Improved Q -Learning Method for Multirobot Formation and Path Planning with Concave Obstacles

Aiming at the formation and path planning of multirobot systems in an unknown environment, a path planning method for multirobot formation based on improved Q -learning is proposed. Based on the leader-following approach, the leader robot uses an improved Q -learning algorithm to plan the path and the follower robot achieves a tracking strategy of gravitational potential field (GPF) by designing a cost function to select actions. Specifically, to improve the Q-learning, Q -value is initialized by environmental guidance of the target’s GPF. Then, the virtual obstacle-filling avoidance strategy is presented to fill non-obstacles which is judged to tend to concave obstacles with virtual obstacles. Besides, the simulated annealing (SA) algorithm whose controlling temperature is adjusted in real time according to the learning situation of the Q -learning is applied to improve the action selection strategy. The experimental results show that the improved Q -learning algorithm reduces the convergence time by 89.9% and the number of convergence rounds by 63.4% compared with the traditional algorithm. With the help of the method, multiple robots have a clear division of labor and quickly plan a globally optimized formation path in a completely unknown environment.

Group consensus coordination control in networked nonholonomic multirobot systems

In this article, the coordination control problem of group tracking consensus is considered for networked nonholonomic mobile multirobot systems (NNMMRSs). This problem framework generalizes the findings of complete consensus in NNMMRSs and group consensus in networked Lagrangian systems (NLSs), enjoying capacious application backgrounds. By leveraging a kinematic controller embedded in the adaptive torque control protocols, a new convergence criterion of group consensus is established. In contrast to the formulation under strict algebraic assumptions, it is found that group tracking consensus for NNMMRSs can be realized under a simple geometrical condition. The system stability analysis is dictated by the property of network topology with acyclic partition. Finally, the theoretical achievements are verified by illustrative numerical examples. The results show an interesting phenomenon that, for NNMMRSs, the state responses exhibit negative correlation with the algebraic connectivity and coupling strength.

Swarm Robot Exploration Strategy for Path Formation Tasks Inspired by Physarum polycephalum

Physarum polycephalum, a unicellular and multiheaded slime mould, can form highly efficient networks connecting separated food sources during the process of foraging. These adaptive networks exhibit a unique characteristic in that they are optimized without the control of a central consciousness. Inspired by this phenomenon, we present an efficient exploration and navigation strategy for a swarm of robots, which exploits cooperation and self-organisation to overcome the limited abilities of the individual robots. The task faced by the robots consists in the exploration of an unknown environment in order to find a path between two distant target areas. For the proposed algorithm (EAIPP), we experimentally present robustness tests and obstacle tests conducted to analyse the performance of our algorithm and compare the proposed algorithm with other swarm robot foraging algorithms that also focus on the path formation task. This work has certain significance for the research of swarm robots and Physarum polycephalum. For the research of swarm robotics, our algorithm not only can lead multirobot as a whole to overcome the limitations of very simple individual agents but also can offer better performance in terms of search efficiency and success rate. For the research of Physarum polycephalum, this work is the first one combining swarm robots and Physarum polycephalum. It also reveals the potential of the Physarum polycephalum foraging principle in multirobot systems.

Deadlock-Free Online Plan Repair in Multi-robot Coordination with Disturbances

Multirobot systems are increasingly deployed in environments where they interact with humans. From the perspective of a robot, such interaction could be considered a disturbance that causes a well-planned trajectory to fail. Previous approaches that modify trajectories in the presence of disturbances rearrange the order in which robots pass collision regions and other obstacles, in the laudable attempt to improve the average travel time for all robots. By doing so, however, deadlock may arise. In this paper, we provide a precise definition of deadlock using a graphical representation and prove some of its important properties. We show how to exploit the representation to detect the possibility of deadlock and to characterize conditions under which deadlock may not occur. We provide experiments in simulated environments that illustrate the potential usefulness of our theory of deadlock.