  In time-sensitive and dynamic missions, multi-UAV teams must respond quickly
to new information and objectives. This paper presents a dynamic decentralized
task allocation algorithm for allocating new tasks that appear online during
the solving of the task allocation problem. Our algorithm extends the
Consensus-Based Bundle Algorithm (CBBA), a decentralized task allocation
algorithm, allowing for the fast allocation of new tasks without a full
reallocation of existing tasks. CBBA with Partial Replanning (CBBA-PR) enables
the team to trade-off between convergence time and increased coordination by
resetting a portion of their previous allocation at every round of bidding on
tasks. By resetting the last tasks allocated by each agent, we are able to
ensure the convergence of the team to a conflict-free solution. CBBA-PR can be
further improved by reducing the team size involved in the replanning, further
reducing the communication burden of the team and runtime of CBBA-PR. Finally,
we validate the faster convergence and improved solution quality of CBBA-PR in
multi-UAV simulations.
