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Programming control systems for mobile robots is complicated and time-consuming, due to three aspects, i.e., the robot behavior coordination, the distributed multi-robot cooperation and the robot software reusability. Subsumption model is a robust control architecture for mobile robots. ALLIANCE model extends it to multirobot systems, which is a fully distributed, fault-tolerant model. Robot operating system (ROS) provides a lot of reusable robot modules. By combining the above three, we propose a software framework named ALLIANCE-ROS for developing fault-tolerant cooperative multi-robot systems with abundant software resources available. We encapsulate the ROS facilities to build the framework prototype. We also use some high-performance plugin-based mechanism to optimize the bottom of the framework. One may use the framework-provided API conveniently to construct single-robot and multi-robot applications with all ROS resources available. This work is demonstrated by three application cases including an autonomous roving robot, a security patrol robot and multiple patrol robots. They are constructed and tested in both the simulated and the real environment. The experimental results validate the usability and availability of ALLIANCE-ROS.

Deep reinforcement learning is making advances in robotics with the platforms of realistic environment simulation. However, as shown in this paper, the realistic simulation introduces vast time cost which is the bottleneck of the learning procedure. To solve this problem generally, we propose a parallel reinforcement learning platform which follows the master-slave principle and integrates learning programs with multiple distributedrobot simulators. The platform is intrinsically scalable and requires no modification to existing serially designed learning environments or algorithms. Experimental results demonstrate that our platform significantly accelerates the learning progress of robots, in direct proportion to the parallel scale. The parallelism also brings richer exploration and sampling, enhancing the performance of deep reinforcement learning algorithms compared with existing serial platforms.

Programming swarm robots is considered to be more challenging than developing single robot applications, mostly due to the unpredictable behaviors and complex coordination of the swarm. In this paper, we present GSDF, a generic development framework for swarm robotics, to promote both programmability and flexibility. In its core lies a lightweight runtime, which maintains all the swarm-related data structures in a decentralized manner. The runtime is based on the design of a two-level communication graph, which decouples intra- and inter-robot communications. On the intra-robot level, existing popular messaging mechanisms, such as that of the Robot Operating System (ROS), can be adopted to reuse programming resources in the ROS eco-system. On the inter-robot level, the framework presents an abstract communication layer. It provides a unified communication interface to the framework runtime, and hence makes the framework agnostic to underlying communication mechanisms. GSDF also provides a clear interface to facilitate programming robot swarms, including prescribed tools for swarm management and information sharing. Finally, we demonstrate the feasibility and efficiency of GSDF using a couple of experiments in robotic simulators.

The agent-based subsumption model is widely acknowledged as the control systems for mobile robots. In this model, incremental layers can be stacked together by inhibitors and suppressors, which increasingly leads to complex and coordinate behaviors. ROS (Robot Operating System) is an open source robot software platform and gradually becoming the de facto standard for robot applications. There are abundant reusable function units in ROS, which can be coupled by a distributed messaging mechanism. This paper describes a template based on ROS for implementing the sub-sumption model, such that one can develop control systems for mobile robots by leveraging ROS-provided software resources. The publish/subscribe messaging mechanism in ROS is used to connect the loosely coupled modules of each layer. The behaviors inside a module are formalized as easy-to-use ROS-based finite state machines. The inhibitors and suppressors among layers are represented as ROS nodes and implemented as templates, which can be easily instantiated. And the work is demonstrated by two experiments. First, a three-layer autonomous wander robot, which is originally designed by Brooks, is reproduced in the ROS simulating environment. Second, a six-layer security patrol robot application, which is controlled by the subsumption model, is constructed and tested in the real world.

Programming multi-robot systems is a complicated and time-consuming work, due to two challenges, i.e., the distributed multi-robot cooperation and the robot software reusability. ALLIANCE [1] is a fully distributed, fault-tolerant and behavior-based model. ROS (Robot Operating System) provides abundant robot software modules. In this paper, by combining both, we propose a software architecture named ALLIANCE-ROS for developing fault-tolerant cooperative multi-robot systems with a lot of software resources available. We encapsulate the ROS mechanisms and Python libraries to construct the basic function units of the ALLIANCE model. One may inherit them to construct the ALLIANCE-model-application with all ROS algorithms, modules and resources available. This work is demonstrated by an experiment of multi-robot patrol in both the simulated and the real environments.