AUTONOMOUS POWER DISTRIBUTION SYSTEMS

Using robotic systems for many missions that require power distribution can decrease the need for human intervention in such missions significantly. For accomplishing this capability a robotic system capable of autonomous navigation, power systems adaptation, and establishing physical connection needs to be developed. This thesis presents developed path planning and navigation algorithms for an autonomous ground power distribution system. In this work, a survey on existing path planning methods along with two developed algorithms by author is presented. One of these algorithms is a simple path planner suitable for implementation on lab-size platforms. A navigation hierarchy is developed for experimental validation of the path planner and proof of concept for autonomous ground power distribution system in lab environment. The second algorithm is a robust path planner developed for real-size implementation based on lessons learned from lab-size experiments. The simulation results illustrates that the algorithm is efficient and reliable in unknown environments. Future plans for developing intelligent power electronics and integrating them with robotic systems is presented. The ultimate goal is to create a power distribution system capable of regulating power flow at a desired voltage and frequency adaptable to load demands

Underwater multi-robot persistent area coverage mission planning

© 2016 IEEE. The search mission for the missing MH370 airplane demonstrated that an autonomous underwater vehicle (AUV) managed by a crew is a reliable resource for critical missions. However, it highlighted the cost associated with human support in robotic operations. This paper presents a mission planning strategy that takes mission constraints such as number of available AUVs and their characteristics, as well as charging resources available (both energy-carrying agents and charging stations) to generate a set of efficient trajectories for AUVs and locations for either energy-carrying agents rendezvous or charging stations placement. The goal is to ensure efficient use of the resources and reduce operational costs

Learning Navigation Tasks from Demonstration for Semi-Autonomous Remote Operation of Mobile Robots

© 2018 IEEE. Mobile robots are valuable tools for search and rescue missions, especially in hazardous or inaccessible areas. These systems have the potential to address a wide variety of tasks that can arise during search and rescue missions. Effective remote operation using these vehicles requires sufficient situational awareness. In practice, communication quality does not often facilitate transfer of large amounts of information to provide situational awareness to the operator in a timely manner. Sharing autonomy between the vehicle and human can address this limitation by offtoading the decision making on low-level actions from the operator to the vehicle\u27s on-board computers. Sending occasional high-level commands to the vehicle is then sufficient for uninterrupted operation. Explicitly designing and tuning a control or decision making algorithm based on the specific task and environment may not always be feasible in the short preparation time available for addressing tasks involved in search and rescue in a specific environment. In this paper, we propose a deep learning framework for quick training of mobile robots to perform navigational tasks and facilitate remote operations. Two deep network models were trained on a hallway navigation task, demonstrated by a human expert. One learns action values for observation and action pairs, the second classifies observations into different action classes. Our evaluations and tests on the task of hallway navigation demonstrated that learning action values results in policies that better generalize compared to classification method. The video at https://youtu.be/wwGHnjRzXTQ demonstrates the implementation of this method

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

Planning Large-Scale Search and Rescue using Team of UAVs and Charging Stations∗

© 2018 IEEE. The energy limitation of robotic networks has hindered search and rescue missions. In this paper, we propose a mission planning method that overcomes the energy limitation by deploying static charging stations. Taking the mission objectives and constraints into consideration, the proposed method provides working robot trajectories and charging station locations together to optimize the overall mission performance. The energy efficient trajectories for working robots considers the priority area to facilitate the smart energy cycling. In addition, a geographic limitation is considered when finding the locations of the charging stations. Simulation results demonstrate the developed method by Monte Carlo method and Gazebo simulation environment. The demonstration video is available at https://youtu.be/0JWofUNTe7s

Rendezvous planning for multiple AUVs with mobile charging stations in dynamic currents

Operation of autonomous underwater vehicles (AUVs) in large spatiotemporal missions is currently challenged by onboard energy resources requiring manned support. With current methods, AUVs are programmed to return to a static charging station based on a threshold in their energy level. Although this approach has shown success in extending the operational life, it becomes impractical due to interruption of AUV operation and loss of energy needed to return to charging station. It is also not practical for large networks due to shortage of charging stations. We introduce mobile onsite power delivery, which will fundamentally change the range and duration of underwater operations. This letter presents a mission planning method to generate mobile charger trajectories, given pre-defined working AUV trajectories, considering environmental constraints such as currents and obstacles. The problem is formulated as a multiple generalized traveling salesman problem, which is then transformed into a traveling salesman problem. Energy cost in dynamic currents is integrated with a path planning algorithm using a grid-based environment model. A scheduling strategy extends the problem over multiple charging cycles. Simulation results show that the planning method significantly improves mission success and energy expenditure. Field experiments in Lake Superior using two types of AUVs, an unmanned surface vessel, and a manned support vessel validate the feasibility of the planned trajectories for long-term marine missions

Collaborative Mission Planning for Long-Term Operation Considering Energy Limitations

Mobile robotics research and deployment is highly challenged by energy limitations, particularly in marine robotics applications. This challenge can be addressed by autonomous transfer and sharing of energy in addition to effective mission planning. Specifically, it is possible to overcome energy limitations in robotic missions using an optimization approach that can generate trajectories for both working robots and mobile chargers while adapting to environmental changes. Such a method must simultaneously optimize all trajectories in the robotic network to be able to maximize overall system efficiency. This letter presents a Genetic Algorithm based approach that is capable of solving this problem at a variety of scales, both in terms of the size of the mission area and the number of robots. The algorithm is capable of re-planning during operation, allowing for the mission to adapt to changing conditions and disturbances. The proposed approach has been validated in multiple simulation scenarios. Field experiments using an autonomous underwater vehicle and a surface vehicle verify feasibility of the generated trajectories. The simulation and experimental validation show that the approach efficiently generates feasible trajectories to minimize energy use when operating multi-robot networks

Learning autonomous systems - An interdisciplinary project-based experience

© 2017 IEEE. With the increased influence of automation into every part of our lives, tomorrow\u27s engineers must be capable working with autonomous systems. The explosion of automation and robotics has created a need for a massive increase in engineers who possess the skills necessary to work with twenty-first century systems. Autonomous Systems (MEEM4707) is a new senior/graduate level elective course with goals of: 1) preparing the next generation of skilled engineers, 2) creating new opportunities for learning and well informed career choices, 3) increasing confidence in career options upon graduation, and 4) connecting academic research to the students world. Presented in this paper is the developed curricula, key concepts of the project-based approach, and resources for other educators to implement a similar course at their institution. In the course, we cover the fundamentals of autonomous robots in a hands-on manner through the use of a low-cost mobile robot. Each student builds and programs their own robot, culminating in operation of their autonomous mobile robot in a miniature city environment. The concepts covered in the course are scalable from middle school through graduate school. Evaluation of student learning is completed using pre/post surveys, student progress in the laboratory environment, and conceptual examinations

Robotic power distribution system for post-disaster operations

Tbis paper presents an architecture for autonomous mobile microgrids to ensure robustness and scalability of such systems for power distribution applications. A schema for development of mobile microgrids is presented based on the feasibility and experimental studies performed using ground robots for establishing microgrids. A microgrid system built with this architecture win act as an autonomous power network capable of connecting to different power nodes (generators, loads, storage units, converters, etc.) and interacting with them accordingly. This system will have the scalability characteristics of an ad-hoc system and could reconfigure itself depending on available power nodes

Ankle angles during step turn and straight walk: Implications for the design of a steerable ankle-foot prosthetic robot

This article compares the three-dimensional angles of the ankle during step turn and straight walking. We used an infrared camera system (Qualisys Oqus ®) to track the trajectories and angles of the foot and leg at different stages of the gait. The range of motion (ROM) of the ankle during stance periods was estimated for both straight step and step turn. The duration of combined phases of heel strike and loading response, mid stance, and terminal stance and pre-swing were determined and used to measure the average angles at each combined phase. The ROM in Inversion/Eversion (IE) increased during turning while Medial/Lateral (ML) rotation decreased and Dorsiflexion/Plantarflexion (DP) changed the least. During the turning step, ankle displacement in DP started with similar angles to straight walk (-9.68° of dorsiflexion) and progressively showed less plantarflexion (1.37° at toe off). In IE, the ankle showed increased inversion leaning the body toward the inside of the turn (angles from 5.90° to 13.61°). ML rotation initiated with an increased medial rotation of 5.68° relative to the straight walk transitioning to 12.06° of increased lateral rotation at the toe off. A novel tendon driven transtibial ankle-foot prosthetic robot with active controls in DP and IE directions was fabricated. It is shown that the robot was capable of mimicking the recorded angles of the human ankle in both straight walk and step turn. Copyright © 2013 by ASME