Summary
This paper tackles the problem of how flying drones (UAVs) and ground robots (UGVs) can work together to search areas with unknown dangers. The authors designed a method that makes the drones use their cameras smartly to explore and spot targets while guiding the ground robots to safer paths that avoid threats. They made this coordination fully decentralized, so each robot plans based on what it knows and shares information to improve their teamwork. Their approach updates plans as new information comes in and balances exploring unknown areas with staying safe. They tested their method in computer simulations showing that multiple flying and ground robots can collaborate efficiently.
What this means in practice
- •For robot operations teams: Coordinate teams of drones and ground robots to explore risky areas while dynamically avoiding threats based on shared sensor data.
- •For emergency response planners: Plan autonomous robot fleets to survey disaster zones with uncertain hazards, improving situational awareness with adaptable sensing and navigation.
Abstract
This paper considers coordinating a heterogeneous team of unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs) traversing a domain containing an unknown number of potentially hazardous targets. The UAVs provide aerial sensing using onboard optical cameras, adapting their trajectories to ground routes and target information. We formulate the aerial search problem using ergodic control, which distributes sensing effort according to a spatial information density to balance exploration and target localization. The formulation incorporates dynamic camera field-of-view footprints and pose-dependent probabilistic detection models. Angles-only measurements are processed through Bayesian inference and marginalization over data-association hypotheses to obtain a Poisson multi-Bernoulli (PMB) approximation of the target posterior, representing both detected and undetected targets. This belief directs aerial sensing toward unexplored regions along the ground routes and viewpoints that improve target localization. Ground planning combines chance-constrained target avoidance, enforced through confidence sets, with a void-probability objective that favors routes unlikely to contain threats. We solve each team's optimization problem in a fully decentralized manner using a nested SCvx*--C-ADMM scheme, with outer sequential convex programming iterations and inner distributed consensus optimization, accounting for nonlinearities, nonseparable objectives, and continuous-time inter-agent separation constraints. A closed-loop scheme updates both fleets' trajectories as information becomes available and permits ground motion when the remaining route risk falls below a prescribed threshold. Numerical case studies examine sensing geometry and target uncertainty, compare decentralized and centralized solvers, and demonstrate full closed-loop collaboration between multiple UAVs and UGVs.