Control, Planning, and Decision-Making

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[momala]

Autonomous systems across ground, airborne, marine, and space domains share common architectural layers—perception, decision-making, and control—but diverge significantly due to differences in dynamics, environmental uncertainty, and safety constraints. Ground systems (e.g., automotive and mobile robots) operate in highly structured yet cluttered environments with frequent interactions with humans and infrastructure. Their control algorithms emphasize real-time responsiveness, friction-limited dynamics, and precise low-speed maneuvering (e.g., PID/MPC controllers tuned for tire-road interaction). Decision-making often relies on rule-based systems augmented with probabilistic reasoning to handle traffic laws and agent interactions, while path planning combines graph-based methods (A*, D*) for global routing with sampling-based or optimization-based planners (RRT*, MPC) for local obstacle avoidance under tight latency constraints.

In contrast, airborne systems (e.g., UAVs, commercial aircraft) operate in a less cluttered but highly dynamic 3D environment with stricter stability and safety requirements. Control systems are typically layered with inner-loop stability augmentation (often linearized or gain-scheduled controllers) and outer-loop guidance laws. Decision-making must account for airspace regulations, weather, and energy constraints, often using hybrid systems and formal methods for safety assurance. Path planning extends into continuous 3D space with trajectory optimization under aerodynamic and kinematic constraints. Marine systems face slower dynamics but significant environmental disturbances (currents, waves, wind) and limited sensing fidelity. Their control approaches often emphasize robustness and disturbance rejection (e.g., adaptive or nonlinear control), while decision-making must handle sparse infrastructure and long-duration autonomy. Path planning may prioritize energy efficiency and waypoint-based navigation over reactive obstacle avoidance, except in congested waterways.

Space systems operate in the most extreme and least forgiving environment, where delays, limited actuation, and orbital mechanics dominate. Control algorithms are heavily model-based, often derived from first principles (e.g., astrodynamics), with limited opportunity for real-time correction. Decision-making is typically conservative and highly validated, with increasing use of onboard autonomy for deep-space missions where communication delays preclude human-in-the-loop control. Path planning is fundamentally different—focused on trajectory design using orbital transfers, optimization under gravitational constraints, and fuel minimization rather than obstacle avoidance. Across these domains, the progression from ground to space reflects a shift from reactive, data-driven approaches toward predictive, model-based, and highly verified methods, driven by increasing consequences of failure and decreasing opportunities for real-time human intervention.