Flexible pick and place architecture using ROS framework

The spread adoption of humanoid social robots in different application fields is growing the interest of hackers who could violate the privacy of people, or—even worse—threaten humans’ life from physical and emotional/social point of views. Different vectors of attack exist, which are more easily exploitable if physical access to the target robot is available. This is very likely for humanoids that typically reside in untrusted environments where physically access to the robot is allowed and expected, thus permitting anyone to exploit the Linux kernel vulnerability (e.g., through the insertion of a USB pen drive) with the objective of tampering sensitive data. The Robot Operating System (ROS) is at the core of humanoids. Thus, it is crucial for their security. The most-recent solutions introduced in ROS2, SROS, and H-ROS are not sufficient for facing powerful adversaries. In this paper, we first identify the uncovered ROS weaknesses, which are particularly worrying in the case of humanoids. Then, we present our patched ROS solution called Trusted-ROS (TROS) leveraging hardware-assisted trusted computing to shield data managed by ROS, which otherwise would reside in robot’s memory unencrypted. The design of TROS is reported, together with a prototype implementation using a simulated version of the NAO humanoid secured through an Intel SGX hardware. Finally, we evaluated the proposed solution from both security and performance perspectives in order to demonstrate the practicability of our approach.

We present software for interfacing FITPix and USB Lite compatible readout electronics using Robot Operating System (ROS). ROS is widely adopted middleware for integration of sensors, processing algorithms and logic to autonomous systems such as robots, unmanned helicopters, and robotic payloads. Thanks to ROS, Timepix detectors can be deployed in automated experiments on platforms spanning from traditional desktop computers to small ARM devices such as Raspberry Pi and Odroid. Acquisition and detector settings can be controlled in Linux shell allowing deployment on headless devices. Using ROS networking capabilities, captured frames and detector control can be transmitted via a network, which allows building distributed processing pipelines. The proposed software is a lightweight package, easily connectable to existing visualization, logging, and processing software in ROS. It offers simple bindings to custom Python and C++ programs for real-time control of the acquisition or processing of the captured frames. Rospix was deployed on NASA suborbital rocket, which was successfully launched on April 4, 2018, from Kwajalein Atoll by Pennsylvania State University. Rospix provides reliable solution for mobile robots where the collected data are used in real time to guide the robot through an environment. We release Rospix using the GitHub platform and welcome the community to contribute on the first project connecting the fields of ionizing radiation imaging and mobile robotics.

Security for the Robot Operating System

Building customized control system for specific robot is generally acknowledged as the fundamental section of developing auto robots. To simplify the programming process and increase the reuse of codes, this research develops a general method of developing customized robot simulation and control system software with robot operating system (ROS). First, a 3D visualization model is created in URDF (unified robot description format), and is viewed in Rviz to achieve motion planning with MoveIt! software package. Second, the machine vision provided by camera driver package in ROS enables the use of tools for image process, 3D point cloud analysis to reconstruct the environment to achieve accurate target location. Third, the communication protocols provided by ROS like serial, Modbus support the communication system development. To examine the method, we designed a tomato harvesting dual-arm robot, and conducted farming experiment with it. This work demonstrates the advantages of ROS when applied in robot control system development, and offers a plain method of building such system with ROS.

The next chapter of the robotics revolution is well underway with the deployment of robots for a broad range of commercial use cases. Even in a myriad of applications and environments, there exists a common vocabulary of components that robots share-the need for a modular, scalable, and reliable architecture; sensing; planning; mobility; and autonomy. The Robot Operating System (ROS) was an integral part of the last chapter, demonstrably expediting robotics research with freely available components and a modular framework. However, ROS 1 was not designed with many necessary production-grade features and algorithms. ROS 2 and its related projects have been redesigned from the ground up to meet the challenges set forth by modern robotic systems in new and exploratory domains at all scales. In this Review, we highlight the philosophical and architectural changes of ROS 2 powering this new chapter in the robotics revolution. We also show through case studies the influence ROS 2 and its adoption has had on accelerating real robot systems to reliable deployment in an assortment of challenging environments.

There are hundreds of companies out there that are bringing new solutions in the field of robotics and trying to get rid of a thousand problems they face. Nevertheless, most of their results do not leave the doors of the lab and remain without any decent attention from society. Institutions and companies require highly qualified personnel to accelerate the development of new solutions and products. High expenses deter broad masses from participating in this activity. It is not only the high price that keeps people away from being a part of the community but also a high entry level to the field. In our paper we consider an approach that makes the process of developing and integration of robotic systems faster and more accessible to the others. At first, the idea implies removing a technical barrier between science labs and other individuals. Secondly, all processes must be automatized by different tools to the greatest possible extent. As a result, we get a cloud web application where anyone can add or edit robotic systems algorithms. There are open technologies that can help us to implement this solution: virtualization, dockerization, web 3d simulator Gazebo, robot operation system (ROS).

The control system of a high-DOF human-interaction robot requires the high-frequency simultaneous control of multiple joints, which needs the capability to handle computation-massive tasks with low latancies. Aiming at the force control of 2-DOF fluid-actuated manipulator, I'm going to introduce a distributed infrastructure design with good flexibility and scalability, while maintaining a high-quality real-time performance. In this thesis, a motor control library, arranged on a core-isolated real-time machine, is developed to provide control implementers with functional APIs and input/output mapping of low-level data structures from SOEM (Simple Open EtherCAT Master) interfacing the direct-drive motors and peripheral sensors via EtherCAT bus communication. While on a remote machine, the ROS (Robot Operating System) is deployed to deal with high-level non-realtime applications, including algorithm processing, data visualization and status analysis. At the same time, a user-friendly Python proxy with a couple of control APIs are offered as an auxiliary medium besides the ROS native interfaces. To synchronize the user-facing data on both sides, a robust ROS interface is developed using the Shared Memory technique which is an IPC (Inter-Process Conmmunication) mechanism supported by Linux POSIX standards. The teleoperation experiment demonstrates that the robot system can operate the closed-loop control consistently at 2 kHz, while limiting the loaded latency below 100 us. In addition, the collision detection test shows both dexterity and maneuverability under unknown environments.

Robotic fleet management systems are increasingly vital for sustainable operations in agriculture, forestry, and other field domains where labor shortages, efficiency, and environmental concerns intersect. We present FORMIGA, a fleet management framework that integrates human operators and autonomous robots into a collaborative ecosystem. FORMIGA combines standardised communication through the Robot Operating System with a user-centered interface for monitoring and intervention, while also leveraging large language models to generate executable task code from natural language prompts. The framework was deployed and validated within the FEROX project, a European initiative addressing sustainable berry harvesting in remote environments. In simulation-based trials, FORMIGA demonstrated adaptive task allocation, reduced operator workload, and faster task completion compared to semi-autonomous control, enabling dynamic labor division between humans and robots. By enhancing productivity, supporting worker safety, and promoting resource-efficient operations, FORMIGA contributes to the economic, and environmental dimensions of sustainability, offering a transferable tool for advancing human-robot collaboration in field robotics.

Autonomous service robots are gaining popularity as they find application in different sectors such as logistics, hospitality, medical, space exploration and military. It is widely seen that autonomous service robots help increase efficiency, reduce cost and ensure safer operations. Automating material handling operations in highly dynamic indoor environments is risky and involves use of complex algorithms. However, continuously advancing technologies and machine programming tend to overcome these challenges. The three essential functions of an autonomous robot are to identify its instantaneous location in three-dimensional space, generate the environment map and decide an optimal path for locomotion. Simultaneous Localization and Mapping (SLAM) is a widely used algorithm for navigation in unfamiliar environments. This chapter presents a cost-effective autonomous ground robot for automating material handling, transportation and other repetitive tasks in dynamically changing surroundings. The robot is programmed to map the indoor environment and localize itself in the workspace with the help of SLAM and Adaptive Monte Carlo Localization (AMCL). In addition, the robot can manoeuvre through the indoor workspace by avoiding static and dynamic obstacles. This work employs the GMapping open-source package in the Robot Operating System (ROS) for performing SLAM with the aid of a laser range finder, wheel odometry and inertial data. The GMapping algorithm uses a probabilistic approach to construct a map and estimate the robot’s location in two-dimensional space. In addition, this chapter presents an autonomous navigation feature with the help of the AMCL method for localizing the robot within an uncertain environment and making the robot move to its goal using move base actions in ROS. The path planning of the mobile robot is classified into global and local plans. The global plan is responsible for planning the path from the start point to the endpoint. The local plan is responsible for planning the actual movement of the robot by avoiding any dynamic obstacles not mapped earlier. This work uses the Dijistra algorithm for the global and the dynamic window approach (DWA) algorithm for the local path planning. The optimization algorithm minimizes the error between the global and local paths. The simulation study of the proposed algorithm is performed in a virtual environment called Gazebo in ROS, which is nearly similar to a real-time indoor workspace. Further, a robot prototype is fabricated, and its performance is evaluated in an unknown environment. The robot prototype is equipped with brushed DC motors with optical encoder feedback for wheel odometry (ODOM), Light Detection and Ranging (LiDAR), and Inertial Measurement Unit (IMU) sensors controlled using a Jetson Nano. The simulation and experimental results showcase the robot’s successful two-dimensional localization and navigation in the factory environment. The current system has the potential for indoor assistance during material transfer and logistic applications. Further upgrades to the system can make it usable for Automated Storage and Retrieval System (ASRS) applications in warehouses/factories.

After a long period of stagnation in the last decades of 20th Century Russia started investing into development of IT and engineering education. In 2017 Kazan Federal University launched a novel master program in Intelligent Robotics, which was established based on the analysis of curriculums of world leading universities in robotics field and is being taught in English language. The paper presents result of surveys that were conducted among first year students of Robot Operating System (ROS) course, which serves as a basic backbone of the program. We analyzed dynamics of English language comprehension, self-efficiency, and active learning strategies among students within a semester while highlighting their attitude and motivation to study ROS as a part of robotics education. Special language environment was created during the class that allowed students to obtain positive results of material comprehension in English language. Additionally, a significant increase of motivation in studying robotics was observed among the students.

Robot systems are growing in importance and complexity. Ecosystems for robot software, such as the Robot Operating System (ROS), provide libraries of reusable software components that can be configured and composed into larger systems. To support compositionality, ROS uses late binding and architecture configuration via “launch files” that describe how to initialize the components in a system. However, late binding often leads to systems failing silently due to misconfiguration, for example by misrouting or dropping messages entirely.In this paper we present ROSDiscover, which statically recovers the run-time architecture of ROS systems to find such architecture misconfiguration bugs. First, ROSDiscover constructs component level architectural models (ports, parameters) from source code. Second, architecture configuration files are analyzed to compose the system from these component models and derive the connections in the system. Finally, the reconstructed architecture is checked against architectural rules described in first-order logic to identify potential misconfigurations.We present an evaluation of ROSDiscover on real world, off-the-shelf robotic systems, measuring the accuracy, effectiveness, and practicality of our approach. To that end, we collected the first data set of architecture configuration bugs in ROS from popular open-source systems and measure how effective our approach is for detecting configuration bugs in that set.

This paper discusses the feasibility of using the framework Robot Operating System (ROS) as a basis for the development of a middleware in the Internet of Things (IoT) context. The main concepts regarding IoT and ROS are presented and followed by the evaluation criteria. Finally, case studies considering the ROS features and some common assumptions of IoT environments are analyzed.Key words: IoT, distributed systems, ROS, Internet of Things.

An ever-increasing number of robotic applications is being created based on the functionalities and the communication facilities offered by the Robotic Operating System (ROS). When applications involve contact with humans, such as in some transportation tasks, a robot supervisory controller shall guarantee that in every possible circumstance safety and task requirements are satisfied. There is no framework provided by ROS to specify supervisory controllers with formal guarantees about the satisfaction of requirements. This is problematic when the complexity of the controller increases, and it is not viable to test every possible scenario during the validation stage of the robotic application. This paper presents novel work that aims at enabling supervisory controller synthesis for ROS-based applications by (a) proposing a novel mapping between concepts from supervisory control theory and ROS, (b) demonstrating that a supervisory control modeling language exists that can be used to describe plant models and to specify requirements and (c) presenting generic finite-state automata models of ROS components, regarded as plant models, which are input for the synthesis procedure. The approach is demonstrated by synthesizing the controller to coordinate state-of-the-art mobile robot navigation modules. Simulations and real-life experiments show that the generated supervisory controller guarantees the correctness of the model with respect to the requirements and that the execution time of a control loop, including the binding code between the supervisory controller and ROS, is on average short enough to be deployed in high-level control loops. The paper is accompanied by a public code repository that contains the implementation of all methods presented in the paper including simulation environments to ease the replication of the results presented.

In this paper, we describe the Evo-ROS framework, which is intended to help bridge the gap between the evolutionary and traditional robotics communities. Evo-ROS combines an evolutionary algorithm with individual physics-based evaluations conducted using the Robot Operating System (ROS) and the Gazebo simulation environment. Our goals in developing Evo-ROS are to (1) provide researchers in evolutionary robotics with access to the extensive support for real-world components and capabilities developed by the ROS community and (2) enable ROS developers, and more broadly robotics researchers, to take advantage of evolutionary search during design and testing. We describe the details of the Evo-ROS structure and operation, followed by presentation of a case study using Evo-ROS to optimize placement of sonar sensors on unmanned ground vehicles that can experience reduced sensing capability due to component failures and physical damage. The case study provides insights into the current capabilities and identifies areas for future enhancements.

In this paper, two algorithms are proposed to navigate the mini unmanned aerial vehicle (MUAV) in unknown environment. The first algorithm is path planning algorithm, it proposed to force the MUAV to track a specific trajectory. The second algorithm is obstacle avoidance algorithm, it proposed to avoid the collision between the MUAV and borrower when the MUAV is maneuvering in unknown environment. The pure pursuit algorithm (PPA) is used to force the MUAV to follow the desired trajectory depends on the given waypoints. While the vector field histogram algorithm (VFHA) is used to avoid collision of the MUAV with obstacles block in the desired path. To keep the flight of MUAV without collision, the 3D laser sensors are used to collect the information from the surrounding environment. The GPS sensor is used to keep the MUAV on the specific path. The collected information from the two sensors are analysed by the controller of the MUAV to improve its performance. The Matlab program is used to implement the proposed algorithms and plot the trajectories of MUAV in the unknown algorithm. The Matlab program is connected with the gazebo program via robotic operating system (ROS) to simulate the visual movement of the MUAV in any unknown environment. The obtained results show the effectiveness of the proposed algorithm to flight the MUAV in different environments.