We study the problem of distributed collision avoidance for mobile robotic systems, where a group of heterogeneous robots with different sizes and motion constraints avoid collisions with each other and static obstacles during the movements from their starting to goal locations. Existing methods mainly consider homogeneous robots and incur a high collision rate in environments with moving robots and static objects. Hence, we propose a distributedcollisionavoidance forheterogeneous mobile robots (Heter-CA), which allows each robot to independently avoid collisions considering the heterogeneity of robots and varying static obstacles. InHeter-CA, each robot predicts the trajectories of neighboring robots and estimates the varying size of static obstacles with the robots' range-finder sensors before motion planning, which enables each robot to avoid obstacles safely. Besides, we prove thatHeter-CAcan guarantee collision-free movement between heterogeneous robots by satisfying sufficient conditions. We evaluateHeter-CAin numerous simulated and real-world scenarios in which groups of heterogeneous robots perform navigation tasks. The experimental results demonstrate thatHeter-CAtakes$10\times$less computation time and achieves$5\%$less collision rate than baseline algorithms.

Pipelines have become one of the most important infrastructures in the city. Over time, they are prone to aging, cracks, corrosion, and the demand for regular inspection is gradually increasing. Robotic solutions are effective methods for in-pipe inspection. However, existing In-pipe Inspection Robots (IPIR) require that the inner diameter of the pipe is fixed in the application scenarios, and need extra labor to control the robot and handle the cable. In this work, we design and develop a deformable robot to adapt to pipes with different inner diameters. Specifically, the passive elastic hinge is used by us to make the robot fully in contact with the pipe, generating enough friction to ensure that the robot is attached to the inner wall of the pipe. An edge device is deployed on the robot, generating velocity commands of wheels through the data from Inertial Measurement Unit (IMU), which eliminates the need for external devices. Experimental results demonstrate that the robot can move in horizontal and vertical pipelines, as well as traverse through pipe joints and scenarios where there is dirty or small obstacle.

Manipulator teams are frequently employed in various industrial applications to handle challenging cooperative tasks. The complicated interaction between manipulators makes it difficult to design applications from scratch. Although robotics middleware has emerged as the key to lowering the development complexity of manipulator applications, existing works still have limitations in controlling multiple manipulators to carry out tasks cooperatively. To overcome the limitations, middleware should provide programming abstraction support, coordination mechanism, and dynamic reconfiguration of motion controllers so that a team of manipulators can work together efficiently. This work proposes ManiWare, an easy-to-use middleware that provides the team-level programming abstraction and the manipulator-level plugin mechanism for programming and configuring manipulator applications. The team-level programming abstraction can facilitate the development process by invoking the functions from the fundamental cooperation components, which drives the developers to focus on designing application logic. Besides, the plugin mechanism dynamically configures and manages the motion controller of different parts of manipulators, making the reconfiguration feasible. This work implements ManiWare and evaluates the task execution performance with three case studies in the high-fidelity simulation platform. The experimental results demonstrate that ManiWare facilitates cooperative tasks with a high success rate, efficient completion time, and marginal runtime overhead. The source code is athttps://github.com/sundycoder/maniware.

Manipulator teams are widely used to handle complex and cooperative tasks in various industrial applications. Although robotic middleware has emerged as the key to reducing the complexity of manipulator application development, existing works still have limitations in controlling multiple manipulators to perform tasks cooperatively. Therefore, middleware should provide programming abstraction support, coordination mechanism, and dynamic reconfiguration of motion controllers so that the manipulator teams can work efficiently. In this paper, we propose ManiWare, an easy-to-use middleware that provides a team-level programming abstraction and the manipulator-level plugin mechanism for programming and building manipulator applications. The team-level programming abstraction can facili-tate the development process, which drives the developers to focus on application logic development. Besides, the plugin mechanism configures and manages the motion controller of different components of manipulators. We implement the functional components in ManiWare and study two cases in the high-fidelity simulation engine. The utilization of ManiWare can reduce the construction difficulties of building manipulator applications.

Summary Multi‐robot systems are widely used to handle complex and cooperative missions in various industrial applications. Although robotic middleware has become the key to reducing the complexity of multi‐robot application development, existing works still have limitations in controlling multiple robots to perform missions cooperatively. To enable multi‐robot cooperation, middleware should provide high‐level abstraction support, dynamic configuration, communication, and synchronization. In this article, we proposeGraphWare, a novel middleware that provides a graph‐based programming abstraction and its underlying runtime kernel for programming and building multi‐robot cooperation applications. The graph‐based programming abstraction can express cooperative missions without exposing the complexity of managing multiple robots. The runtime kernel configures and manages multiple heterogeneous robots to intelligently perform cooperative missions. We implementGraphWareand evaluate its performance with ball collection missions which are cooperatively accomplished by a group of mobile robots, and study the fault‐tolerance, flexibility, and scalability of the middleware in the realistic simulation. The experimental results demonstrate thatGraphWarefacilitates the multi‐robot cooperative mission with efficient mission completion time, high success rate, and marginal runtime overhead.