Robots are increasingly used in search-and-rescue (SaR) to perform dangerous tasks in place of humans, and to free human rescue workers to focus on other tasks. These robots require effective and reliable control methods to ensure that they perform as well as the humans they replace. Model predictive control (MPC) is an optimisation-based method which innately handles hard constraints such as not colliding with victims, and has been well-studied in SaR contexts. Tube-based MPC (TMPC) is an extension of MPC which is robust to the bounded disturbances inevitable in real-life SaR, but due to its fixed tube is often overly conservative. Surma and Jamshidnejad propose state-dependant dynamic TMPC (SDD-TMPC), which adjusts tube size online based on an estimate of the current maximum disturbance, allowing for reduced conservativeness when possible disturbances are small. We develop a grid-based SaR simulation to evaluate how SDD-TMPC's adaptive tube affects conservativeness and the safety-performance trade-off in a dynamic, partially observable environment. SDD-TMPC, MPC, and TMPC are compared over many simulated search missions, where the agent is tasked with finding all victims in an environment. The results show that while retaining the empirical safety of TMPC, SDD-TMPC behaves less conservatively and outperforms TMPC in mission feasibility and duration. ... Read more