Algorithm 1: Team-based convex optimization algorithm for robot deployment

Input: Initial configuration ($q_s$), Goal configuration ($q_g$), and Required cost $c_{ij}$, $d_{ij}$ Output: $Path$ (or sequence of nodes from $q_s$ to $q_g$), Robots’ final locations ($L_{g}oal$) Begin 1: Classify m robots into k teams ${\mathcal{T}}_i$, $i\in [1,k]$; 2: Approximate teams ${\mathcal{T}}_i$ to k circles at center of ${\mathcal{T}}_j(x_j,y_j)$, $j\in [1,k]$; 3: Solve the convex optimization model (Equation (8)) having $C_j(x_j,y_j)$, $j\in [1,k]$; 4: Obtain relative locations of teams. New circle locations ${\tilde{C}}_j({\tilde{x}}_j,{\tilde{y}}_j)$, $j\in [1,k]$; 5: Apply SOMNN to subtask allocation to reach g goals $\mathbf{G}({\mathbf{x}}_{\mathbf{l}},{\mathbf{y}}_{\mathbf{l}})$, $l\in [1,g]$; End