We propose a coalition-based approach to solve the task allocation problem of multiple robots with resource constraints. The resources required by task execution characterize the robots and tasks. Robots must form coalitions to accomplish the assigned tasks because individually, each robot may not complete the task independently due to resource limitation. We consider both online and offline assignment manners of the task allocation problem. For online assignment, a sequential coalition method is proposed to select efficiently the suitable robots to form coalitions for the assigned task. For offline assignment, a holistic coalition method is proposed for global optimization of all the assigned tasks. Both sequential and holistic coalition methods are compared with existing approaches. Numerous simulations and experiments performed on heterogeneous multiple mobile robots demonstrate the effectiveness of the proposed coalition-based task allocation methods. Note to Practitioners - Task allocation into a group of heterogeneous mobile robots for implementing multiple tasks is a challenge in multirobot applications. To find and organize the most suitable coalition for each task, we need to well organize the coalition for each task to maximize the robot group utility and optimize the task allocation solution. The sequential and holistic coalition methods presented in this paper provide both online and offline solutions for optimal multirobot task allocation. The advantage of the sequential coalition method lies in its efficiency in selecting best-fitted robots during coalition forming, and the advantage of the holistic coalition method lies in its effectiveness in finding the global optimal solution for all tasks. We illustrate the effectiveness of the proposed methods through numerous case studies with comparisons in this paper.

This paper presents a novel formation framework for the manipulation of micro-scaled particles with robotics and optical tweezers technologies. An allocation-based formation algorithm is used to calculate particles' trajectories. Along the trajectories, particles are trapped and moved by optical tweezers. Particles can be gradually moved into a pre-defined formation array. The main contribution of this paper lies in the proposal of using multi-agent solution to address the formation problem of particles in micro environment. The proposed framework can be applied to many bio-applications, such as cell sorting, cell transportation, cell-to-cell interaction study, etc., with high throughput and precision. Experiments on micro-scaled particles, with a robot-tweezer manipulation system, are performed to demonstrate the effectiveness of the proposed approach.

This paper presents a novel flocking framework for the manipulation of micro-scale particles with robotics and optical tweezers technologies. A region-based flocking algorithm is used to calculate the particles' trajectories. The optical tweezers are used to trap and move the particles along generated trajectories. All particles can be gradually moved into a pre-defined region. The main contribution of this paper lies in the proposal of using multi-agent solution to address the flocking problem of particles in micro environment. The proposed framework can be applied to many bio-applications such as cell sorting, cell property characterization, and so on, with high throughput and precision. Experiments on micro-scale particles with a robot-tweezers system are finally performed to verify the effectiveness of the proposed approach.

This paper investigates the multirobot task allocation (MRTA) problem for a group of heterogeneous mobile robots. The robots and tasks are characterized by resources as required by task execution. The robots are required to generate optimal solutions for the MRTA problem while forming coalitions to meet the resource constraints imposed by tasks. A leader-follower based coalition methodology is developed, with detailed discussions on leader selection, coalition forming and refinement algorithms. It is shown that the resource constrained task allocation problem can be well resolved by the proposed leader-follower coalition algorithms. Simulations performed on a mobile robot group demonstrate the effectiveness of the proposed approach.