Multi-Goal Motion Memory

Autonomous mobile robots (e.g., warehouse logistics robots) often need to traverse complex, obstacle-rich, and changing environments to reach multiple fixed goals (e.g., warehouse shelves). Traditional motion planners need to calculate the entire multi-goal path from scratch in response to changes in the environment, which result in a large consumption of computing resources. This process is not only time-consuming but also may not meet real-time requirements in application scenarios that require rapid response to environmental changes. In this paper, we provide a novel Multi-Goal Motion Memory technique that allows robots to use previous planning experiences to accelerate future multi-goal planning in changing environments. Specifically, our technique predicts collision-free and dynamically-feasible trajectories and distances between goal pairs to guide the sampling process to build a roadmap, to inform a Traveling Salesman Problem (TSP) solver to compute a tour, and to efficiently produce motion plans. Experiments conducted with a vehicle and a snake-like robot in obstacle-rich environments show that the proposed Motion Memory technique can substantially accelerate planning speed by up to 90\%. Furthermore, the solution quality is comparable to state-of-the-art algorithms and even better in some environments.

Accounting for Work Zone Disruptions in Traffic Flow Forecasting

Traffic speed forecasting is an important task in intelligent transportation system management. The objective of much of the current computational research is to minimize the difference between predicted and actual speeds, but information modalities other than speed priors are largely not taken into account. In particular, though state of the art performance is achieved on speed forecasting with graph neural network methods, these methods do not incorporate information on roadway maintenance work zones and their impacts on predicted traffic flows; yet, the impacts of construction work zones are of significant interest to roadway management agencies, because they translate to impacts on the local economy and public well-being. In this paper, we build over the convolutional graph neural network architecture and present a novel ``Graph Convolutional Network for Roadway Work Zones" model that includes a novel data fusion mechanism and a new heterogeneous graph aggregation methodology to accommodate work zone information in spatio-temporal dependencies among traffic states. The model is evaluated on two data sets that capture traffic flows in the presence of work zones in the Commonwealth of Virginia. Extensive comparative evaluation and ablation studies show that the proposed model can capture complex and nonlinear spatio-temporal relationships across a transportation corridor, outperforming baseline models, particularly when predicting traffic flow during a workzone event.

Motion Memory: Leveraging Past Experiences to Accelerate Future Motion Planning

When facing a new motion-planning problem, most motion planners solve it from scratch, e.g., via sampling and exploration or starting optimization from a straight-line path. However, most motion planners have to experience a variety of planning problems throughout their lifetimes, which are yet to be leveraged for future planning. In this paper, we present a simple but efficient method called Motion Memory, which allows different motion planners to accelerate future planning using past experiences. Treating existing motion planners as either a closed or open box, we present a variety of ways that Motion Memory can contribute to reduce the planning time when facing a new planning problem. We provide extensive experiment results with three different motion planners on three classes of planning problems with over 30,000 problem instances and show that planning speed can be significantly reduced by up to 89% with the proposed Motion Memory technique and with increasing past planning experiences.

Traffic Flow Forecasting with Maintenance Downtime via Multi-Channel Attention-Based Spatio-Temporal Graph Convolutional Networks

Forecasting traffic flows is a central task in intelligent transportation system management. Graph structures have shown promise as a modeling framework, with recent advances in spatio-temporal modeling via graph convolution neural networks, improving the performance or extending the prediction horizon on traffic flows. However, a key shortcoming of state-of-the-art methods is their inability to take into account information of various modalities, for instance the impact of maintenance downtime on traffic flows. This is the issue we address in this paper. Specifically, we propose a novel model to predict traffic speed under the impact of construction work. The model is based on the powerful attention-based spatio-temporal graph convolution architecture but utilizes various channels to integrate different sources of information, explicitly builds spatio-temporal dependencies among traffic states, captures the relationships between heterogeneous roadway networks, and then predicts changes in traffic flow resulting from maintenance downtime events. The model is evaluated on two benchmark datasets and a novel dataset we have collected over the bustling Tyson's corner region in Northern Virginia. Extensive comparative experiments and ablation studies show that the proposed model can capture complex and nonlinear spatio-temporal relationships across a transportation corridor, outperforming baseline models.