Persistent operation of mobile robots

Success of numerous unmanned mobile missions in space, air, ground, and water is measured by the ability of the robots to usefully operate for extended time in dynamic and uncertain environments. This talk will provide an overview of the recent progress towards performing autonomous long-term missions. The approach includes task and energy routing scheduling, efficient path planning and coordination, and low-infrastructure platforms. The goal is to provide practical solutions by lowering deployment and operating costs, while also increasing efficiency, endurance and persistence during complex missions like disaster responses and long-term science discoveries.https://digitalcommons.mtu.edu/techtalks/1026/thumbnail.jp

Neural Network-PSO-based Velocity Control Algorithm for Landing UAVs on a Boat

Precise landing of Unmanned Aerial Vehicles (UAVs) onto moving platforms like Autonomous Surface Vehicles (ASVs) is both important and challenging, especially in GPS-denied environments, for collaborative navigation of heterogeneous vehicles. UAVs need to land within a confined space onboard ASV to get energy replenishment, while ASV is subject to translational and rotational disturbances due to wind and water flow. Current solutions either rely on high-level waypoint navigation, which struggles to robustly land on varied-speed targets, or necessitate laborious manual tuning of controller parameters, and expensive sensors for target localization. Therefore, we propose an adaptive velocity control algorithm that leverages Particle Swarm Optimization (PSO) and Neural Network (NN) to optimize PID parameters across varying flight altitudes and distinct speeds of a moving boat. The cost function of PSO includes the status change rates of UAV and proximity to the target. The NN further interpolates the PSO-founded PID parameters. The proposed method implemented on a water strider hexacopter design, not only ensures accuracy but also increases robustness. Moreover, this NN-PSO can be readily adapted to suit various mission requirements. Its ability to achieve precise landings extends its applicability to scenarios, including but not limited to rescue missions, package deliveries, and workspace inspections

Reinforcement learning-based multi-AUV adaptive trajectory planning for under-ice field estimation

This work studies online learning-based trajectory planning for multiple autonomous underwater vehicles (AUVs) to estimate a water parameter field of interest in the under-ice environment. A centralized system is considered, where several fixed access points on the ice layer are introduced as gateways for communications between the AUVs and a remote data fusion center. We model the water parameter field of interest as a Gaussian process with unknown hyper-parameters. The AUV trajectories for sampling are determined on an epoch-by-epoch basis. At the end of each epoch, the access points relay the observed field samples from all the AUVs to the fusion center, which computes the posterior distribution of the field based on the Gaussian process regression and estimates the field hyper-parameters. The optimal trajectories of all the AUVs in the next epoch are determined to maximize a long-term reward that is defined based on the field uncertainty reduction and the AUV mobility cost, subject to the kinematics constraint, the communication constraint and the sensing area constraint. We formulate the adaptive trajectory planning problem as a Markov decision process (MDP). A reinforcement learning-based online learning algorithm is designed to determine the optimal AUV trajectories in a constrained continuous space. Simulation results show that the proposed learning-based trajectory planning algorithm has performance similar to a benchmark method that assumes perfect knowledge of the field hyper-parameters

Desarrollo y evaluación de prototipos de sustitutos óseos acelulares generados por bioingeniería de tejidos para resolución de lesiones críticas de hueso y osteogénesis

Los injertos de hueso autólogo (hueso del propio paciente) u homólogo (hueso de donante humano cadavérico) son alternativas terapéuticas en casos de pérdida de tejido óseo, dado que proveen a la lesión de células osteogénicas y factores osteoinductivos y osteogénicos. Sin embargo, estas estrategias terapéuticas tienen limitaciones: los injertos de tejido óseo homólogo puedan transmitir enfermedades infecciosas y la utilización de hueso autólogo (que normalmente se toma de cresta ilíaca) genera, por un lado, una nueva herida cruenta, con la morbilidad asociada, y por otro puede no resultar suficiente. Es decir que estas limitaciones los hacen inaplicables en ciertos casos. La utilización de implantes metálicos o cerámicos exhiben una pobre integración, entre otros inconvenientes. Por ello, es necesario desarrollar nuevas estrategias que permitan generar sustitutos óseos por bioingeniería, lo cual implica contar con una matriz, biocompatible, reabsorbible, con una porosidad y tamaño de poro que permitan la invasión celular y la libre difusión de nutrientes, desechos y gases, y que además sean osteoinductivos y osteoconductivos. El objetivo del presente proyecto es la generación de un sustituto óseo libre de células a través del uso de matrices de colágeno cargadas con BMP-2, IMT-504 o una combinación de ambos que permita la regeneración ósea en lesiones críticas de hueso en un modelo de calota de rata. También se propone evaluar estas construcciones con un molde de nanocelulosa como retardador de fibrosis en un modelo de osteogénesis guiada en calota de rata. En el caso de regeneración ósea, se ensayaron 4 grupos experimentales (Membrana Hellitape + BMP-2, Membrana Hellitape + ODN IMT-504, Hellitape + BMP-2 + ODN IMT-504, Esponja BIOPAD + ODN IMT-504), 2 controles de membranas (Hellitape Control y BIOPAD Control) y un control de lesión sin injerto (Control absoluto). Los estudios radiográficos a 10 días no mostraron diferencias significativas en el cierre de la herida entre los distintos grupos. 40 días poscirugía, todos los grupos mostraron una disminución significativa de la lesión respecto a los 10 días. Si comparamos los tratamientos a 40 días, solo el grupo Hellitape-BMP2,y su control, mostraron un mejor cierre de lesión que el control absoluto. Por el contrario, la presencia del ODN IMT-504 empeoró el cierre de la herida respeto del grupo control Hellitape. Este efecto fue parcialmente revertido por BMP2 en el grupo Hellitape BMP2+ODNIMT504. El grupo BIOPAD + ODN IMT-504 y su Control BIOPAD mostraron resultados similares al control absoluto. Esto parece mostrar que BMP2 mejora el cierre de la herida, mientras que el ODN IMT-504 mostraría el efecto contrario. Los estudios histológicos mostraron que la presencia de BMP-2 incrementó notablemente la performance de la matriz, mejorando posiblemente el efecto osteoconductor y osteoinductor. La presencia de hueso nuevo fue continua y de estructura más ordenada a lo largo de la lesión, comparada con el control absoluto, que solo mostró formación de hueso nuevo en los bordes, y respecto del control Hellitape, que mostró “islas” de hueso nuevo. Por último, el ODN IMT-504 mostró una peor regeneración ósea que el control absoluto, posiblemente debido a un efecto antiinflamatorio en su aplicación tópic

Effective Turning Motion Control of Internally Actuated Autonomous Underwater Vehicles

This paper presents a novel roll mechanism and an efficient control strategy for internally actuated autonomous underwater vehicles (AUVs). The developed control algorithms are tested on Michigan Tech’s custom research glider, ROUGHIE (Research Oriented Underwater Glider for Hands-on Investigative Engineering), in a controlled environment. The ROUGHIE’s design parameters and operational constraints were driven by its requirement to be man portable, expandable, and maneuverable in shallow water. As an underwater glider, the ROUGHIE is underactuated with direct control of only depth, pitch, and roll. A switching control method is implemented on the ROUGHIE to improve its maneuverability, enabling smooth transitions between different motion patterns. This approach uses multiple feedforward-feedback controllers. Different aspects of the roll mechanism and the effectiveness of the controller on turning motion are discussed based on experimental results. The results illustrate that the ROUGHIE is capable of achieving tight turns with a radius of 2.4 meters in less than 3 meters of water, or one order of magnitude improvement on existing internally actuated platforms. The developed roll mechanism is not specific to underwater gliders and is applicable to all AUVs, especially at lower speeds and in shallower water when external rudder is less effective in maneuvering the vehicle

Robustness of orthogonal eigenstructure control to actuators failure

Orthogonal eigenstructure control (OEC) is a feedback control method applicable to multi-input multi-output linear systems. While the available control design methodologies offer a large and complex design space of options that can often overwhelm a designer, this control method offers a significant simplification of the design task while still allowing some experience-based design freedom. In this chapter, the robustness of the method to the failure of the actuators was investigated. It was shown the control gain was capable of controlling the systems during an actuator failure, as OEC generates the control gain by maintaining the closed-loop eigenvectors within the achievable eigenvectors set. A system of lumped masses was used to explain the method; then, the problem of failed actuators in the vibration control of a plate was investigated. Finite element analysis was used for modeling the plate to simulate the dynamic behavior of the system. Five cases were considered and the suppression of the vibration in a plate with three working actuators was compared to the performance of a similar control system with a failed actuator. Also, the behaviors of the system with failed actuators were compared to the systems that were designed to operate with lesser control actuators. It was shown that the number of closed-loop eigenvalue pairs that moved from the cluster of the open-loop poles was equal to the number of working actuators. The closed-loop poles in all the systems were moved to the vicinity of one specific area, generating a break frequency with sufficient damping for robust active vibration control

Simulation-driven optimization of underwater docking station design

This paper presents the optimization of a novel docking design for docking and charging of autonomous underwater vehicles. The docking design has been optimized to maximize the capture envelope and minimize the maximum contact force. Two design parameters, sweep angle and ramp angle, were optimized as was the velocity during terminal homing and capture. The optimization was an exhaustive optimization with 5600 unique simulations completed that included the vehicle hydrodynamics, impact dynamics, and controller. Unique to the presented docking design is the ability to support a wide variety of vehicles from different size classes through its simplified funnel design and use of a docking adapter as validated in simulation with a Dolphin II AUV and Bluefin SandShark. The only part of the docking system that contacts the vehicle is a standardized docking adapter that is meant as a drop-in replacement for an antenna mast. The presented docking system is low-cost, compact, and can be deployed in a wide variety of situations including mobile docking

Autonomous underwater pipeline monitoring navigation system

This paper details the development of an autonomous motion-control and navigation algorithm for an underwater autonomous vehicle, the Ocean Server IVER3, to track long linear features such as underwater pipelines. As part of this work, the Nonlinear and Autonomous Systems Laboratory (NAS Lab) developed an algorithm that utilizes inputs from the vehicles state of the art sensor package, which includes digital imaging, digital 3-D Sidescan Sonar, and Acoustic Doppler Current Profilers. The resulting algorithms should tolerate real-world waterway with episodic strong currents, low visibility, high sediment content, and a variety of small and large vessel traffic. © 2014 SPIE

Autonomous Oil Spill Detection: Mission Planning for ASVs and AUVs with Static Recharging

© 2018 IEEE. In this paper, a mission planning method for oil spill detection using multiple Autonomous Underwater Vehicles (AUVs) and Autonomous Surface Vehicles (ASVs) is presented. Deploying multiple marine robots provides the opportunity for efficient autonomous water sampling and leak detection. Considering the large operation area of monitoring and survey missions, the endurance of marine robots and manual recharging is a big limitation. In this work, the energy constraints are considered and a planning approach for battery recharging using charging stations is proposed. The proposed method uses a Genetic Algorithm (GA) to optimize the trajectories of ASVs and AUVs together with the locations of charging stations to minimize the mission completion time and energy cost. A realistic mission scenario is simulated to test the performance and show the capabilities. The presented mission planning algorithm is adaptable to different mission scales, environmental constraints, vehicle types and counts