Vision Based Trajectory Tracking Of Space Debris In Close Proximity Via Integrated Estimation And Control

Since the launch of the first rocket by the scientists during the World War II , mankind continues their exploration of space. Those space explorations bring the benefits to human, such as high technology products like GPS, cell phone, etc. and in-depth insight of outside of the earth. However, they produce millions of debris with a total estimated mass of more than 3,000,000 kg in the space around the earth, which has and will continue to threat the safety of manned or unmanned space exploration. According to the research, at least tens of spacecraft were considered been damaged or destroyed by the debris left in the space. Thus, the increasingly cluttered environment in space is placing a premium on techniques capable of tracking and estimating the trajectory of space debris. Among debris, the pieces smaller than 1cm are unable to damage spacecraft because of the crafts’ shields, while the pieces larger than 10cm can be tracked by ground-based radars or a radar network. However, unlike the debris within these size ranges, the debris larger than 1 cm and smaller than 10 cm are able to hurt the shield of space craft and are hard to be detected by the exiting technical equipments because of their small size and cross-section area. Accordingly it is always a challenge for spacecraft or satellite mission designers to consider explicitly the ones ranged from 1 cm to 10 cm a priori. To tackle this challenge, a vision based debris’ trajectory tracking method is presented in the thesis. Unlike radar tracking, vision based tracking doesn’t require knowledge of a debris’ cross-section, regardless of its size. In this work, two cameras onboard of satellites in a formation are used to track the debris in iv close proximity. Also to differentiate the target debris from other clutters (i.e. the debris that are not tracked intentionally), a data association technique is investigated. A two-stage nonlinear robust controller is developed to adjust the attitude of the satellites such that the target debris is always inside of the field of view of the cameras. Capabilities of the proposed integrated estimation and control methods are validated in the simulations

bio-inspired attitude control of micro air vehicles using rich information from airflow sensors

Biological phenomena found in nature can be learned and customized to obtain innovative engineering solutions. In recent years, biologists found that birds and bats use their mechanoreceptors to sense the airflow information and use this information directly to achieve their agile flight performance. Inspired by this phenomenon, an attitude control system for micro air vehicles using rich amount of airflow sensor information is proposed, designed and tested. The dissertation discusses our research findings on this topic. First, we quantified the errors between the calculated and measured lift and moment profiles using a limited number of micro pressure sensors over a straight wing. Then, we designed a robust pitching controller using 20 micro pressure sensors and tested the closed-loop performance in a simulated environment. Additionally, a straight wing was designed for the pressure sensor based pitching control with twelve pressure sensors, which was then tested in our low-speed wind tunnel. The closed-loop pitching control system can track the commanded angle of attack with a rising time around two seconds and an overshoot around 10%. Third, we extended the idea to the three-axis attitude control scenarios, where both of the pressure and shear stress information are considered in the simulation. Finally, a fault tolerant controller with a guaranteed asymptotically stability is proposed to deal with sensor failures and calculation errors. The results show that the proposed fault tolerant controller is robust, adaptive, and can guarantee an asymptotically stable performance even in case that 50% of the airflow sensors fail in flight

Control and coordination for a group of mobile robots in unknown environments

This thesis studies the trajectory tracking and cooperative behavior for a team of mobile robots using nonlinear and intelligent algorithms to more efficiently achieve the mission outcome. There are many practical applications where specific tasks are more resourcefully achieved by using a group of mobile robots rather than a single robot. Mobile robots can subdivide and multi-task the mission with speed and accuracy and the ability to be individually modified for precise tasks makes them ideally suited for applications such as search and rescue, exploration or entertainment. When comparing the mission outcome of a group of multi mobile robots (MMR) to that of a single robot, we see that the performance of the MMR group improves the specific task allocation, safety, the time duration required and the system effectiveness to achieve the outcome. In order to create the most effective control algorithm for trajectory tracking, we present three different techniques including Lyapunov technique, intelligent control (fuzzy control) and the exponential version of sliding mode. The developed algorithms instruct a robot to keep moving on their desired trajectory while simultaneously reducing tracking errors. The experimental results when using a single mobile robot are presented to demonstrate the potential and capability of the developed algorithms. In order to coordinate a group of mobile robots to achieve a common outcome, the goal is to create efficient system architecture and a control algorithm that enables them to work both individually and in meaningful robot formations. This is achieved by employing coordination and trajectory tracking techniques with the knowledge derived by the localization of the robots from their environment. Three different hierarchical controllers are presented based on nonlinear and intelligent techniques in order to construct an algorithm that exhibits both group cooperation and coordination for a team of mobile robots. These controllers consist of Lyapunov technique, intelligent control (fuzzy control) and the exponential version of sliding mode. For improved trajectory tracking, each robot is fitted with onboard sensors. When an obstacle is detected by any of the robots’ sensors, they direct that robot to move around the obstacle by changing its velocity and direction. As well as obstacle avoidance, the controllers work to make the MMR group arrive concurrently at their target points by adjusting each of the individual robots’ velocities as they move along their desired trajectories. This means the group will arrive at their destination within the same time duration, regardless of the length of each individual trajectory or number of obstacles that confront each robot. The experimental results obtained using three mobile robots display the performance of these control algorithms in producing a cooperative and coordinated behavior for the robot group