Predicting Human Trajectories by Learning and Matching Patterns

Thesis document of the degree of Master of Science in Robotics of Carnegie Mellon University School of Computer Science.

Core Challenges of Social Robot Navigation: A Survey

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.

Noticing Motion Patterns: Temporal CNN with a Novel Convolution Operator for Human Trajectory Prediction

We propose a novel way to learn, detect and extract patterns in sequential data, and successfully applied it to the problem of human trajectory prediction. Our model, Social Pattern Extraction Convolution (Social-PEC), when compared to existing methods, achieves the best performance in terms of Average/Final Displacement Error. In addition, the proposed approach avoids the obscurity in the previous use of pooling layer, presenting intuitive and explainable decision making processes.

A real-time multi-constraints obstacle avoidance method based on LiDAR

Obstacle avoidance is one of the essential and indispensable functions for autonomous mobile robots. Most of the existing solutions are typically based on single condition constraint and cannot incorporate sensor data in a real-time manner, which often fail to respond to unexpected moving obstacles in dynamic unknown environments. In this paper, a novel real-time multi-constraints obstacle avoidance method based on Light Detection and Ranging(LiDAR) is proposed, which is able to, based on the latest estimation of the robot pose and environment, find the sub-goal defined by a multi-constraints function within the explored region and plan a corresponding optimal trajectory at each time step iteratively, so that the robot approaches the goal over time. Meanwhile, at each time step, the improved Ant Colony Optimization(ACO) algorithm is also used to re-plan optimal paths from the latest robot pose to the latest defined sub-goal position. While ensuring convergence, planning in this method is done by repeated local optimizations, so that the latest sensor data from LiDAR and derived environment information can be fully utilized at each step until the robot reaches the desired position. This method facilitates real-time performance, also has little requirement on memory space or computational power due to its nature, thus our method has huge potentials to benefit small low-cost autonomous platforms. The method is evaluated against several existing technologies in both simulation and real-world experiments.

High Precision In-Pipe Robot Localization with Reciprocal Sensor Fusion

The huge advantage of in-pipe robots is that they are able to measure from inside the pipes, and to sense the geometry, appearance and radiometry directly. The downside is the inability to know precise, absolute position of the measurements in very long pipe runs. This paper develops the unprecedented localization required for this purpose.