Solving Geodesic Equations with Composite Bernstein Polynomials for Trajectory Planning

TL;DR

This paper introduces a trajectory planning method using composite Bernstein polynomials within a symbolic optimization framework, enabling smooth, collision-free paths in complex environments for various autonomous systems.

Contribution

It presents a novel approach combining composite Bernstein polynomials with symbolic optimization for continuous, efficient, and safe trajectory planning in multi-dimensional spaces.

Findings

Efficient generation of smooth, collision-free paths in obstacle-rich scenarios.

Applicable to diverse environments including ground, aerial, underwater, and space.

Supports high numerical efficiency for spacecraft trajectory planning.

Abstract

This work presents a trajectory planning method based on composite Bernstein polynomials for autonomous systems navigating complex environments. The method is implemented in a symbolic optimization framework that enables continuous paths and precise control over trajectory shape. Trajectories are planned over a cost surface that encodes obstacles as continuous fields rather than discrete boundaries. Regions near obstacles are assigned higher costs, naturally encouraging the trajectory to maintain a safe distance while still allowing efficient routing through constrained spaces. The use of composite Bernstein polynomials preserves continuity while enabling fine control over local curvature to satisfy geodesic constraints. The symbolic representation supports exact derivatives, improving optimization efficiency. The method applies to both two- and three-dimensional environments and is suitable for ground, aerial, underwater, and space systems. In spacecraft trajectory planning, for example, it enables the generation of continuous, dynamically feasible trajectories with high numerical efficiency, making it well suited for orbital maneuvers, rendezvous and proximity operations, cluttered gravitational environments, and planetary exploration missions with limited onboard computational resources. Demonstrations show that the approach efficiently generates smooth, collision-free paths in scenarios with multiple obstacles, maintaining clearance without extensive sampling or post-processing. The optimization incorporates three constraint types: (1) a Gaussian surface inequality enforcing minimum obstacle clearance; (2) geodesic equations guiding the path along locally efficient directions on the cost surface; and (3) boundary constraints enforcing fixed start and end conditions. The method can serve as a standalone planner or as an initializer for more complex motion planning problems.