<p>Generating and stabilizing periodic motions in nonlinear systems is a challenging task. In the control system community this topic is also known as limit cycle control. In recent years a framework known as Virtual Holonomic Constraints (VHC) has been developed as one of the solutions to this problem. The aim of this thesis is to give an insight into this approach and its practical application.</p><p>The contribution of this work is primarily the experimental validation of the theory. A step by step procedure of this methodology is given for motion planning, as well as for controller design. Three particular setups were chosen for experiments: the inertia wheel pendulum, the Furuta pendulum and the two-link planar pendulum. These under-actuated mechanical systems are well known benchmarking setups for testing advanced control design methods.</p><p>Further application is intended for cases such as biped robot walking/running, human and animal locomotion analysis, etc.</p>

<p>Many of the applications that have been proposed for future small unmanned aerial vehicles (UAVs) are at low altitude in areas with many obstacles. A vital component for successful navigation in such environments is a path planner that can find collision free paths for the UAV.</p><p>Two popular path planning algorithms are the probabilistic roadmap algorithm (PRM) and the rapidly-exploring random tree algorithm (RRT).</p><p>Adaptations of these algorithms to an unmanned autonomous helicopter are presented in this thesis, together with a number of extensions for handling constraints at different stages of the planning process.</p><p>The result of this work is twofold:</p><p>First, the described planners and extensions have been implemented and integrated into the software architecture of a UAV. A number of flight tests with these algorithms have been performed on a physical helicopter and the results from some of them are presented in this thesis.</p><p>Second, an empirical study has been conducted, comparing the performance of the different algorithms and extensions in this planning domain. It is shown that with the environment known in advance, the PRM algorithm generally performs better than the RRT algorithm due to its precompiled roadmaps, but that the latter is also usable as long as the environment is not too complex. The study also shows that simple geometric constraints can be added in the runtime phase of the PRM algorithm, without a big impact on performance. It is also shown that postponing the motion constraints to the runtime phase can improve the performance of the planner in some cases.</p> / Report code: LiU–Tek–Lic–2006:10.

This paper explores the motion planning problem for multiple moving objects. The approach taken consists of assigning priorities to the objects, then planning motions one object at a time. For each moving object, the planner constructs a configuration space-time that represents the time-varying constraints imposed on the moving object by the other moving and stationary objects. The planner represents this space-time approximately, using two-dimensional slices. The space-time is then searched for a collision-free path. The paper demonstrates this approach in two domains. One domain consists of translating planar objects; the other domain consists of two-link planar articulated arms.

Searching for objects in physical space is one of the most important tasks for humans. Mobile sensor networks can be great tools for the task. Transient targets refer to a class of objects which are not identifiable unless momentary sensing and signaling conditions are satisfied. The transient property is often introduced by target attributes, privacy concerns, environment constraints, and sensing limitations. Transient target localization problems are challenging because the transient property is often coupled with factors such as sensing range limits, various coverage functions, constrained mobility, signal correspondence, limited number of searchers, and a vast searching region. To tackle these challenge tasks, we gradually increase complexity of the transient target localization problem such as Single Robot Single Target (SRST), Multiple Robots Single Target (MRST), Single Robot Multiple Targets (SRMT) and Multiple Robots Multiple Targets (MRMT). We propose the expected searching time (EST) as a primary metric to assess the searching ability of a single robot and the spatiotemporal probability occupancy grid (SPOG) method that captures transient characteristics of multiple targets and tracks the spatiotemporal posterior probability distribution of the target transmissions. Besides, we introduce a team of multiple robots and develop a sensor fusion model using the signal strength ratio from the paired robots in centralized and decentralized manners. We have implemented and validated the algorithms under a hardware-driven simulation and physical experiments.

This thesis introduces new concepts and algorithms that can be used to solve the simultaneous task and motion planning (STAMP) problem. Given a set of actions a robot could perform, the STAMP problem asks for a sequence of actions that takes the robot to its goal and for motion plans that correspond to the actions in that sequence. This thesis shows how to solve the STAMP problem more efficiently and obtain more robust solutions, when compared to previous work. A solution to the STAMP problem is a prerequisite for most operations complex robots such as mobile manipulators are asked to perform. Solving the STAMP problem efficiently thus expands the range of capabilities for mobile manipulators, and the increased robustness of computed solutions can improve safety. A basic sub-problem of the STAMP problem is motion planning. This thesis generalizes KPIECE, a sampling-based motion planning algorithm designed specifically for planning in high-dimensional spaces. KPIECE offers computational advantages by employing projections from the searched space to lower-dimensional Euclidean spaces for estimating exploration coverage. This thesis further develops the original KPIECE algorithm by introducing a means to automatically generate projections to lower-dimensional Euclidean spaces. KPIECE and other state-of-the-art algorithms are implemented as part the Open Motion Planning Library (OMPL), and the practical applicability of KPIECE and OMPL is demonstrated on the PR2 hardware platform. To solve the STAMP problem, this thesis introduces the concept of a task motion multigraph (TMM), a data structure that can express the ability of mobile manipulators to perform specific tasks using different hardware components. The choice of hardware components determines the state space for motion planning. An algorithm that prioritizes the state spaces for motion planning using TMMs is presented and evaluated. Experimental results show that planning times are reduced by a factor of up to six and solution paths are shortened by a factor of up to four, when considering the available planning options. Finally, an algorithm that considers uncertainty at the task planning level based on generating Markov Decision Process (MDP) problems from TMMs is introduced.

In this thesis we present a hybrid control methodology using Artificial Potential Fields (APF) integrated with a modified Simulated Annealing (SA) optimization algorithm for motion planning of a multi-link robots team. The principle of this work is based on the locomotion of a snake where subsequent links follow the trace of the head. The proposed algorithm uses the APF method which provides simple, efficient and effective path planning and the modified SA is applied in order for the robots to recover from a local minima. Modifications to the SA algorithm improve the performance of the algorithm and reduce convergence time. Validation on a three-link snake robot shows that the derived control laws from the motion planning algorithm that combine APF and SA can successfully navigate the robot to reach its destination, while avoiding collisions with multiple obstacles and other robots in its path as well as recover from local minima. To improve the performance of the algorithm, the gradient descent method is replaced by Newton’s method which helps in reducing the zigzagging phenomenon in gradient descent method while the robot moves in the vicinity of an obstacle. / UOIT

Teams of mobile robots have numerous applications, such as space exploration, underground mining, warehousing, and building security. Multi-robot teams can provide a number of practical benefits in such applications, including simultaneous presence in multiple locations, improved system performance, and greater robustness and redundancy compared to individual robots. This thesis addresses three aspects of coordination and navigation for teams of mobile robots: localization, the estimation of the position of each robot in the environment; motion planning, the process of finding collision-free trajectories through the environment; and task allocation, the selection of appropriate goals to be assigned to each robot. Each of these topics are investigated in the context of many robots working in a common environment. A particle-filter based system for cooperative global localization is presented. The system combines the sensor data from three robots, including measurements of the distances between robots, to cooperatively estimate the global position of each robot in the environment. The method is developed for a single triad of robots, then extended to larger groups of robots. The algorithm is demonstrated in a simulation of robots equipped with only simple range sensors, and is shown to successfully achieve global localization of robots that are unable to localize using only their own local sensor data. Motion planning is investigated for large teams of robots operating in tunnel and corridor environments, where coordinated planning is often required to avoid collision or deadlock conditions. A complete and scalable motion planning algorithm is presented and evaluated in simulation with up to 150 robots. In contrast to popular decoupled approaches to motion planning (which cannot guarantee a solution), this algorithm uses a multi-phase approach to create and maintain obstacle-free paths through a graph representation of the environment. The resulting plan is a set of collision-free trajectories, guaranteeing that every robot will reach its goal. The problem of task allocation is considered in the same type of tunnel and corridor environments, where tasks are defined as locations in the environment that must be visited by one of the robots in the team. To find efficient solutions to the task allocation problem, an optimization approach is used to generate potential task assignments, and select the best solution. The multi-phase motion planner is applied within this system as an efficient method of evaluating potential task assignments for many robots in a large environment. The algorithm is evaluated in simulations with up to 20 robots in a map of large underground mine. A real-world implementation of 3 physical robots was used to demonstrate the implementation of the multi-phase motion planning and task allocation systems. A centralized motion planning and task allocation system was developed, incorporating localization and time-dependent trajectory tracking on the robot processors, enabling cooperative navigation in a shared hallway environment.

Teams of mobile robots have numerous applications, such as space exploration, underground mining, warehousing, and building security. Multi-robot teams can provide a number of practical benefits in such applications, including simultaneous presence in multiple locations, improved system performance, and greater robustness and redundancy compared to individual robots. This thesis addresses three aspects of coordination and navigation for teams of mobile robots: localization, the estimation of the position of each robot in the environment; motion planning, the process of finding collision-free trajectories through the environment; and task allocation, the selection of appropriate goals to be assigned to each robot. Each of these topics are investigated in the context of many robots working in a common environment. A particle-filter based system for cooperative global localization is presented. The system combines the sensor data from three robots, including measurements of the distances between robots, to cooperatively estimate the global position of each robot in the environment. The method is developed for a single triad of robots, then extended to larger groups of robots. The algorithm is demonstrated in a simulation of robots equipped with only simple range sensors, and is shown to successfully achieve global localization of robots that are unable to localize using only their own local sensor data. Motion planning is investigated for large teams of robots operating in tunnel and corridor environments, where coordinated planning is often required to avoid collision or deadlock conditions. A complete and scalable motion planning algorithm is presented and evaluated in simulation with up to 150 robots. In contrast to popular decoupled approaches to motion planning (which cannot guarantee a solution), this algorithm uses a multi-phase approach to create and maintain obstacle-free paths through a graph representation of the environment. The resulting plan is a set of collision-free trajectories, guaranteeing that every robot will reach its goal. The problem of task allocation is considered in the same type of tunnel and corridor environments, where tasks are defined as locations in the environment that must be visited by one of the robots in the team. To find efficient solutions to the task allocation problem, an optimization approach is used to generate potential task assignments, and select the best solution. The multi-phase motion planner is applied within this system as an efficient method of evaluating potential task assignments for many robots in a large environment. The algorithm is evaluated in simulations with up to 20 robots in a map of large underground mine. A real-world implementation of 3 physical robots was used to demonstrate the implementation of the multi-phase motion planning and task allocation systems. A centralized motion planning and task allocation system was developed, incorporating localization and time-dependent trajectory tracking on the robot processors, enabling cooperative navigation in a shared hallway environment.

A mathematical model for a Two-Span Web Roller machine is defined in order to facilitate motion planning, motion tracking and state observer design for tracking web tension and web velocity. Differential Flatness is utilized to create reference trajectories that are tracked with a high convergence rate. Flatness also allows for nominal input torque generation without integration. Constraints on the inputs are satisfied through the motion planning phase. A partial state feedback linearization is performed and an exponential tracking dynamic feedback controller is defined. An exponential Kalman-related tension observer is also defined with semi-optimal gain formulation. The observer takes advantage of the bilinearity of the dynamics up to additive output nonlinearity. The closed-loop system is simulated in MatLab with comparisons to reference trajectories previously employed in literature. The importance of proper motion planning is demonstrated by producing excellent performance compared with existing tracking and tension observing methods.

Navigation is an invisible utility that is often taken for granted with considerable societal and economic impacts. Not only is navigation essential to our modern life, but the more it advances, the more possibilities are created. Navigation is at the heart of three emerging fields: autonomous vehicles, location-based services, and intelligent transportation systems. Global navigation satellite systems (GNSS) are insufficient for reliable anytime, anywhere navigation, particularly indoors, in deep urban canyons, and in environments under malicious attacks (e.g., jamming and spoofing). The conventional approach to overcome the limitations of GNSS-based navigation is to couple GNSS receivers with dead reckoning sensors. A new paradigm, termed opportunistic navigation (OpNav), is emerging. OpNav is analogous to how living creatures naturally navigate: by learning their environment. OpNav aims to exploit the plenitude of ambient radio frequency signals of opportunity (SOPs) in the environment. OpNav radio receivers, which may be handheld or vehicle-mounted, continuously search for opportune signals from which to draw position and timing information, employing on-the-fly signal characterization as necessary. In collaborative opportunistic navigation (COpNav), multiple receivers share information to construct and continuously refine a global signal landscape. For the sake of motivation, consider the following problem. A number of receivers with no a priori knowledge about their own states are dropped in an environment comprising multiple unknown terrestrial SOPs. The receivers draw pseudorange observations from the SOPs. The receivers' objective is to build a high-fidelity signal landscape map of the environment within which they localize themselves in space and time. We then ask: (i) Under what conditions is the environment fully observable? (ii) In cases where the environment is not fully observable, what are the observable states? (iii) How would receiver-controlled maneuvers affect observability? (iv) What is the degree of observability of the various states in the environment? (v) What motion planning strategy should the receivers employ for optimal information gathering? (vi) How effective are receding horizon strategies over greedy for receiver trajectory optimization, and what are their limitations? (vii) What level of collaboration between the receivers achieves a minimal price of anarchy? This dissertation addresses these fundamental questions and validates the theoretical conclusions numerically and experimentally. / text