Drawing inspiration from Maslow's “hierarchy of needs”, this paper develops a real-time control synthesis framework for robots to address hierarchical, complex objectives, recognizing that their behaviors are inherently driven by underlying needs. Each need is encoded by the zero-superlevel set of a control barrier function (CBF), which can be time-varying, and all the needs at the same level in a hierarchy are composed into a single one through Boolean compositions of the corresponding CBFs. The effectiveness of the proposed framework is demonstrated through a hypothetical interstellar exploration mission using laboratory robots, and novel results on nonsmooth CBF and time-varying CBF are derived.

Inspired by common features found in collaborative behaviors in nature, we investigate a general collaborative pursuit framework enabling heterogeneous multi-robot systems to adapt to dynamic environments and diverse tasks. A class of augmented Fokker-Planck equations is formulated to characterize dynamic environmental conditions, and the resulting time-varying density functions drive a novel coverage-based controller, with provable stability properties, for the participating robots to perform tasks in real time. The developed framework is decentralized and incorporates heterogeneity among different robots in task suitability, relative performance in a specific task, and safe operating regions. To demonstrate its adaptivity and effectiveness, the framework is implemented across four experimental applications ranging from multi-robot coordination to collaboration, namely forest firefighting, pursuit-evasion, monitoring of various environmental phenomena, and phoretic interactions.

A coverage-based collaborative control strategy is developed in this paper for a multi-robot system with heterogeneous effective sensing ranges and safe operation zones to simultaneously estimate the states of and follow multiple targets governed by stochastic dynamics. Multiplicatively weighted Voronoi diagrams are exploited to define each robot's dominant region considering its limited sensing radius. The asymptotically stable system dynamics enabling the heterogeneous multi-robot system to (locally) optimally cover the time-varying probability density distributions that characterize the uncertainties of the targets' positions is derived, and minimally perturbed by control barrier functions designed to ensure that each robot moves within its safe operation zone in a collision-free manner. Specific target dynamics and measurement models are chosen in the experiment, whose results demonstrate the effectiveness of the proposed dynamic multi-target tracking approach.