Accelerating Trajectory Generation for Quadrotors Using Transformers

In this work, we address the problem of computation time for trajectory generation in quadrotors. Most trajectory generation methods for waypoint navigation of quadrotors, for example minimum snap/jerk and minimum-time, are structured as bi-level optimizations. The first level involves allocating time across all input waypoints and the second step is to minimize the snap/jerk of the trajectory under that time allocation. Such an optimization can be computationally expensive to solve. In our approach we treat trajectory generation as a supervised learning problem between a sequential set of inputs and outputs. We adapt a transformer model to learn the optimal time allocations for a given set of input waypoints, thus making it into a single step optimization. We demonstrate the performance of the transformer model by training it to predict the time allocations for a minimum snap trajectory generator. The trained transformer model is able to predict accurate time allocations with fewer data samples and smaller model size, compared to a feedforward network (FFN), demonstrating that it is able to model the sequential nature of the waypoint navigation problem.Comment: Accepted at L4DC 202

Cross-utterance ASR Rescoring with Graph-based Label Propagation

We propose a novel approach for ASR N-best hypothesis rescoring with graph-based label propagation by leveraging cross-utterance acoustic similarity. In contrast to conventional neural language model (LM) based ASR rescoring/reranking models, our approach focuses on acoustic information and conducts the rescoring collaboratively among utterances, instead of individually. Experiments on the VCTK dataset demonstrate that our approach consistently improves ASR performance, as well as fairness across speaker groups with different accents. Our approach provides a low-cost solution for mitigating the majoritarian bias of ASR systems, without the need to train new domain- or accent-specific models.Comment: To appear in IEEE ICASSP 202

A Novel Solution for a Variable Delivery Flow External Gear Pump for Low Pressure Applications

This study describes an innovative design concept to enable dynamic variable delivery flow control of external spur gear pumps for low and medium pressure applications. The basic principle used to obtain flow variation relies on a variable timing concept previously demonstrated in literature. This principle permits to vary the flow within a certain range, without introducing additional sources of power loss. Previous work proved the applicability of the proposed concept in a pressure compensated design of external gear pump for high pressure applications. This concept took advantage of the pressure differential acting on the slider, which is the internal element able to perform the flow regulation. In this paper, a solution that permits to achieve balance of the pressure forces acting on the slider is proposed. This solution permits to reduce the actuation forces, thus enabling the usage of multiple actuation technologies. An electronic control system is implemented in the prototype to achieve accurate delivery flow control. The proposed solution is cost effective, it consists of a limited number of parts, and it is suitable for pumps without pressure compensation, i.e. for low or intermediate pressures. This work details the aspects of the pump design, which was performed by using a multi-objective algorithm that maximizes the flow operating range and at the same time the pump. The optimum design could achieve a flow variation of about 31% in simulations and this was also demonstrated in actual experiments on a prototype realized within this research. The proposed design can impact several of the current applications of external gear pumps, introducing the additional “flow on demand” capability