Robust Coordinated Hybrid Source Seeking With Obstacle Avoidance in Multivehicle Autonomous Systems

In multi-vehicle autonomous systems that operate under unknown or adversarial environments it is a challenging task to simultaneously achieve source seeking and obstacle avoidance. Indeed, even for single-vehicle systems, smooth time-invariant feedback regulators based on navigation or barrier functions have been shown to be highly susceptible to arbitrarily small jamming signals that are able to locally stabilize spurious equilibria or to induce instability in the closed-loop system. When the location of the source is further unknown, adaptive smooth source seeking dynamics based on averaging theory may suffer from similar limitations. In this paper, we address this challenge by introducing a class of novel distributed hybrid model-free adaptive controllers that achieve robust source seeking and obstacle avoidance in multi-vehicle autonomous systems with general nonlinear dynamics stabilizable by hybrid feedback. The hybrid source seeking law switches between a family of cooperative gradient-free controllers, derived from potential fields that satisfy mild invexity assumptions. The stability and robustness properties of the closed-loop system are analyzed using Ly