FK
F.O.B. Keerberg
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Perception-Driven Tube Scaling for State-Dependent Dynamic Tube-Based MPC
Application to disturbance-aware search and rescue scenarios
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.
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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.