Spline Trajectory Tracking and Obstacle Avoidance for Mobile Agents via Convex Optimization
<|reference_start|>Spline Trajectory Tracking and Obstacle Avoidance for Mobile Agents via Convex Optimization: We propose an output feedback control-based motion planning technique for agents to enable them to converge to a specified polynomial trajectory while imposing a set of safety constraints on our controller to avoid collisions within the free configuration space (polygonal environment). To achieve this, we 1) decompose our polygonal environment into different overlapping cells 2) write out our polynomial trajectories as the output of a reference dynamical system with given initial conditions 3) formulate convergence and safety constraints as Linear Matrix Inequalities (LMIs) on our controller using Control Lyapunov Functions (CLFs) and Control Barrier Functions (CBFs) and 4) solve a semi-definite programming (SDP) problem with convergence and safety constraints imposed to synthesize a controller for each convex cell. Extensive simulations are included to test our motion planning method under different initial conditions and different reference trajectories. The synthesized controller is robust to changes in initial conditions and is always safe relative to the boundaries of the polygonal environment.<|reference_end|>
@article{dickson2024spline,
    title={Spline Trajectory Tracking and Obstacle Avoidance for Mobile Agents via
  Convex Optimization},
    author={Akua Dickson, Christos G. Cassandras and Roberto Tron},
    journal={arXiv preprint arXiv:2403.16900},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.16900},
    primaryClass={eess.SY cs.SY}
}