Browsing by Subject "Multi-robot"

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    Mobile Robot Localization Under Processing And Communication Constraints
    (2016-03) Nerurkar, Esha
    Mobile robot localization is one of the most fundamental problems in robotics. For robots assisting humans in tasks such as surveillance, search and rescue, and space exploration, accurate localization, that is, precisely estimating the robot's pose (position and orientation), is a prerequisite for autonomous operation. The system resources (processing and communication) for localization, however, are often limited, and their availability varies widely depending upon the application and the operating environment. Therefore, the objective of this work is to develop resource-aware estimators for robot localization, which optimally utilize all available resources in order to maximize estimation accuracy. In the first part of this thesis, we address the problem of robot localization under processing constraints, focusing on the key applications of single-robot Simultaneous Localization and Mapping (SLAM) and multi-robot Cooperative Localization (CL). For SLAM, we propose two resource-aware approaches, the approximate Minimum Mean Squared Error (MMSE) estimator-based Power-SLAM algorithm and the approximate batch Maximum A Posterior (MAP) estimator-based Constrained Keyframe-based Localization and Mapping (C-KLAM). When approximations are inevitable due to processing constraints, both approaches aim to minimize the information loss while generating consistent estimates. For CL, we exploit the sparse structure of the batch MAP estimator to develop a resource-aware, fully-distributed multi-robot localization algorithm, that harnesses the processing, storage, and communication resources of the entire team, to obtain substantial speed-up. The second part of this thesis focuses on CL under communication constraints, in particular, asynchronous communication and bandwidth constraints. Due to limited communication range or the presence of obstacles, robots communicate asynchronously, that is, they can only interact with different sub-teams over time and exchange information intermittently. For this scenario, we develop a family of resource-aware information exchange rules for the robots, in order to ensure optimal and consistent localization performance. Lastly, this thesis investigates the problem of decentralized estimation under stringent communication bandwidth constraints. Here, robots can communicate only a severely quantized version (few or only one bit), of their real-valued sensor measurements, to the team. Existing estimation frameworks, however, are designed to process either real-valued or quantized measurements. To overcome this drawback, we propose a paradigm shift in estimation methodology by focusing on the design and performance evaluation of the first-ever, resource-aware, hybrid estimators. The proposed hybrid estimators are able to process both locally-available real-valued information, along with the quantized information received from the team, in order to maximize localization accuracy. Finally, we note that mobile robot applications are no longer limited to specialized and expensive robots. Commonly-available hand-held devices such as cell phones, PDAs, and even cars, are equipped with processing, sensing, and networking capabilities. Therefore, when coupled with the proposed innovative, scalable, and resource-aware algorithms, these ubiquitous mobile devices can lead to a proliferation of novel location-based services.
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    Motion induced robot-to-robot extrinsic calibration.
    (2012-05) Zhou, Xun
    Multi-robot systems, or mobile sensor networks, which have become increasingly popular due to recent advances in electronics and communications, can be used in a wide range of applications, such as space exploration, search and rescue, target tracking, and cooperative localization and mapping. In contrast to single robots, multi-robot teams are more robust against single-point failures, accomplish coverage tasks more efficiently by dispersing multiple robots into large areas, and achieve higher estimation accuracy by directly communicating and fusing their sensor measurements. Realizing these advantages of multi-robot systems, however, requires addressing certain challenges. Specifically, in order for teams of robots to cooperate, or fuse measurements from geographically dispersed sensors, they need to know their poses with respect to a common frame of reference. Initializing the robots' poses in a common frame is relatively easy when using GPS, but very challenging in the absence of external aids. Moreover, planning the motion of multiple robots to achieve optimal estimation accuracy is quite challenging. Specifically, since the estimation accuracy depends on the locations where the robots record their sensor measurements, it may take an extensive amount of time to reach a required level of accuracy, if the robots' motions are not properly designed. This thesis offers novel solutions to the aforementioned challenges. The first part of the thesis investigates the problem of relative robot pose initialization, using robot-to-robot distance and/or bearing measurements collected over multiple time steps. In particular, it focuses on solving minimal problems and proves that in 3D there exist only 14 such problems that need to be solved. Furthermore, it provides efficient algorithms for computing the robot-to-robot transformation, which exploit recent advances in algebraic geometry. The second part of the thesis investigates the problem of optimal motion strategies for localization in leader-follower formations using distance or bearing measurements. Interestingly, the robot-to-robot pose is unobservable if the robots move on a straight line and maintain their formations, hence, the uncertainty of the robots' poses increases over time. If the robots, however, deviate from the desired formation, their measurements provide additional information which makes the relative pose observable. This thesis addresses the trade-off between maintaining the formation and estimation accuracy, and provides algorithms for computing the optimal positions where the robots should move to in order to collect the most informative measurements at the next time step. By providing solutions to two important problems for multi-robot systems: motion-induced extrinsic calibration, and optimal motion strategies for relative localization, the work presented in this thesis is expected to promote the use of multi-robot teams in real-world applications.