Robotic systems in delicate fields like manufacturing, healthcare, and defence have created difficult forensic and security issues. The popular robotics software known as Robot Operating System (ROS) is modular, decentralised, and devoid of built-in security features. Because traditional digital forensic techniques are designed for centralised computing systems, these features make their application more difficult. A new forensic framework designed specifically for ROS-based systems, the Robot Operating System Forensic Framework (ROSFF), is presented here. In contrast to conventional forensic models, ROSFF resolves the odd ROS structure by combining procedures and tools that facilitate decentralised logging, distributed evidence collection, and real-time monitoring. Because it integrates the three areas of investigation: technical, organisational, and legal. The forensic process becomes scalable, methodical, and also morally and legally sound. With the help of anomaly detection and event tracing, ROSFF is constructed using four fundamental steps: data acquisition, analysis, examination, and reporting. When compared to other forensic models, ROSFF is more adaptable, provides comprehensive evidence, and can be used with robotic systems. The results demonstrate that ROSFF is a useful tool for conducting efficient forensic investigations and safeguarding ROS environments. This work lays the groundwork for upcoming digital forensic operations in autonomous systems and fills a major knowledge gap in robotic cybersecurity.

This paper focuses on target-oriented grasping in occluded scenes, where the target object is specified by a binary mask and the goal is to grasp the target object with as few robotic manipulations as possible. Most existing methods rely on a push-grasping synergy to complete this task. To deliver a more powerful target-oriented grasping pipeline, we present MPGNet, a three-branch network for learning a synergy between moving, pushing, and grasping actions. We also propose a multi-stage training strategy to train the MPGNet which contains three policy networks corresponding to the three actions. The effectiveness of our method is demonstrated via both simulated and real-world experiments. Video of the real-world experiments is at https://youtu.be/S_QKZqkh0w8.

Efficiently detecting target weld seams while ensuring sub-millimeter accuracy has always been an important challenge in autonomous welding, which has significant application in industrial practice. Previous works mostly focused on recognizing and localizing welding seams one by one, leading to inferior efficiency in modeling the workpiece. This paper proposes a novel framework capable of multiple weld seams extraction using both RGB images and 3D point clouds. The RGB image is used to obtain the region of interest by approximately localizing the weld seams, and the point cloud is used to achieve the fine-edge extraction of the weld seams within the region of interest using region growth. Our method is further accelerated by using a pre-trained deep learning model to ensure both efficiency and generalization ability. The proposed method was comprehensively tested on various workpieces featuring both linear and curved weld seams, as well as in physical experiment systems. The results showcase considerable potential for real-world industrial applications, emphasizing the method’s efficiency and effectiveness. Videos of the real-world experiments can be found at https://youtu.be/pq162HSP2D4.

This work embodies the overspeed protection and safe headway control into an iterative learning control (ILC) based train trajectory tracking algorithm to satisfy the high safety requirement of high-speed railways. First, a D-type ILC scheme with overspeed protection is proposed. Then, a corresponding coordinated ILC scheme with multiple trains is studied to keep the safe headway. Finally, the control scheme under traction/braking force constraint is also considered for this proposed ILC-based train trajectory tracking strategy. Rigorous theoretical analysis has shown that the proposed control schemes can guarantee the asymptotic convergence of train speed and position to its desired profiles without requirement of the physical model aside from some mild assumptions on the system. Effectiveness is further evaluated through simulations.

Automated task planning for service robots faces great challenges in handling dynamic domestic environments. Classical methods in the Artificial Intelligence (AI) area mostly focus on relatively structured environments with fewer uncertainties. This work proposes a method to combine semantic knowledge representation with classical approaches in AI to build a flexible framework that can assist service robots in task planning at the high symbolic level. A semantic knowledge ontology is constructed for representing two main types of information: environmental description and robot primitive actions. Environmental knowledge is used to handle spatial uncertainties of particular objects. Primitive actions, which the robot can execute, are constructed based on a STRIPS-style structure, allowing a feasible solution (an action sequence) for a particular task to be created. With the Care-O-Bot (CoB) robot as the platform, we explain this work with a simple, but still challenging, scenario named “get a milk box”. A recursive back-trace search algorithm is introduced for task planning, where three main components are involved, namely primitive actions, world states, and mental actions. The feasibility of the work is demonstrated with the CoB in a simulated environment.

Symbolic grounding in unstructured environments remains an important challenge in robotics [7]. Homecare robots are often required to be instructed by their human users intuitively, which means the robots are expected to take highlevel commands and execute corresponding tasks in a domestic environment. High-level commands are represented with symbolic terms such as “near” and “close” and, on the other hand, robots are controlled based on trajectories. The robots need to translate the symbolic terms to trajectories. In addition, domestic environment is unstructured where the same objects can be placed in different places over the time. This increases the difficulties in symbolic grounding. This paper presents a fuzzy logic based approach to symbolic grounding. In this approach, grounded concepts are modelled as fuzzy sets and the existing knowledge is used to deduce grounded values given real-time sensory inputs. Experiments results show that this approach works well in unstructured environment.

Symbolic grounding is a bridge between high-level planning and actual robot sensing, and actuation. Uncertainties raised by the unstructured environment make a bottleneck for integrating traditional artificial intelligence with service robotics. This paper presents a fuzzy logic based approach to formalise the grounding problems into a fuzzy optimization problem, which is robust to uncertainties. Novel techniques are applied to establish the objective function, to model fuzzy constraints and to perform fuzzy optimisation. The outcome is tested with a service robot fetch and carry task, where the fuzzy optimisation approach helps the robot to determine the most comfortable position (location and orientation) for grasping objects. Experimental results show that the proposed approach improves the robustness of the task implementation in unstructured environments.

SRS is a European research project for building robust personal assistant robots using ROS (Robotic Operating System) and Care-O-bot (COB) 3 as the initial demonstration platform. In this paper, experience gained while building the SRS system is presented. A main contribution of the paper is the SRS autonomous control framework. The framework is divided into two parts. First, it has an automatic task planner, which initialises actions on the symbolic level. The planner produces proactive robotic behaviours based on updated semantic knowledge. Second, it has an action executive for coordination actions at the level of sensing and actuation. The executive produces reactive behaviours in well-defined domains. The two parts are integrated by fuzzy logic based symbolic grounding. As a whole, they represent the framework for autonomous control. Based on the framework, several new components and user interfaces are integrated on top of COB's existing capabilities to enable robust fetch and carry in unstructured environments. The implementation strategy and results are discussed at the end of the paper.