Delayed Self-Reinforcement to Reduce Deformation during Decentralized Flexible-Object Transport

Recent works have investigated bio-inspired strategies to transport objects using decentralized robot networks that only use local measurements without the need for communication between robots. Typically, there is an inverse relationship between the object deformation and the transport time, i.e., reducing the object deformation requires an increase in the transport time needed for transitioning from the initial configuration to the final configuration of the object. In contrast, the main contribution in this article is the development of a delayed self-reinforcement (DSR) approach that seeks to decentralize the ideal centralized transport that has no object deformation, and thereby, reduces the object deformation without increasing the transport time for the same specified transport trajectory. In the DSR method, each robot only uses already available prior-information to reinforce each robot's actions, and thus, the DSR implementation does not require additional information or changes in the network structure. Another contribution in this article is to analyze the potentially time-varying dynamics to establish conditions on the control parameters for stability and quantify the performance of the proposed DSR-based transport. Furthermore, experimental results are presented to show that the proposed cohesive transport method can reduce the deformation by at least 72% for the same transport time, when compared to the case without DSR.