A Motion Planning Processor on Reconfigurable Hardware

Motion planning algorithms enable us to find feasible paths for moving objects. These algorithms utilize feasibility checks to differentiate valid paths from invalid ones. Unfortunately, the computationally expensive nature of such checks reduces the effectiveness of motion planning algorithms. However, by using hardware acceleration to speed up the feasibility checks, we can greatly enhance the performance of the motion planning algorithms. Of course, such acceleration is not limited to feasibility checks; other components of motion planning algorithms can also be accelerated using specially designed hardware. A Field Programmable Gate Array (FPGA) is a great platform to support such an acceleration. An FPGA is a collection of digital gates which can be reprogrammed at run time, i.e., it can be used as a CPU that reconfigures itself for a given task. In this paper, we study the feasibility of an FPGA based motion planning processor and evaluate its performance. In order to leverage its highly parallel nature and its modular structure, our processor utilizes the probabilistic roadmap method at its core. The modularity enables us to replace the feasibility criteria with other ones. The reconfigurability lets us run our processor in different roles, such as a motion planning co-processor, an autonomous motion planning processor or dedicated collision detection chip. Our experiments show that such a processor is not only feasible but also can greatly increase the performance of current algorithms

Development of system supervision and control software for a micromanipulation system

This paper presents the realization of a modular software architecture that is capable of handling the complex supervision structure of a multi degree of freedom open architecture and reconfigurable micro assembly workstation. This software architecture initially developed for a micro assembly workstation is later structured to form a framework and design guidelines for precise motion control and system supervision tasks explained subsequently through an application on a micro assembly workstation. The software is separated by design into two different layers, one for real-time and the other for non-realtime. These two layers are composed of functional modules that form the building blocks for the precise motion control and the system supervision of complex mechatronics systems

Enhancing Microcomputer Edge Computing for Autonomous IoT Motion Control

Devices microprocessors, microcontrollers, and Field Programmable Gate Arrays (FPGA) play the core rule at the IoT edge level and it should be right provisioned. For proper controller performance, control algorithms should be implemented near the actuator eliminating the delay effects. In the IoT domain, this means to implement the mentioned algorithm at the edge level and prior data transmitting. The efficient IoT-enabled motion control can be obtained by considering two main factors; the first factor is from the actuator design point of view and the second factor is from the controller performance point of view. Therefore, in this article, the two mentioned factors are treated concerning the microprocessor rule and importance as a core for proper prototype design and as the main platform to implement the control algorithms. A comparison of controller performance indices for both prototypes is done using previously distributed motion control schemes and newly developed schemes after tuning the respective schemes gains in an optimal manner. The scheme with better behavior of both prototypes are selected for the IoT integration process, this scheme ensures optimal edge computing for the distributed motion control, making the implementation of all control computation take place at the IoT-edge level. As a result, the dynamic pipeline stages (DPS) based prototype gives better controller performance indices for most strategies, less power consumption, and optimally utilized resources encouraging the use of the microprocessors with reconfigurable components at the IoT-edge level

Transfer Learning-Based Crack Detection by Autonomous UAVs

Unmanned Aerial Vehicles (UAVs) have recently shown great performance collecting visual data through autonomous exploration and mapping in building inspection. Yet, the number of studies is limited considering the post processing of the data and its integration with autonomous UAVs. These will enable huge steps onward into full automation of building inspection. In this regard, this work presents a decision making tool for revisiting tasks in visual building inspection by autonomous UAVs. The tool is an implementation of fine-tuning a pretrained Convolutional Neural Network (CNN) for surface crack detection. It offers an optional mechanism for task planning of revisiting pinpoint locations during inspection. It is integrated to a quadrotor UAV system that can autonomously navigate in GPS-denied environments. The UAV is equipped with onboard sensors and computers for autonomous localization, mapping and motion planning. The integrated system is tested through simulations and real-world experiments. The results show that the system achieves crack detection and autonomous navigation in GPS-denied environments for building inspection

Retrofit Reconfigurable Flight Control System and the F/A-18C

The United States Navy has completed the initial flight test of a Reconfigurable Control Law System (RCLAWS) on the F/A-18C. The purpose of reconfigurable control is to allow for the safe operation of an aircraft that has experienced a sudden change in aircraft dynamics resulting from aircraft damage or flight control effector damage. The RCLAWS utilized during this flight test are novel in that they are designed to augment the production flight control system instead of replacing it. In order to reduce verification and certification requirements, this retrofit reconfigurable methodology supplements pilot commands to compensate for undesirable aircraft dynamics instead of manipulating control surfaces directly. Through comparison of the aircraft’s actual response to model data of the aircraft’s desired response, the RCLAWS determines what commands need to be applied to produce the desired aircraft response. Flight test data have been collected to determine the viability of the in-line retrofit reconfigurable control method. Although flight data indicate a modest improvement within the limited flight test envelope, simulation analysis has indicated that the retrofit RCLAWS provide substantial improvements for more aggressive failures. Simulation shows RCLAWS has proven to reduce the aircrew workload in a recent catastrophic failure present in the F/A-18 community and provide predictable aircraft dynamics for a safe recovery

Development and validation of the crew-station system-integration research facility

The various issues associated with the use of integrated flight management systems in aircraft were discussed. To address these issues a fixed base integrated flight research (IFR) simulation of a helicopter was developed to support experiments that contribute to the understanding of design criteria for rotorcraft cockpits incorporating advanced integrated flight management systems. A validation experiment was conducted that demonstrates the main features of the facility and the capability to conduct crew/system integration research