Algorithm For Multiple Robots Research Articles

Navigation of Multiple Robots in Formative Manner in an Unknown Environment using Artificial Potential Field Based Path Planning Algorithm

This paper proposes a new algorithm for multiple robots that can navigate from source to goal position in a coordinated and formative manner in an optimum path and time, efficiently avoiding static and dynamic obstacles. The proposed algorithm is applied here to track surveillance for two and four robots from starting point to the goal point in an obstacle environment without collision. This algorithm is inspired by the concept of the Artificial Potential Field (APF) method and behavior-based approximate reasoning. An exhaustive search approach is used to avoid the randomly appeared obstacles, approximately estimate positions of obstacles, thus reaching the surveillance point and then tracking the allotted path for the robots. The robots are governed to move in real-time, maintaining a formation between them to track the assigned path cooperatively. The performance of this algorithm is validated in a real-time and simulation environment using MATLAB/Simulink. It is observed from the results of performance measuring parameters path length and travel time that the proposed algorithm outperforms than existing A*, RRT, and GA algorithm.

Randomized gathering of asynchronous mobile robots

This paper revisits the widely researched gathering problem for two robots without any agreement on the coordinate system in a scenario which allows randomization in the asynchronous scheduling model. The scheduler is considered to be the adversary which determines the activation schedule of the robots. The adversary comes in three flavors, namely, adaptive offline, adaptive online, and oblivious, based on the knowledge of the outcome of random bits. The robots follow wait-look-compute-move cycle. In this paper, we classify the problems based on the capability of the adversary to control the parameters such as wait time, computation delay and the speed of robots and check the feasibility of gathering in terms of adversarial knowledge and capabilities. First, we show the impossibility of gathering under an adaptive offline adversary with non-negative wait time and non-negative computation delay. Gradually relaxing the adversarial capabilities, we present impossibility of gathering for finite random choices and improbability for infinite random choices for an adaptive online adversary. Under an oblivious adversary, we establish the possibility of gathering with zero computation delay. We improve the runtime of the algorithm by restricting the oblivious adversary to choose wait time and computation delay such that the sum of wait time and computation delay is more than a positive constant. Finally, we also extend our algorithm for multiple robots with merging.

A Bi-Level Probabilistic Path Planning Algorithm for Multiple Robots with Motion Uncertainty

For improving the system efficiency when there are motion uncertainties among robots in the warehouse environment, this paper proposes a bi-level probabilistic path planning algorithm. In the proposed algorithm, the map is partitioned into multiple interconnected districts and the architecture of proposed algorithm is composed of topology level and route level generating from above map: in the topology level, the order of passing districts is planned combined with the district crowdedness to achieve the district equilibrium and reduce the influence of robots under motion uncertainty. And in the route level, a MDP method combined with probability of motion uncertainty is proposed to plan path for all robots in each district separately. At the same time, the number of steps for each planning is dependent on the probability to decrease the number of planning. The conflict avoidance is proved, and optimization is discussed for the proposed algorithm. Simulation results show that the proposed algorithm achieves improved system efficiency and also has acceptable real-time performance.

Dynamic Formation Control for Car-like Mobile Robots

For the formation problem of the car-like mobile robots, a dynamic coordinated control algorithm for multiple robots is proposed. Firstly, a model of trajectory tracking system for the follower robot is established through the virtual robot, which is generated according to the desired formation parameters of the leader and follower robots. Then the robot's kinematics model is transformed into the chain form, and the Lyapunov function of the trajectory tracking system is constructed via the backstepping method. The trajectory tracking controller for the follower robot is obtained by making this Lyapunov function negative-definite. The formation control of the car-like mobile robots is realized in this way. A 3D simulation testbed is designed by the Microsoft Robotics Developer Studio 4(MRDS4), and the effectiveness of the proposed method is demonstrated through 3 sets of experiments.

Concurrent localization of multiple robots

A concurrent localization method for multiple robots using ultrasonic beacons is proposed. This method provides a high-accuracy solution using only low-price sensors. To measure the distance of a mobile robot from a beacon at a known position, the mobile robot alerts one beacon to send out an ultrasonic signal to measure the traveling time from the beacon to the mobile robot. When multiple robots requiring localization are moving in the same block, it is necessary to have a schedule to choose the measuring sequence in order to overcome constant ultrasonic signal interference among robots. However, the increased time delay needed to estimate the positions of multiple robots degrades the localization accuracy. To solve this problem, we propose an efficient localization algorithm for multiple robots, where the robots are in groups of one master robot and several slave robots. In this method, when a master robot calls a beacon, all the group robots simultaneously receive an identical ultrasonic signal to estimate their positions. The effectiveness of the proposed algorithm has been verified through experiments.