https://robolabor.ee/homelab/ 2026-05-15T21:54:34+00:00 https://robolabor.ee/homelab/ https://robolabor.ee/homelab/lib/tpl/arctic/images/favicon.ico text/html 2026-04-29T16:52:02+00:00 Anonymous (anonymous@undisclosed.example.com) https://robolabor.ee/homelab/en/safeav/ctrl/algorithms?rev=1777470722&do=diff Motion Planning and Behavioural Algorithms While decision-making algorithms determine what high-level goal the autonomous vehicle should pursue (e.g., reach destination, avoid obstacle, follow lane), motion planning and behavioral algorithms translate these goals into specific, executable paths and maneuvers within the dynamic and complex environment. This sub-chapter delves into these critical components, exploring how they generate safe, efficient, and predictable trajectories and behaviors f… text/html 2026-04-24T09:45:00+00:00 Anonymous (anonymous@undisclosed.example.com) https://robolabor.ee/homelab/en/safeav/ctrl/sim?rev=1777013100&do=diff Simulation & Formal Methods Why Simulation Needs Formalism Simulation is indispensable in autonomous-vehicle validation because it lets us probe safety-critical behavior without exposing the public to risk, but simulation alone is only as persuasive as its predictive value. A simulator that cannot anticipate how the real system behaves—because of poor modeling, missing variability, or unmeasured assumptions—does not provide credible evidence for a safety case. This is why we pair simulation wi… text/html 2026-04-29T16:45:24+00:00 Anonymous (anonymous@undisclosed.example.com) https://robolabor.ee/homelab/en/safeav/ctrl/strategies?rev=1777470324&do=diff Classical and AI-Based Control Strategies Classical Control Strategies Classical control strategies form the bedrock of modern vehicle control systems. These methods rely on mathematical models of the vehicle dynamics and well-established principles from control theory, primarily developed in the 20th century. Their strength lies in their mathematical rigor, transparency, and well-understood stability properties. text/html 2026-04-24T09:59:56+00:00 Anonymous (anonymous@undisclosed.example.com) https://robolabor.ee/homelab/en/safeav/ctrl/summary?rev=1777013996&do=diff Summary This chapter develops a comprehensive view of how control, decision-making, and motion planning form the core of autonomous system behavior, and how these elements vary across domains and implementation paradigms. It begins by contrasting classical control methods text/html 2026-04-23T11:27:20+00:00 Anonymous (anonymous@undisclosed.example.com) https://robolabor.ee/homelab/en/safeav/ctrl/testing?rev=1776932840&do=diff Physical Testing Physical testing infrastructures across ground, airborne, marine, and space systems reflect a progression from high-access, repeatable environments to extremely constrained, high-cost, and often non-replicable conditions. Each domain builds specialized facilities to bridge the gap between simulation and real-world deployment, with increasing emphasis on safety, controllability, and observability of complex system interactions. text/html 2026-04-29T17:01:28+00:00 Anonymous (anonymous@undisclosed.example.com) https://robolabor.ee/homelab/en/safeav/ctrl/vctrl?rev=1777471288&do=diff Validation of Control & Planning Principles and Scope Planning and control are where intent becomes motion. A planning stack selects a feasible, safety-aware trajectory under evolving constraints; the control stack turns that trajectory into actuation while respecting vehicle dynamics and delays. Validating these layers is therefore about much more than unit tests: it is about demonstrating, with evidence, that the combined decision–execution loop behaves safely and predictably across the inte…