This paper proposes a novel two-layer framework based on conflict-based search and regional divisions to improve the efficiency of multi-robot path planning. The high-level layer targets the reduction of conflicts and deadlocks, while the low-level layer is responsible for actual path planning. Distinct from previous dual-level search frameworks, the novelties of this work are (1) subdivision of planning regions for each robot to decrease the number of conflicts encountered during planning; (2) consideration of the number of robots in the region during planning in the node expansion stage of A*, and (3) formal proof demonstrating the nonzero probability of the proposed method in obtaining a solution, along with providing the upper bound of the solution in a special case. Experimental comparisons with Enhanced Conflict-Based Search demonstrate that the proposed method not only reduces the number of conflicts but also achieves a computation time reduction of over 30%.

In this article, we investigate the problem of parameter identification of spatial–temporal varying processes described by a general nonlinear partial differential equation and validate the feasibility and robustness of the proposed algorithm using a group of coordinated mobile robots equipped with sensors in a realistic diffusion field. Based on the online parameter identification method developed in our previous work using multiple mobile robots, in this article, we first develop a parameterized model that represents the nonlinear spatially distributed field, then develop a parameter identification scheme consisting of a cooperative Kalman filter and recursive least square method. In the experiments, we focus on the diffusion field and consider the realistic scenarios that the diffusion field contains obstacles and hazard zones that the robots should avoid. The identified parameters together with the located source could potentially assist in the reconstruction and monitoring of the field. To validate the proposed methods, we generate a controllable carbon dioxide (CO2) field in our laboratory and build a static CO2 sensor network to measure and calibrate the field. With the reconstructed realistic diffusion field measured by the sensor network, a multi-robot system is developed to perform the parameter identification in the field. The results of simulations and experiments show satisfactory performance and robustness of the proposed algorithms.

Serial spherical linkages have been used in the design of a number of robots for minimally invasive surgery, in order to mechanically constrain the surgical instrument with respect to the incision. However, the typical serial spherical mechanism suffers from conflicting design objectives, resulting in an unsuitable compromise between avoiding collision with the patient and producing good kinematic and workspace characteristics. In this paper, we propose a multi-robot system composed of two redundant serial spherical linkages to achieve this purpose. A multi-objective optimization for achieving the aforementioned design goals is presented first for a single redundant robot and then for a multi-robot system. The problem of mounting multiple robots on the operating table as well as the way cooperative actions can be performed is addressed. The sensitivity of each optimal solution (single-robot and multi-robot) to uncertainties in the design parameters is investigated.

A novel approach for trajectory tracking of a mobile-robots formation by using linear algebra theory and numerical methods is presented in this paper. The formation controller design is based on the formation states concept and the dynamic model of a unicycle-like nonholonomic mobile robot. The proposed control law designed is decentralized and scalable. Simulations and experimental results confirm the feasibility and the effectiveness of the proposed controller and the advantages of using the dynamic model of the mobile robot. By using this new strategy, the formation of mobile robots is able to change its configuration (shape and size) and follow different trajectories in a precise way, minimizing the tracking and formation errors.

Two distributed stochastic search algorithms are proposed for motion planning of multi-robot systems: (i) distributed gradient, (ii) swarm intelligence theory. Distributed gradient consists of multiple stochastic search algorithms that start from different points in the solutions space and interact with each other while moving toward the goal position. Swarm intelligence theory is a derivative-free approach to the problem of multi-robot cooperation which works by searching iteratively in regions defined by each robot's best previous move and the best previous move of its neighbors. The performance of both approaches is evaluated through simulation tests.

A work cell with multiple robots increases manufacturing flexibility and productivity. Robots in the work cell equipped in a sparse area usually share a motion path, resources and workspace. In this paper, an embedded Markov chain model for a multi-robot system that has a common workspace is constructed on the basis of the concept of a multi-processor system. With the presented model, we measure the performance of error recovery schemes under different workloads and analyze the sensitivity of the execution time with respect to the robot speed. We verify the presented model with an experimental multi-robot work cell. This study is useful in evaluating the performance of a robotic work cell and presents a guide for designing a complex work cell.

The paper presents an object-oriented approach to the implementation of a software library (MRROC+) which contains building blocks for the construction of multi-robot system controllers tailored to meet specific demands of a task at hand. Moreover, the paper supplies a brief overview of robot programming methods.