Exploration and Coordination of Complementary Multirobot Teams in a Hunter-and-Gatherer Scenario

The hunter-and-gatherer approach copes with the problem of dynamic multirobot task allocation, where tasks are unknowingly distributed over an environment. This approach employs two complementary teams of agents: one agile in exploring (hunters) and another dexterous in completing (gatherers) the tasks. Although this approach has been studied from the task planning point of view in our previous works, the multirobot exploration and coordination aspects of the problem remain uninvestigated. This paper proposes a multirobot exploration algorithm for hunters based on innovative notions of “expected information gain” to minimize the collective cost of task accomplishments in a distributed manner. Besides, we present a coordination solution between hunters and gatherers by integrating the novel notion of profit margins into the concept of expected information gain. Statistical analysis of extensive simulation results confirms the efficacy of the proposed algorithms compared in different environments with varying levels of obstacle complexities. We also demonstrate that the lack of effective coordination between hunters and gatherers significantly distorts the total effectiveness of the planning, especially in environments containing dense obstacles and confined corridors. Finally, it is statistically proven that the overall workload is distributed equally for each type of agent which ensures that the proposed solution is not biased to a particular agent and all agents behave analogously under similar characteristics.

N-learning: An Approach for Learning and Teaching Skills in Multirobot Teams

SummaryWe propose the N-learning practical approach for teaching and learning behaviors in a multirobot system, which is performed through mandatory behavior acquisition based on interactions between the robots at execution time. The proposed methodology can be used to self-program the robots of a team by programming only a single robot with a set of codes that contain behaviors to be transferred and used by other robots as necessary. These codes are implemented in a modular fashion. An advantage of our approach is that when a team of robots is required to perform a specific mission, the set of behaviors required to accomplish that mission can be implemented only once in a single robot or in a distributed fashion. Then, these distributed behaviors are transferred to each of the other robots in the team according to their demand, without the need to reprogram them by hand since the robots in the team can share them autonomously. As an application example, a human critic can teach (or program) only one or a few robots, and these robots are thus able to exchange knowledge with the other team members since they have been preinstalled to run the N-learning system basics. Simulated and real robot experiments are performed to demonstrate the feasibility and validation of our approach.

Decentralized Cooperative Localization with Fault Detection and Isolation in Robot Teams

Robot localization, particularly multirobot localization, is an important task for multirobot teams. In this paper, a decentralized cooperative localization (DCL) algorithm with fault detection and isolation is proposed to estimate the positions of robots in mobile robot teams. To calculate the interestimate correlations in a distributed manner, the split covariance intersection filter (SCIF) is applied in the algorithm. Based on the split covariance intersection filter cooperative localization (SCIFCL) algorithm, we adopt fault detection and isolation (FDI) to improve the robustness and accuracy of the DCL results. In the proposed algorithm, the signature matrix of the original FDI algorithm is modified for application to DCL. A simulation-based comparative study is conducted to demonstrate the effectiveness of the proposed algorithm.

MARS: An Educational Environment for Multiagent Robot Simulations

Undergraduate robotics students often find it difficult to design and validate control algorithms for teams of mobile robots. This is mainly due to two reasons. First, very rarely, educational laboratories are equipped with large teams of robots, which are usually expensive, bulky, and difficult to manage and maintain. Second, robotics simulators often require students to spend much time to learn their use and functionalities. For this purpose, a simulator of multiagent mobile robots namedMARShas been developed within the Matlab environment, with the aim of helping students to simulate a wide variety of control algorithms in an easy way and without spending time for understanding a new language. Through this facility, the user is able to simulate multirobot teams performing different tasks, from cooperative to competitive ones, by using both centralized and distributed controllers. Virtual sensors are provided to simulate real devices. A graphical user interface allows students to monitor the robots behaviour through an online animation.