Journal of Robotics

Delivery robots face the problem of storage and computational stress when performing immediate tasks, exceeding the limits of on-board computing power. Based on cloud computing, robots can offload intensive tasks to the cloud and acquire massive data resources. With its distributed cluster architecture, the platform can help offload computing and improve the computing power of the control center, which can be considered the external “brain” of the robot. Although it expands the capabilities of the robot, cloud service deployment remains complex because most current cloud robot applications are based on monolithic architectures. Some scholars have proposed developing robot applications through the microservice development paradigm, but there is currently no unified microservice-based robot cloud platform. This paper proposes a delivery robot cloud platform based on microservice, providing dedicated services for autonomous driving of delivery robot. The microservice architecture is adopted to split the monomer robot application into multiple services and then implement automatic orchestration and deployment of services on the cloud platform based on components such as Kubernetes, Docker, and Jenkins. This enables containerized CI/CD (continuous integration, continuous deployment, and continuous delivery) for the cloud platform service, and the whole process can be visualized, repeatable, and traceable. The platform is prebuilt with development tools, and robot application developers can use these tools to develop in the cloud, without the need for any customization in the background, to achieve the rapid deployment and launch of robot cloud service. Through the cloud migration of traditional robot applications and the development of new APPs, the platform service capabilities are continuously improved. This paper verifies the feasibility of the platform architecture through the delivery scene experiment.

The bacterial foraging optimization algorithm (BFOA) is an intelligent population optimization algorithm widely used in collision avoidance problems; however, the BFOA is inappropriate for the intelligent ship collision avoidance planning with high safety requirements because BFOA converges slowly, optimizes inaccurately, and has low stability. To fix the above shortcomings of BFOA, an autonomous collision avoidance algorithm based on the improved bacterial foraging optimization algorithm (IBFOA) is demonstrated in this paper. An adaptive diminishing fractal dimension chemotactic step length is designed to replace the fixed step length to achieve the adaptive step length adjustment, an optimal swimming search method is proposed to solve the invalid searching and repeated searching problems of the traditional BFOA, and the adaptive migration probability is developed to take the place of the fixed migration probability to prevent elite individuals from being lost in BOFA. The simulation of benchmark tests shows that the IBFOA has a better convergence speed, optimized accuracy, and higher stability; according to a collision avoidance simulation of intelligent ships which applies the IBFOA, it can realize the autonomous collision avoidance of intelligent ships in dynamic obstacles environment is quick and safe. This research can also be used for intelligent collision avoidance of automatic driving ships.

Applying deep learning methods, this paper addresses depth prediction problem resulting from single monocular images. A vector of distances is predicted instead of a whole image matrix. A vector-only prediction decreases training overhead and prediction periods and requires less resources (memory, CPU). We propose a module which is more time efficient than the state-of-the-art modules ResNet, VGG, FCRN, and DORN. We enhanced the network results by training it on depth vectors from other levels (we get a new level by changing the Lidar tilt angle). The predicted results give a vector of distances around the robot, which is sufficient for the obstacle avoidance problem and many other applications.

This paper focuses on the design and development of a reconfigurable three-degree-of-freedom articulated robot for conducting pick-and-place tasks. To implement the system, an Android platform for the manual control of an articulated robot using wireless Bluetooth technology was developed. This application allows the user to manually reconfigure the robot following the requirements of the integrated system via a user-friendly display. The articulated robot comprises four motors, three of which are used for positioning and orientation and finally used to carry out the pick-and-place task. An Arduino Un R3 board is used to control the movement of the links via a pulse width modulation method. We introduce a set of conveniently composed kinematic and dynamic mathematical models for positioning the robot’s arms and, in our results and discussion section, calculate and report the torque required to move each joint.

This paper provides a brief history of medical robotic systems. Since the first use of robots in medical procedures, there have been countless companies competing to developed robotic systems in hopes to dominate a field. Many companies have succeeded, and many have failed. This review paper shows the timeline history of some of the old and most successful medical robots and new robotic systems. As the patents of the most successful system, i.e., Da Vinci® Surgical System, have expired or are expiring soon, this paper can provide some insights for new designers and manufacturers to explore new opportunities in this field.

In order to study the fault diagnosis method of small underwater thruster, an experimental device for fault diagnosis of underwater thruster is designed, and a controller hardware and monitoring software of upper computer and lower computer are developed to realize the acquisition and storage of parameters for underwater propeller. The experimental device can simulate four kinds of thruster faults, collect the hydrophone data, classify the fault types by fault clustering analysis, analyze the spectrum of four types of faults, and calculate the thrust under different fault conditions based on the results of spectrum analysis. The experimental results show that the experimental system effectively simulates different faults of the thruster, and the analysis method realizes the classification of different faults. The thrust loss of different faults is also calculated based on the analysis method.