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Industrial motion control systems, e.g. pick-andplace tasks in semiconductor manufacturing equipment, require precise positioning for achieving high machine throughput. Linear encoders are the standard industrial sensors used for position feedback due to their relatively low cost, high resolution, and high operating frequency. The challenge is that the linear encoders measure the positions at the points-ofcontrol of the equipment, e.g. motors, and not at the points-ofinterest, e.g. pick-and-place positions. The coupling between a point-of-control and the point-of-interest is affected by external disturbances such as mechanical misalignment of the product, friction, and warping of the material, and linear encoders fail to sense these disturbances. Vision-based sensing is a potential alternative to achieve robust sensing and high-precision control. However, vision processing has a long computational delay and affects the machine throughput. In this paper, we propose a multi-rate multi-sensor fusion approach to improve the positioning accuracy of industrial motion control systems with different points-of-control and points-of-interest. We present a multi-rate Kalman filter with bias correction to fuse accurate but slow and delayed vision sensor data with fast but less accurate linear encoder data for highprecision position control. We validate the proposed method in an evaluation framework by considering an industrial case study of a semiconductor die-bonding machine. A design-space exploration is done to evaluate the performance of the proposed solution with respect to various relevant design parameters. The effectiveness of the proposed solution depends on the type of disturbances and vision processing delay. For the parameter range under consideration, we achieve a positioning accuracy of 1μm.

Robot Operating System (ROS) is a de-facto standard robot middleware in many academic and industrial use cases. However, utilizing ROS/ROS2 in safety-critical embedded applications with real-time requirement is challenging because of C1) Non-real-time underlying hardware, C2) No control on the host OS scheduler, C3) Unpredictable dynamic memory allocation, C4) High resource requirement, and C5) Unpredictable execution model for ROS nodes. In this paper, we address these limiting factors by proposing a hardware-software architecture -CompROS- for ROS2 based robotic development in a Multi-Processor System on Chip (MPSoC) platform that. The proposed hardware architecture consists of a Hard Real-Time (HRT) RISC-V based subsystem implemented in the Programmable Logic (PL) part of the MPSoC platform, a Soft Real-Time (SRT) ARM-based subsystem in the Processing System (PS) part of the MPSoC platform, and a Non-Real-Time (NRT) PC. While the proposed hardware architecture along with a partitioning layer overcomes the first two limiting factors, the rest are managed by the proposed multi-layer software architecture. We make a bare-metal implementation of XRCE-DDS standard for PL-PS communication, while peer-to-peer PL-PL communication is done through a proposed real-time publish-subscribe approach. The reliable communication for PS-PLL communication is done through utilizing C-HEAP protocol. Further, we integrate ROS2 software layers on top of the proposed hardware and software layers. Finally, with respect to C5, we present a real-time execution model of ROS2 nodes by a mapping of ROS2 entities to CompROS entities, which is validated through experimental results. We run ROS2 middleware with an executable size of less than 200 KB on an MPSoC platform.

Image-based control (IBC) systems use camera sensor(s) to perceive the environment. The inherent compute-heavy nature of image processing causes long processing delay that negatively influences the performance of the IBC systems. Our idea is to reduce the long delay using coarse-grained approximation of the image signal processing pipeline without affecting the functionality and performance of the IBC system. The question is: how is the degree of approximation related to the closed-loop quality-of-control (QoC), memory utilization and energy consumption? We present a software-in-the-loop (SiL) evaluation framework for the above approximation-in-the-loop system. We identify the error resilient stages and the corresponding coarse-grained approximation settings for the IBC system. We perform trade off analysis between the QoC, memory utilisation and energy consumption for varying degrees of coarse-grained approximation. We demonstrate the effectiveness of our approach using a concrete case study of a lane keeping assist system (LKAS). We obtain energy and memory reduction of upto 84% and 29% respectively, for 28% QoC improvements.

This paper presents a model-based PIL simulation framework targeting multi-core multi-application FPGA-based embedded platforms. The process from model-based simulations to implementing on the platform requires a target specified code generation, compile and execution. The presented framework is able to automatically go through this process and perform the PIL simulation starting from a model-based environment in particular Simulink. It is also able to consider the multi-application nature of the target platform, executing the PIL simulation without interfering other applications. We validate the functionality of the PIL framework by testing a control systems application, using various PIL configurations.

Image Processing (IP) applications have become popular with the advent of efficient algorithms and low-cost CMOS cameras with high resolution. However, IP applications are compute-intensive, consume a lot of energy and have long processing times. Image approximation has been proposed by recent works for an energy-efficient design of these applications. It also reduces the impact of long processing times. The challenge here is that the IP applications often work as a part of bigger closed-loop control systems, e.g. advanced driver assistance system (ADAS). The impact of image approximations that tolerate certain error on these image-based control (IBC) systems is very important. However, there is a lack of tool support to evaluate the performance of such closed-loop IBC systems when the IP is approximated. We propose a framework - for both software-in-the-loop (SiL) and hardware-in-the-loop (HiL) simulation - for performance evaluation of image approximation on a closed-loop automotive IBC system (IMACS). Both simulation setups model the 3D environment in 3ds Max, and simulate the system dynamics, camera position and environment in V-REP. Our SiL setup simulates the system software in C++ or Matlab. Here, V-REP runs as a server and the software as a client in synchronous mode. Our HiL simulation setup runs the system software in the NVIDIA Drive PX2 platform and communicates to V-REP using application programming interfaces (APIs) for synchronous execution. We show the effectiveness of our framework using a vision-based lateral control example.

Modular design is a common practice when designing complex applications for embedded systems. Another important practice in the embedded systems domain is the use of abstract models to realize predictable behaviour. Modular model-based design allows to construct a modular model of a complex system via model composition. The model of computation considered in this paper is scenario-aware dataflow, a dataflow model that allows for dynamic behaviour. We model applications with behaviour that changes according to a periodic pattern. Composing models with periodic patterns results in a model with a periodic pattern with a common hyper-period. We propose an efficient algorithmic method to compose cyclo-static scenario-aware dataflow models by generating composite patterns in a concise representation. We show that our approach can automatically generate concise models of several real-life image processing applications.

Feedback control applications are robust to occasional deadline misses. This opens up the possibility of saving scarce (computation and communication) resources on embedded platforms. Stability and performance requirements of a control loop impose restrictions on acceptable patterns of deadline misses (e.g., not too many misses in a row). Such requirements are captured by (m,k)-firmness conditions. That is, at least m control computation jobs must meet deadlines in any k consecutive jobs. (m,k)-firm design requires (i) representation of stability and performance requirements in terms of (m,k)-firm deadlines (ii) controller synthesis taking into account the (m,k)-firmness parameters (iii) schedule analysis to verify guarantees on meeting the firmness conditions. We present a co-synthesis framework for these three design components and illustrate its applicability with examples. © 2018 IEEE.

Vehicle platooning has gained attention for its potential to achieve an increased road capacity and safety, and a higher fuel efficiency. Member vehicles of a platoon wirelessly communicate complying with industrial standards such as IEEE 802.11p. By exchanging information with other members via wireless communication, a platoon member computes its desired acceleration which is then passed on to the engine control system via in-vehicle network to physically realize the acceleration. This leads to a multi-layer control scheme. The upper-layer is influenced by the behavior of 802.11p communication and network congestion due to transmissions by other vehicles in the traffic. The lower-layer engine control loop communicates over the fast and reliable in-vehicle networks (e.g., FlexRay, Ethernet). Design of the overall system therefore depends on (i) the characteristics of 802.11p-based communication (ii) the nature of the traffic (iii) the control algorithms running at the two layers. We present a cosimulation framework consisting of Matlab (for the multi-layer control algorithms), ns-3 (for the 802.11p network) and SUMO (for the traffic behavior). The framework can be used to validate different platooning setups. As an illustrative case study, we consider a multi-layer control strategy where the upper-layer uses Model Predictive Control (MPC) at a rate in compliance with 802.11p and the lower-layer uses statefeedback control at a higher sampling rate in line with in-vehicle networking capabilities. The control strategy is evaluated considering various realistic traffic and network congestion scenarios. © 2018 IEEE.

SUMMARYThe postural stability of bipedal robots is investigated in perspective of foot-rotation during locomotion. With foot already rotated, the biped is modeled as an underactuated kinematic structure. The stability of such biped robots is analyzed by introducing the concept ofrotational stability. Therotational stabilityinvestigates whether a biped would lead to a flat-foot posture or the biped would topple over. Therotational stabilityis quantified as a ground reference point named “rotational stability index (RSI)” point. Conditions are established to achieverotational stabilityduring biped locomotion using the concept of the RSI point. The applicability of the RSI point is illustrated through experimentation for the landing stability analysis of the bipedal jumping gaits.The traditional stability criteria such as zero-moment point (ZMP) [M. Vukobratovic and B. Borovac, “Zero-moment point – thirty five years of its life,”Int. J. Humanoid Robot. 1(1), 157–173 (2004)] and foot-rotation indicator (FRI) [A. Goswami, “Postural stability of biped robots and the foot-rotation indicator (FRI) point,”Int. J. Robot. Res. 18(6), 523–533 (1999)] are not applicable to analyze biped's postural stability when foot is already rotated. The RSI point is established as a stability criteria for planar bipedal locomotion in presence of foot rotation.

SUMMARYThe inverse kinematics of a 12 degrees-of-freedom (DOFs) biped robot is formulated in terms of certain parameters. The biped walking gaits are developed using the parameters. The walking gaits are optimized using genetic algorithm (GA). The optimization is carried out considering relative importance of stability margin and walking speed. The stability margin depends on the position of zero-moment-point (ZMP) while walking speed varies with step-size. The ZMP is computed by an approximation-based method which does not require system dynamics. The optimal walking gaits are experimentally realized on a biped robot.

SUMMARYA novel method of Zero-Moment-Point (ZMP) compensation is proposed to improve the stability of locomotion of a biped, which is subjected to disturbances. A compensating torque is injected into the ankle-joint of the foot of the robot to improve stability. The value of the compensating torque is computed from the reading of the force sensors located at the four corners of each foot. The effectiveness of the method is verified on a humanoid robot, MANUS-I. With the compensation technique, the robot successfully rejected disturbances in different forms. It carried an additional weight of 390 gm (17% of body weight) while walking. Also, it walked up a 10° slope and walked down a 3° slope.