Online state-time trajectory planning in highly dynamic environments remains an unsolved problem due to the curse of dimensionality of the state-time space. Existing state-time planners are typically implemented based on randomized sampling approaches or path searching on discrete graphs. The smoothness, path clearance, or planning efficiency is sometimes not satisfying. In this work, we propose a gradient-based planner on the state-time space for online trajectory generation in highly dynamic environments. To enable the gradient-based optimization, we propose a Timed-ESDT that supports distance and gradient queries with state-time keys. Based on the Timed-ESDT, we also define a smooth prior and an obstacle likelihood function that are compatible with the state-time space. The trajectory planning is then formulated to a MAP problem and solved by an efficient numerical optimizer. Moreover, to improve the optimality of the planner, we also define a state-time graph and conduct path searching on it to find a better initialization for the optimizer. By integrating the graph searching, the planning quality is significantly improved. Experiments on simulated and benchmark datasets demonstrate the superior performance of our proposes method over conventional ones.

This paper presents a search-based partial motion planner for generating feasible trajectories of car-like robots in highly dynamic environments. The planner searches for smooth, safe, and near-time-optimal trajectories by exploring a state graph built on motion primitives. To enable fast online planning, we propose an efficient path searching algorithm based on the aggregation and pruning of motion primitives. We then propose a fast collision checking algorithm that takes into account the motions of moving obstacles. The algorithm linearizes relative motions between the robot and obstacles, and then checks collisions by calculating a point-line distance. Benefiting from the fast searching and collision checking algorithms, the planner can effectively explore the state-time space to generate near-time-optimal solutions. Experiments show that the proposed method can generate feasible trajectories within milliseconds while maintaining a higher success rate than up-to-date methods, which significantly demonstrates its advantages.

Autonomous elevator operation is considered an intelligent solution in handling the inter-floor navigation problem of service robots. As one of the most fundamental steps, elevator button recognition starts to receive more and more attention. However, due to the challenging image conditions and severe class imbalance problem, the performance of existing results is unsatisfying. In this paper, we propose to combine an optical character recognition (OCR) network and the Faster RCNN architecture into a single neural network, called OCR-RCNN to facilitate an end-to-end training and elevator button recognition procedure. To verify our method, we collect a large dataset of elevator panels and carry out extensive comparative experiments. The experiment results show that our method can greatly outperform the traditional recognition pipelines, yielding an accurate and robust performance on recognizing untrained elevator buttons.