In recent years, there has been a growing interest in designing multi-robot systems (hereafter MRSs) to provide cost effective, fault-tolerant and reliable solutions to a variety of automated applications. Here, we review recent advancements in MRSs specifically designed for cooperative object transport, which requires the members of MRSs to coordinate their actions to transport objects from a starting position to a final destination. To achieve cooperative object transport, a wide range of transport, coordination and control strategies have been proposed. Our goal is to provide a comprehensive summary for this relatively heterogeneous and fast-growing body of scientific literature. While distilling the information, we purposefully avoid using hierarchical dichotomies, which have been traditionally used in the field of MRSs. Instead, we employ a coarse-grain approach by classifying each study based on the transport strategy used; pushing-only, grasping and caging. We identify key design constraints that may be shared among these studies despite considerable differences in their design methods. In the end, we discuss several open challenges and possible directions for future work to improve the performance of the current MRSs. Overall, we hope to increasethe visibility and accessibility of the excellent studies in the field and provide a framework that helps the reader to navigate through them more effectively.

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Navigation on unmarked and possible poorly delineated roads where the boundaries between the road and the nonroad surfaces are not clearly indicated is a particularly challenging task for autonomous vehicles. The results of this study show that fairly robust navigation strategies can be generated by a robot equipped with a dynamic activevision based control system represented by an artificial neural network synthesized using evolutionary computation techniques. In the experiments described in this paper, a simulated Pioneer robot is required to visually navigate multiple poorly delineated roads that differ in terms of variations in luminance and/or chrominance between the road and the adjacent non-road areas. Low resolution camera images are processed by a mechanism that continuously adjusts the contribution of each component of a three dimensional colour model (e.g., R, G and B) to the generation of the robot perceptual experience. We show that the best controller can successfully drive a simulated Pioneer robot in environments with colour characteristics never encountered during the design phase, and operate with colour models never used during training. We show that the dynamic differential weighting of the colour components is underpinned by a complex pattern of neural activity that allows the robot to successfully adapt its perceptual system to the colour characteristics of different visual scenes. We also show that the controller can be easily ported onto real hardware, by showing the results of a series of tests with a physical Pioneer robot required to navigate various poorly delineated pedestrian roads.