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<p>In this thesis, a human-in-the-loop control system is presented to assist an operator in teleoperation of a mobile robot. In a conventional teleoperation paradigm, the human operator would directly navigate the robot without any assistance which may result in poor performance in complex and unknown task environments due to inadequacy of visual feedback. The proposed method in this thesis builds on an earlier general control framework that systematically combines teleoperation and autonomous control subtasks. In this approach, the operator controls the mobile robot (slave) using a force-feedback haptic interface (master). Teleoperation control commands coordinate master and slave robots while an autonomous control subtask helps the operator avoid collisions with obstacles in the robot task environment by providing corrective force feedback. The autonomous collision avoidance is based on a Model Predictive Control (MPC) philosophy. The autonomous subtask control commands are generated by formulating and solving a constrained optimization problem over a rolling horizon window of time into the future using system models to predict the operator force and robot motion. The goal of the optimization is to prevent collisions within the prediction horizon by applying corrective force feedback, while minimizing interference with the operator teleoperation actions. It is assumed that the obstacles are stationary and sonar sensors mounted on the mobile robot measure the obstacle distances relative to the robot. Two formulation of MPC-based collision avoidance are proposed. The first formulation directly incorporates raw observation points as constraints in the MPC optimization problem. The second formulation relies on a line segment representation of the task environment. This thesis employs the well-known Hough transform method to effectively transform the raw sensor data into line segments. The extracted line segments constitute a compact model for the environment that is used in the formulation of collision constraints. The effectiveness of the proposed model-predictive control obstacle avoidance schemes is demonstrated in teleoperation experiments where the master robot is a 3DOF haptic interface and the slave is a P3-DX mobile robot equipped with eight (8) sonar sensors at the front.</p> / Master of Applied Science (MASc)

Yes / This paper proposes a robot navigation scheme using wireless visual sensors deployed in an environment. Different from the conventional autonomous robot approaches, the scheme intends to relieve massive on-board information processing required by a robot to its environment so that a robot or a vehicle with less intelligence can exhibit sophisticated mobility. A three-state snake mechanism is developed for coordinating a series of sensors to form a reference path. Wireless visual sensors communicate internal forces with each other along the reference snake for dynamic adjustment, react to repulsive forces from obstacles, and activate a state change in the snake body from a flexible state to a rigid or even to a broken state due to kinematic or environmental constraints. A control snake is further proposed as a tracker of the reference path, taking into account the robot’s non-holonomic constraint and limited steering power. A predictive control algorithm is developed to have an optimal velocity profile under robot dynamic constraints for the snake tracking. They together form a unified solution for robot navigation by distributed sensors to deal with the kinematic and dynamic constraints of a robot and to react to dynamic changes in advance. Simulations and experiments demonstrate the capability of a wireless sensor network to carry out low-level control activities for a vehicle. / Royal Society, Natural Science Funding Council (China)

Planetary rovers are robots that need to perform autonomous navigation, because of the long delay communication and no human assistance. Furthermore, they need to perform the optimal estimation of its position in order to have a good performance on its navigation system. The need for good performance filters for estimating the actual position of mobile robots of this kind is needed, due to the fact that sensors are noisy and that information is of vital importance for a planetary rover’s mission. Besides, good accurate sensors for the matter, are not easy to find for space application. Extended Kalman Filter (EKF) and Unscented Kalman Filter (UKF) were implemented to analyze a data set of a 4-wheel robot, and later used for comparison on accuracy in the estimation of its pose. The analysis will give the possibility to know the right combination of sensors, recognize some issues during the trajectory. Furthermore, this study has been made with aims to give the reader knowledge of state of the art in planetary rovers, their constraints and consideration while developing them. The robot used for the research has been developed for an international competition of field robot automation. The main goal is to navigate autonomously through flowerpots performing different tasks as flowerpot collection, distance traveled and robustness on localization and navigation algorithms. / <p>Validerat; 20120822 (anonymous)</p>

Operation of Autonomous Mobile Robots (AMRs) of all forms that include wheeled ground vehicles, quadrupeds and humanoids in dynamically changing GPS denied environments without a-priori maps, exclusively using onboard sensors, is an unsolved problem that has potential to transform the economy, and vastly improve humanity's capabilities with improvements to agriculture, manufacturing, disaster response, military and space exploration. Conventional AMR automation approaches are modularized into perception, motion planning and control which is computationally inefficient, and requires explicit feature extraction and engineering, that inhibits generalization, and deployment at scale. Few works have focused on real-world end-to-end approaches that directly map sensor inputs to control outputs due to the large amount of well curated training data required for supervised Deep Learning (DL) which is time consuming and labor intensive to collect and label, and sample inefficiency and challenges to bridging the simulation to reality gap using Deep Reinforcement Learning (DRL). This dissertation presents a novel method to efficiently train DRL with significantly fewer samples in a constrained racetrack environment at physical limits in simulation, transferred zero-shot to the real-world for robust end-to-end AMR navigation. The representation learned in a compact parameter space with 2 fully connected layers with 64 nodes each is demonstrated to exhibit emergent behavior for Out-of-Distribution (OOD) generalization to navigation in new environments that include unstructured terrain without maps, dynamic obstacle avoidance, and navigation to objects of interest with vision input that encompass low light scenarios with the addition of a night vision camera. The learned policy outperforms conventional navigation algorithms while consuming a fraction of the computation resources, enabling execution on a range of AMR forms with varying embedded computer payloads. / Doctor of Philosophy / Robots with wheels or legs to move around environments improve humanity's capabilities in many applications such as agriculture, manufacturing, and space exploration. Reliable, robust mobile robots have the potential to significantly improve the economy. A key component of mobility is navigation to either explore the surrounding environment, or travel to a goal position or object of interest by avoiding stationary, and dynamic obstacles. This is a complex problem that has no reliable solution, which is one of the main reasons robots are not present everywhere, assisting people in various tasks. Past and current approaches involve first mapping an environment, then planning a collision-free path, and finally executing motor signals to traverse along the path. This has several limitations due to the lack of detailed pre-made maps, and inability to operate in previously unseen, dynamic environments. Furthermore, these modular methods require high computation resources due to the large number of calculations required for each step that prevents high real-time speed, and functionality in small robots with limited weight capacity for onboard computers, that are beneficial for reconnaissance, and exploration tasks. This dissertation presents a novel Artificial Intelligence (AI) method for robot navigation that is more computationally efficient than current approaches, with better performance. The AI model is trained to race in simulation at multiple times real-time speed for cost-effective, accelerated training, and transferred to a physical mobile robot where it retains its training experience, and generalizes to navigation in new environments without maps, with exploratory behavior, and dynamic obstacle avoidance capabilities.

The navigation and control of an autonomous vehicle is a highly complex task. Making a vehicle intelligent and able to operate "unmanned" requires extensive theoretical as well as practical knowledge. An autonomous vehicle must be able to make decisions and respond to situations completely on its own. Navigation and control serves as the major limitation of the overall performance, accuracy and robustness of an autonomous vehicle. This thesis will address this problem and propose a unique navigation and control scheme for an autonomous lawn mower (ALM). Navigation is a key aspect when designing an autonomous vehicle. An autonomous vehicle must be able to sense its location, navigate its way toward its destination, and avoid obstacles it encounters. Since this thesis attempts to automate the lawn mowing process, it will present a navigational algorithm that covers a bounded region in a systematic way, while avoiding obstacles. This algorithm has many applications including search and rescue, floor cleaning, and lawn mowing. Furthermore, the robustness and utility of this algorithm is demonstrated in a 3D simulation. This thesis will specifically study the dynamics of a two-wheeled differential drive vehicle. Using this dynamic model, various control techniques can then be applied to control the movement of the vehicle. This thesis will consider both open loop and closed loop control schemes. Optimal control, path following, and trajectory tracking are all considered, simulated, and evaluated as practical solutions for control of an ALM. To design and build an autonomous vehicle requires the integration of many sensors, actuators, and controllers. Software serves as the glue to fuse all these devices together. This thesis will suggest various sensors and actuators that could be used to physically implement an ALM. This thesis will also describe the operation of each sensor and actuator, present the software used to control the system, and discuss physical limitations and constraints that might be encountered while building an ALM. / Master of Science