Efficient Adaptive Spline-Based Path Planning for In-Space Servicing, Assembly, and Manufacturing Applications

Autonomous robotic systems operating in cluttered and partially observed environments require trajectory generation methods that produce smooth and dynamically feasible motion while reacting to locally sensed obstacles. This requirement is especially pronounced for free-flyer and in-space servicing, assembly, and manufacturing (ISAM) platforms, where onboard sensing is sparse, global environmental information is unavailable, and communication or computational resources are constrained. In such settings, motion plans must be updated online using incomplete and rapidly changing local observations, while avoiding excessive replanning that can lead to oscillatory or unstable behavior. Many existing approaches either rely on dense optimization over extended horizons, which is difficult to sustain under strict real-time constraints, or prioritize reactive responsiveness at the expense of high-order smoothness and dynamic consistency, leading to trajectories that are difficult to execute reliably. This dissertation introduces the Waypoint Adaptive Spline Planner (WASP), a generic local trajectory generation framework that formulates motion using minimal, event-driven spline representation. WASP constructs motion primitives using a single spline defined between the robot's instantaneous pose and an adaptive intermediate waypoint, which acts as the sole decision variable in the planning loop. Waypoint updates are triggered only when locally sensed environmental changes satisfy explicit update conditions, enabling obstacle avoidance while suppressing unnecessary replanning. This framework is evaluated through a representative instantiation involving planar robot dynamics, local LiDAR perception, a quintic polynomial spline, and example waypoint adaptation and smoothing strategies. Simulation studies are conducted in cluttered environments with both static and dynamic obstacles under limited sensing conditions. The evaluation focuses on the qualitative and quantitative behavior of the generated trajectories, including smoothness, continuity, obstacle clearance, and replanning stability during online execution. Results show that the proposed framework yields smooth, dynamically feasible local trajectories that adapt to environmental changes without inducing oscillatory or erratic replanning behavior, while relying solely on locally available perception. These findings suggest that minimal, event-driven spline frameworks such as WASP offer a practical and extensible alternative to dense optimization-based planners and purely reactive approaches for local trajectory generation in constrained autonomous systems.