753 research outputs found
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
FASTER: Fast and Safe Trajectory Planner for Flights in Unknown Environments
High-speed trajectory planning through unknown environments requires
algorithmic techniques that enable fast reaction times while maintaining safety
as new information about the operating environment is obtained. The requirement
of computational tractability typically leads to optimization problems that do
not include the obstacle constraints (collision checks are done on the
solutions) or use a convex decomposition of the free space and then impose an
ad-hoc time allocation scheme for each interval of the trajectory. Moreover,
safety guarantees are usually obtained by having a local planner that plans a
trajectory with a final "stop" condition in the free-known space. However,
these two decisions typically lead to slow and conservative trajectories. We
propose FASTER (Fast and Safe Trajectory Planner) to overcome these issues.
FASTER obtains high-speed trajectories by enabling the local planner to
optimize in both the free-known and unknown spaces. Safety guarantees are
ensured by always having a feasible, safe back-up trajectory in the free-known
space at the start of each replanning step. Furthermore, we present a Mixed
Integer Quadratic Program formulation in which the solver can choose the
trajectory interval allocation, and where a time allocation heuristic is
computed efficiently using the result of the previous replanning iteration.
This proposed algorithm is tested extensively both in simulation and in real
hardware, showing agile flights in unknown cluttered environments with
velocities up to 3.6 m/s.Comment: IROS 201
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