Public articles linked to the same research event.
arXiv The work introduces PLUTO, an interactive trajectory design framework that embeds agentic AI coding tools within a sequential convex programming (SCP) architecture, mapping natural-language rendezvous mission requirements into structured mathematical constraints that pass through an auto-convexification pipeline into executable optimal control formulations; across 50 natural-language rendezvous mission prompts spanning 10 constraint types, PLUTO generated a trajectory for every prompt and 94% of the resulting trajectories satisfied both numerical constraint checks and semantic consistency with the original mission intent.
The work introduces PLUTO, an interactive trajectory design framework that embeds agentic AI coding tools within a sequential convex programming (SCP) architecture, mapping natural-language rendezvous mission requirements into structured mathematical constraints that pass through an auto-convexification pipeline into executable optimal control formulations; across 50 natural-language rendezvous mission prompts spanning 10 constraint types, PLUTO generated a trajectory for every prompt and 94% of the resulting trajectories satisfied both numerical constraint checks and semantic consistency with the original mission intent.
The work introduces PLUTO, an interactive trajectory design framework that embeds agentic AI coding tools within a sequential convex programming (SCP) architecture, mapping natural-language rendezvous mission requirements into structured mathematical constraints that pass through an auto-convexification pipeline into executable optimal control formulations; across 50 natural-language rendezvous mission prompts spanning 10 constraint types, PLUTO generated a trajectory for every prompt and 94% of the resulting trajectories satisfied both numerical constraint checks and semantic consistency with the original mission intent.
The work introduces PLUTO, an interactive trajectory design framework that embeds agentic AI coding tools within a sequential convex programming (SCP) architecture, mapping natural-language rendezvous mission requirements into structured mathematical constraints that pass through an auto-convexification pipeline into executable optimal control formulations; across 50 natural-language rendezvous mission prompts spanning 10 constraint types, PLUTO generated a trajectory for every prompt and 94% of the resulting trajectories satisfied both numerical constraint checks and semantic consistency with the original mission intent.