A. Dhyani
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Driven by the need to improve safety, the maritime industry is undergoing a transformative change with the development of autonomous surface vessels, which can aim to reduce the likelihood of accidents by incorporating risk awareness directly into motion planning and control to account for uncertainties in maritime operations. However, existing literature does not consider risk-aware control capabilities that can enable fail-safe actions. To address this limitation, this work proposes a Multi-Trajectory (MT) Risk-Aware Model Predictive Control (MPC) scheme designed to simultaneously optimize two distinct trajectories: a mission-driven nominal trajectory and a fail-safe contingency trajectory. The fail-safe trajectory is computed with a risk cost term in the MPC objective function to guide the vessel towards minimum-risk conditions. In addition, a terminal velocity cost term ensures that the velocity of the vessel can be reduced at each optimization step, to bring it to a near-stop at the end of the fail-safe trajectory. By enforcing a shared initial control input constraint, the system reduces the possibility that the nominal trajectory leads to a state from which an escape is impossible. A supervisory system monitors these trajectories, evaluating them for predicted grounding, the physical capability to bring the vessel to a stop and if the risk is predicted to increase along the nominal trajectory. If a hazardous situation which requires a fail-safe action is detected, defined as a predicted grounding event or a loss of stopping capability while risk is predicted to increase, the supervisory system activates the fail-safe action. The fail-safe action is activated by using the binary variable (B), which accordingly modifies the MT MPC objective function. Verification is performed in a simulation environment by comparing the MT scheme against a Single-Trajectory (ST) MPC scheme during no-fault and stuck rudder and loss of effectiveness fault scenarios in conjunction with drifting forces due to wind. Results demonstrate that the MT scheme is able to able to reduce the likelihood of grounding compared to the ST scheme. Finally, this enhanced safety imposes no penalty on travel time, energy consumption, or path-tracking accuracy during scenarios that do not require a fail-safe action. The only significant trade-off is a two- to three-fold increase in average solver time compared to the ST scheme.
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Driven by the need to improve safety, the maritime industry is undergoing a transformative change with the development of autonomous surface vessels, which can aim to reduce the likelihood of accidents by incorporating risk awareness directly into motion planning and control to account for uncertainties in maritime operations. However, existing literature does not consider risk-aware control capabilities that can enable fail-safe actions. To address this limitation, this work proposes a Multi-Trajectory (MT) Risk-Aware Model Predictive Control (MPC) scheme designed to simultaneously optimize two distinct trajectories: a mission-driven nominal trajectory and a fail-safe contingency trajectory. The fail-safe trajectory is computed with a risk cost term in the MPC objective function to guide the vessel towards minimum-risk conditions. In addition, a terminal velocity cost term ensures that the velocity of the vessel can be reduced at each optimization step, to bring it to a near-stop at the end of the fail-safe trajectory. By enforcing a shared initial control input constraint, the system reduces the possibility that the nominal trajectory leads to a state from which an escape is impossible. A supervisory system monitors these trajectories, evaluating them for predicted grounding, the physical capability to bring the vessel to a stop and if the risk is predicted to increase along the nominal trajectory. If a hazardous situation which requires a fail-safe action is detected, defined as a predicted grounding event or a loss of stopping capability while risk is predicted to increase, the supervisory system activates the fail-safe action. The fail-safe action is activated by using the binary variable (B), which accordingly modifies the MT MPC objective function. Verification is performed in a simulation environment by comparing the MT scheme against a Single-Trajectory (ST) MPC scheme during no-fault and stuck rudder and loss of effectiveness fault scenarios in conjunction with drifting forces due to wind. Results demonstrate that the MT scheme is able to able to reduce the likelihood of grounding compared to the ST scheme. Finally, this enhanced safety imposes no penalty on travel time, energy consumption, or path-tracking accuracy during scenarios that do not require a fail-safe action. The only significant trade-off is a two- to three-fold increase in average solver time compared to the ST scheme.