SteadyPath: Ensuring Stability in Autonomous Industrial Maneuvering
Keywords:
Automated guided vehicle (AGV), Autonomous forklift, Industrial robotics, Model predictive control (MPC), Motion planning, Obstacle avoidance, Terminal-state constraints, Vehicle stabilityAbstract
Autonomous industrial vehicles, such as automated guided vehicles (AGVs) and autonomous forklifts, operate in confined, dynamic environments where collision-free motion alone is not a sufficient safety criterion. A maneuver that successfully avoids an obstacle can still leave the vehicle in an unstable or uncontrollable configuration, jeopardizing the next stage of motion. This article presents SteadyPath, a stability-focused navigation framework that integrates environment state acquisition, decision-making, sampling/graph-based motion planning, and terminal-constrained Model Predictive Control (MPC) within a closed feedback loop. Unlike conventional planners that only certify path feasibility, SteadyPath explicitly constrains the terminal state of the prediction horizon to a safe, controllable region, ensuring that the vehicle exits every avoidance maneuver in a state from which safe continuation is supported. The framework is developed from an expanded survey that combines the original five motivating studies with recent IEEE journal literature on autonomous-mobile-robot path planning, terminally constrained safe navigation, smooth trajectory optimization, and robust MPC. The proposed architecture, mathematical formulation, intended simulation environment, test scenarios, and stability-oriented evaluation metrics are described. The study is presented as a design and methodology contribution; quantitative performance results are not included and are reserved for future experimental validation.
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