Furthermore, we compute locally optimal solutions—with respect to multi-faceted agent “intent” and “flexibility”—in near real time on a laptop CPU. We test on challenging scenarios from the ETH crowd dataset and show that the safety and efficiency statistics of foIGP is competitive with human safety and efficiency statistics. Further, we compute the safety and efficiency statistics of dynamic window avoidance, a physics based model variant of foIGP, a Monte Carlo inference based approach, and the best performing deep reinforcement learning algorithm; foIGP outperforms all of them.

Mobile robots moving in crowded environments have to navigate among pedestrians safely. Ideally, the way the robot avoids the pedestrians should not only be physically safe but also perceived safe and comfortable. Despite the rich literature in collision-free crowd navigation, limited research has been conducted on how humans perceive robot behaviors in the navigation context. In this paper, we implement three local pedestrian avoidance strategies inspired by human avoidance behaviors on a self-balancing mobile robot and evaluate their perception in a human-robot crossing scenario through a large-scale user study with 98 participants. The study reveals that the avoidance strategies positively affect the participants' perception of the robot's safety, comfort, and awareness to different degrees. Furthermore, the participants perceive the robot as more intelligent, friendly and reliable in the last trial than in the first even with the same strategy.

Real-time navigation in dense human environments is a challenging problem in robotics. Most existing path planners fail to account for the dynamics of pedestrians because introducing time as an additional dimension in search space is computationally prohibitive. Alternatively, most local motion planners only address imminent collision avoidance and fail to offer long-term optimality. In this work, we present an approach, called Dynamic Channels, to solve this global to local quandary. Our method combines the high-level topological path planning with low-level motion planning into a complete pipeline. By formulating the path planning problem as graph-searching in the triangulation space, our planner is able to explicitly reason about the obstacle dynamics and capture the environmental change efficiently. We evaluate the efficiency and performance of our approach on public pedestrian datasets and compare it to a state-of-the-art planning algorithm for dynamic obstacle avoidance. Completeness proofs are provided in the supplement at http://caochao.me/files/proof.pdf. An extended version of the paper is available on arXiv.