An FPGA-based controller for collaborative robotics

The use of robots is becoming more common in society. Industrial robots are being developed to work with people, and lower-force collaborative robots are being developed to help people in their everyday lives. These may need fast and sophisticated motion control and behavioral algorithms, but are expected to be more compact and lower cost. This paper proposes a processor plus FPGA solution for the control systems for such robots, where the FPGA performs all real-time tasks, freeing the processor to run lower-frequency high level control and interface to other devices such as camera systems. A demonstrator robot is designed, combining multi-axis motion control with 3D robot vision

A High Performance Fuzzy Logic Architecture for UAV Decision Making

The majority of Unmanned Aerial Vehicles (UAVs) in operation today are not truly autonomous, but are instead reliant on a remote human pilot. A high degree of autonomy can provide many advantages in terms of cost, operational resources and safety. However, one of the challenges involved in achieving autonomy is that of replicating the reasoning and decision making capabilities of a human pilot. One candidate method for providing this decision making capability is fuzzy logic. In this role, the fuzzy system must satisfy real-time constraints, process large quantities of data and relate to large knowledge bases. Consequently, there is a need for a generic, high performance fuzzy computation platform for UAV applications. Based on Lees’ [1] original work, a high performance fuzzy processing architecture, implemented in Field Programmable Gate Arrays (FPGAs), has been developed and is shown to outclass the performance of existing fuzzy processors

CORBYS cognitive control architecture for robotic follower

In this paper the novel generic cognitive robot control architecture CORBYS is presented. The objective of the CORBYS architecture is the integration of high-level cognitive modules to support robot functioning in dynamic environments including interacting with humans. This paper presents the preliminary integration of the CORBYS architecture to support a robotic follower. Experimental results on high-level empowerment-based trajectory planning have demonstrated the effectiveness of ROS-based communication between distributed modules developed in a multi-site research environment as typical for distributed collaborative projects such as CORBYS

DeepPicar: A Low-cost Deep Neural Network-based Autonomous Car

We present DeepPicar, a low-cost deep neural network based autonomous car platform. DeepPicar is a small scale replication of a real self-driving car called DAVE-2 by NVIDIA. DAVE-2 uses a deep convolutional neural network (CNN), which takes images from a front-facing camera as input and produces car steering angles as output. DeepPicar uses the same network architecture---9 layers, 27 million connections and 250K parameters---and can drive itself in real-time using a web camera and a Raspberry Pi 3 quad-core platform. Using DeepPicar, we analyze the Pi 3's computing capabilities to support end-to-end deep learning based real-time control of autonomous vehicles. We also systematically compare other contemporary embedded computing platforms using the DeepPicar's CNN-based real-time control workload. We find that all tested platforms, including the Pi 3, are capable of supporting the CNN-based real-time control, from 20 Hz up to 100 Hz, depending on hardware platform. However, we find that shared resource contention remains an important issue that must be considered in applying CNN models on shared memory based embedded computing platforms; we observe up to 11.6X execution time increase in the CNN based control loop due to shared resource contention. To protect the CNN workload, we also evaluate state-of-the-art cache partitioning and memory bandwidth throttling techniques on the Pi 3. We find that cache partitioning is ineffective, while memory bandwidth throttling is an effective solution.Comment: To be published as a conference paper at RTCSA 201

Robot Teardown, Stripping Industrial Robots for Good

Building a robot requires a careful selection of components that interact across networks while meeting timing deadlines. Given the complexity associated, as robots get damaged or security compromised, their components will increasingly require updates and replacements. Contrary to the expectations and similar to Ford in the 1920s with cars, most robot manufacturers oppose to this. They employ planned obsolescence practices organizing dealers and system integrators into "private networks", providing repair parts only to "certified" companies to discourage repairs and evade competition. In this article, we introduce and advocate for robot teardown as an approach to study robot hardware architectures and fuel security research. We show how teardown can help understanding the underlying hardware and demonstrate how our approach can help researchers uncovering security vulnerabilities. Our case studies show how robot teardown becomes an essential practice to security in robotics, helping us identify and report a total of 100 security flaws with 17 new CVE IDs over a period of two years. Lastly, we finalize by demonstrating how, through teardown, planned obsolescence hardware limitations can be identified and bypassed obtaining full control of the hardware, which poses both a threat to the robot manufacturers' business model as well as a security threat

Robot Teardown, Stripping Industrial Robots for Good

Building a robot requires a careful selection of components that interact across networks while meeting timing deadlines. Given the complexity associated, as robots get damaged or security compromised, their components will increasingly require updates and replacements. Contrary to the expectations and similar to Ford in the 1920s with cars, most robot manufacturers oppose to this. They employ planned obsolescence practices organizing dealers and system integrators into "private networks", providing repair parts only to "certified" companies to discourage repairs and evade competition. In this article, we introduce and advocate for robot teardown as an approach to study robot hardware architectures and fuel security research. We show how teardown can help understanding the underlying hardware and demonstrate how our approach can help researchers uncovering security vulnerabilities. Our case studies show how robot teardown becomes an essential practice to security in robotics, helping us identify and report a total of 100 security flaws with 17 new CVE IDs over a period of two years. Lastly, we finalize by demonstrating how, through teardown, planned obsolescence hardware limitations can be identified and bypassed obtaining full control of the hardware, which poses both a threat to the robot manufacturers' business model as well as a security threat

RobotPerf: An Open-Source, Vendor-Agnostic, Benchmarking Suite for Evaluating Robotics Computing System Performance

We introduce RobotPerf, a vendor-agnostic benchmarking suite designed to evaluate robotics computing performance across a diverse range of hardware platforms using ROS 2 as its common baseline. The suite encompasses ROS 2 packages covering the full robotics pipeline and integrates two distinct benchmarking approaches: black-box testing, which measures performance by eliminating upper layers and replacing them with a test application, and grey-box testing, an application-specific measure that observes internal system states with minimal interference. Our benchmarking framework provides ready-to-use tools and is easily adaptable for the assessment of custom ROS 2 computational graphs. Drawing from the knowledge of leading robot architects and system architecture experts, RobotPerf establishes a standardized approach to robotics benchmarking. As an open-source initiative, RobotPerf remains committed to evolving with community input to advance the future of hardware-accelerated robotics