For VSLAM (Visual Simultaneous Localization and Mapping), localization is a challenging task, especially for some challenging situations: textureless frames, motion blur, etc. To build a robust exploration and localization system in a given space, a submap-based VSLAM system is proposed in this paper. Our system uses a submap back-end and a visual front-end. The main advantage of our system is its robustness with respect to tracking failure, a common problem in current VSLAM algorithms. The robustness of our system is compared with the state-of-the-art in terms of average tracking percentage. The precision of our system is also evaluated in terms of ATE (absolute trajectory error) RMSE (root mean square error) comparing the state-of-the-art. The ability of our system in solving the “kidnapped” problem is demonstrated. Our system can improve the robustness of visual localization in challenging situations.

In this paper, we present a collective, or swarm construction algorithm to control robotic bulldozers in the creation of a work site. Predictions about robotic missions to the planet Mars have described such site preparation as essential to the success of later mission objectives, such as the construction of solar arrays, etc. This algorithm was based on a behaviour observed in a particular species of ant called "blind bulldozing". We developed a mathematical model of blind bulldozing using a unique approach based on Markov chains. Robot bulldozers were developed and used to test the algorithms in our laboratory. The team of robots was found to be successful at clearing an open area out of field of rocks. Our robots' behaviour also agreed with the predictions of our model. This work is significant because it demonstrated the viability of blind bulldozing and represents the first time, to our knowledge, that a multiple robot system has carried out a form of the general construction task outside of simulation.

In this paper we present an approach to controlling transitions in multirobot tasks which have been modelled as a linear series of steps. A box-pushing task is described as a sequence of sub-tasks with a separate controller designed for each step using finite state automata theory. Perceptual cues are formed by concatenating binary variables which represent locally sensed stimuli into boolean vectors used to specify transitions between sub-task steps. The approach is designed for a redundant set of homogeneous mobile robots equipped with simple sensors and stimulus-response behaviours. A set of perceptual cues used in box-pushing are designed and tested on 10 physical mobile robots. It is argued that perceptual cues and finite state automata offers a new approach to environment-specific task modelling in collective robotics.

Accomplishing useful tasks with a collection of decentralized mobile robots will require control methods that deal effectively with a number of unique problems that impede the system's progress. Reactive control architectures can easily cause the problems of stagnation and cyclic behaviour, both characterized by a lack of progress in achieving a task. In this paper the authors present one possible solution to stagnation recovery, motivated from the study of group transport in ants and demonstrate its use in a box-pushing task. By using stagnation recovery behaviours, which are triggered by a lack of progress in the task-achieving activity of the system, the collective system can monitor its own advancement in a decentralized manner. A set of such behaviours are progressively ordered using timeouts, with each set designed for a specific recovery strategy. The stagnation recovery behaviours have been tested in simulation with the results to be mapped onto a set of ten autonomous robots presently under construction.>

In this paper, the authors explore the idea of using an adaptive logic network (ALN) for behavior arbitration. Their approach is to define the collective task, to be performed by multiple robots, as a group behavior. The group behavior is a set of behaviors, each of which specifies a single step in the collective task. An environmental cue can be used to control the transition between behaviors, thus allowing the progress of the collective task to self-govern its execution. The authors simplify the behavior arbitration in the collective task by training an ALN implemented using simple combinational logic. They provide a description of the Collective Robotic Intelligence Project (CRIP) including the simulation results and the multi-robot system on which these results will be deployed.