Decentralized Task Re-planning Approaches with en Route Information Rewards


This paper presents extensions to the consensus-based bundle algorithm (CBBA) for distributed task planning to take into account rewards obtained en route in the information-gathering missions. The key idea is to incorporate acquired information on the fly when defining scores of the assigned tasks in the re-plan process so that agents can react to the changes in the environment with correct awareness of the executed task scores. Two methods are proposed to quantify this en route acquired information – linear heuristic and entropy-based reward. Numerical simulation results demonstrate that the proposed methods facilitate agents to perform more tasks and thus achieve higher overall scores.

In International Conference on Robot Intelligence Technology and Applications (RiTA).