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

Autonomous robots increasingly support Search and Rescue (SaR), yet victim trust in these robots remains largely unaddressed in simulation, where robots typically neither estimate nor respond to it. This paper implements a transparent, bidirectional trust model in a SaR simulation with cognitively realistic victims based on the GT-BDI framework. Victims maintain a true internal trust state driven by observable signals–proximity and perceived danger–while the robot estimates this trust from proximity and switches between approaching the victim to build trust and leading it to the exit. An ablation study over 100 matched seeds compared trust-aware against trust-blind robots. Trust-aware behavior saved significantly more victims (+0.74 on average, p < 0.001), reducing victims stranded at timeout rather than lowering mortality. This gain stemmed not from higher trust, but from better robot positioning. These results suggest that how a robot responds to trust can meaningfully improve evacuation outcomes, offering a practical and interpretable path to better robot-guided rescue. ... Read more

Autonomous search and rescue (SaR) requires an agent to explore an unknown, partially observable and hazardous environment quickly without harming itself or the victims it is looking for. Reinforcement learning (RL) produces adaptive policies but offers no safety guarantee, while model-predictive control (MPC) enforces model-based safety constraints but is conservative and slow. We combine the two, shielding a learned policy under an MPC backup and ask when the result is preferable to either on a partially observable grid-world SaR task. We first build a pure-RL explorer and show its coverage hits a ceiling near 70% success, a limit that is due to the method rather than to tuning. We then wrap it in a slip-aware safety shield that certifies every action's reachable "slip tube" against the agent's belief and defers to a model-predictive backup when the policy proposes an unsafe move. Across 250 held-out hazardous maps, the shield makes pure RL deployable by eliminating its collisions in evaluation, reducing it from 117.7 to 0 and cutting damage around 15x while still retaining 93% of its success (from 0.43 to 0.40), at 53 ms per decision and beating the slower tube-based MPC on coverage, victims and speed. Only the exhaustive MPC is more effective, but it runs at approximately 5x the latency and does not scale. We thus position the shield not as a universal winner, but as the best choice when collisions cannot be tolerated and decisions must stay fast. ... Read more

Effective autonomous search and rescue (SaR) requires safe and efficient traversal of hazardous regions. While model predictive control (MPC) offers a solid framework for controlling an autonomous agent, the robust tube MPC (TMPC) approach sacrifices too much performance, especially where disturbance probabilities vary spatially. Current approaches to address this problem explore varying tube size in scenarios with perfect information, but none test whether local perception of the environment can be used to dynamically scale tube size to reduce conservativeness adaptively. A state-dependent dynamic TMPC (SDD-TMPC) formulation is proposed, scaling tube dynamically on perceived disturbance risk and confidence. Three controllers: MPC, TMPC, SDD-TMPC, are tested over 250 seeded SaR simulations, with additional runs introducing noise into perceived disturbance risk to assess the formulation's robustness. SDD-TMPC is shown to substantially outperform TMPC (odds-ratio (OR)=40.0) while maintaining 0 wall/victim collisions, operating in TMPC mode only 32.3\% of the time. Robustness and performance advantage are retained under perceived noise of magnitudes up to 0.2, however, 0.4 is identified as the boundary where this no longer holds. Overall, this suggests that perception-based tube scaling is a promising direction for autonomous agents. ... Read more

Task allocation in multi-robot Search and Rescue (SaR) is challenging: the environment is unknown, time-critical, and too dynamic for continuous centralized planning. This paper asks how a lightweight supervisory framework can dynamically allocate sub-tasks within a multi-robot team to balance exploration and victim retrieval without incurring prohibitive computational costs. We propose an event-driven, auction-based supervisory layer that intervenes only on discrete triggers; spatial conflicts, agent stagnation, and victim discovery, leaving robots to operate autonomously otherwise. Compared to a purely decentralized baseline across 216 simulated missions, the supervisor raises the victim discovery success rate from 50.5% to 91.7%, locates all victims roughly 24% faster without meaningfully increasing computation time. When extended to a heterogeneous fleet including a dedicated rescue agent, these exploration gains persist, but the increased rate of victim discovery outpaces single-agent rescue capacity, revealing a trade-off between exploration efficiency and victim retrieval throughput that the current allocation mechanism does not yet resolve. ... Read more