Some Modifications to Our End-to-End UAV Planner

• URL: http://arxiv.org/abs/2608.15741v1
• Date: Sun, 16 Aug 2026 13:39:46 GMT
• Title: Some Modifications to Our End-to-End UAV Planner
• Abstract summary: The one-stage planner maps a single depth image and the robot state directly to a set of candidate trajectories.<n>We show that YOPO's multi-modal prediction span to distinct homotopy classes, treating each motion as a trajectory that confines the trajectory to a feasible - without explicit safe-building or construction penalties.
• Score: 2.3190673374277524
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The one-stage planner YOPO maps a single depth image and the robot state directly to a set of candidate trajectories, trained by backpropagating through differentiable trajectory costs. This yields dense, geometrically informative supervision, but inherits the pathologies of soft-constrained optimization: the safety cost competes with the smoothness and goal-reaching terms, is non-convex across homotopy classes, and the single-piece polynomial is limited in expressiveness. In this report, we summarize several effective modifications. We adopt a two-piece MINCO parameterization, trading time for smoothness without altering the trajectory's spatial profile. We further lift YOPO's multi-modal prediction to span distinct homotopy classes, treating each motion primitive as a homotopy anchor that confines the trajectory to a feasible basin - without explicit safe-flight-corridor construction or front-end search. For dynamic feasibility, we impose barrier penalties on velocity and acceleration together with a curvature-dependent speed limit whose gradient acts only on the velocity, producing an adaptive-speed behavior that decelerates in cluttered regions or sharp turns. We replace score regression with a ranking loss, preventing small score errors from reordering the candidate set. These yield richer trajectory representations, safer obstacle avoidance, and more direct flight paths.

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