Generalized Backup Plan-Constrained Model Predictive Control

The increasing use of unmanned aerial vehicles for long-duration missions has heightened safety concerns, motivating the introduction of backup plan safety (BPS) for autonomous vehicles. BPS refers to the capability of executing an alternative mission in the event of primary mission abortion. In path planning, BPS integrates alternative destinations, serving as backup safety landing sites, into the decision-making process, maximizing the feasibility of reaching potential destinations. Consequently, the trajectory is adjusted to redirect toward alternative destinations, minimizing the time required to reach a safe landing site if the primary site becomes inaccessible. To address BPS, we formulate the Feasibility Maximization Problem (FMP) based on multi-objective model predictive control (MPC), balancing the costs of multiple missions. The FMP enhances the evaluation of objectives, corresponding to each mission’s cost, by incorporating control input horizons for all alternative missions alongside the horizon for the primary mission. The proposed Generalized-Backup-Plan-constrained MPC (GBP MPC) generates control inputs by solving the FMP and minimizing a weighted cost that reflects tradeoffs between the primary and alternative missions. To guarantee stability, GBP MPC departs from the step-by-step decrease in weighted cost required in Backup Plan constrained MPC and instead generalizes the decrease to a parameterized multistep period.