Autonomous homing and docking tasks for an underwater vehicle

This paper briefly introduces a strategy for autonomous homing and docking tasks using an autonomous underwater vehicle. The control and guidance based path following for those tasks are described in this work. A standard sliding mode for controller design is briefly given. The method provides robust motion control efforts for an underwater vehicle’s decoupled system whilst minimising chattering effects. In a guidance system, the vector field based on a conventional artificial potential field method gives a desired trajectory with a use of existing information from sensors in the network. A well structured Line-of-Sight method is used for an AUV to follow the path. It provides guidance for an AUV to follow the predefined trajectory to a required position with the final desired orientation at the dock. Integration of a control and guidance system provides a complete system for this application. Simulation studies are illustrated in the paper

COORDINATION OF LEADER-FOLLOWER MULTI-AGENT SYSTEM WITH TIME-VARYING OBJECTIVE FUNCTION

This thesis aims to introduce a new framework for the distributed control of multi-agent systems with adjustable swarm control objectives. Our goal is twofold: 1) to provide an overview to how time-varying objectives in the control of autonomous systems may be applied to the distributed control of multi-agent systems with variable autonomy level, and 2) to introduce a framework to incorporate the proposed concept to fundamental swarm behaviors such as aggregation and leader tracking. Leader-follower multi-agent systems are considered in this study, and a general form of time-dependent artificial potential function is proposed to describe the varying objectives of the system in the case of complete information exchange. Using Lyapunov methods, the stability and boundedness of the agents\u27 trajectories under single order and higher order dynamics are analyzed. Illustrative numerical simulations are presented to demonstrate the validity of our results. Then, we extend these results for multi-agent systems with limited information exchange and switching communication topology. The first steps of the realization of an experimental framework have been made with the ultimate goal of verifying the simulation results in practice

Analogue mouse pointer control via an online steady state visual evoked potential (SSVEP) brain-computer interface

The steady state visual evoked protocol has recently become a popular paradigm in brain–computer interface (BCI) applications. Typically (regardless of function) these applications offer the user a binary selection of targets that perform correspondingly discrete actions. Such discrete control systems are appropriate for applications that are inherently isolated in nature, such as selecting numbers from a keypad to be dialled or letters from an alphabet to be spelled. However motivation exists for users to employ proportional control methods in intrinsically analogue tasks such as the movement of a mouse pointer. This paper introduces an online BCI in which control of a mouse pointer is directly proportional to a user's intent. Performance is measured over a series of pointer movement tasks and compared to the traditional discrete output approach. Analogue control allowed subjects to move the pointer faster to the cued target location compared to discrete output but suffers more undesired movements overall. Best performance is achieved when combining the threshold to movement of traditional discrete techniques with the range of movement offered by proportional control

Multi-Modal Mean-Fields via Cardinality-Based Clamping

Mean Field inference is central to statistical physics. It has attracted much interest in the Computer Vision community to efficiently solve problems expressible in terms of large Conditional Random Fields. However, since it models the posterior probability distribution as a product of marginal probabilities, it may fail to properly account for important dependencies between variables. We therefore replace the fully factorized distribution of Mean Field by a weighted mixture of such distributions, that similarly minimizes the KL-Divergence to the true posterior. By introducing two new ideas, namely, conditioning on groups of variables instead of single ones and using a parameter of the conditional random field potentials, that we identify to the temperature in the sense of statistical physics to select such groups, we can perform this minimization efficiently. Our extension of the clamping method proposed in previous works allows us to both produce a more descriptive approximation of the true posterior and, inspired by the diverse MAP paradigms, fit a mixture of Mean Field approximations. We demonstrate that this positively impacts real-world algorithms that initially relied on mean fields.Comment: Submitted for review to CVPR 201

Nonholonomic Feedback Control Among Moving Obstacles

A feedback controller is developed for navigating a nonholonomic vehicle in an area with multiple stationary and possibly moving obstacles. Among other applications the developed algorithms can be used for automatic parking of a passenger car in a parking lot with complex configuration or a ground robot in cluttered environment. Several approaches are explored which combine nonholonomic systems control based on sliding modes and potential field methods

Velocity field path-planning for single and multiple unmanned ariel vehicles

Unmanned aerial vehicles (UAV) have seen a rapid growth in utilisation for reconnaissance, mostly using single UAVs. However, future utilisation of UAVs for applications such as bistatic synthetic aperture radar and stereoscopic imaging, will require the use of multiple UAVs acting cooperatively to achieve mission goals. In addition, to de-skill the operation of UAVs for certain applications will require the migration of path-planning functions from the ground to the UAV. This paper details a computationally efficient algorithm to enable path-planning for single UAVs and to form and re-form UAV formations with active collision avoidance. The algorithm presented extends classical potential field methods used in other domains for the UAV path-planning problem. It is demonstrated that a range of tasks can be executed autonomously, allowing high level tasking of single and multiple UAVs in formation, with the formation commanded as a single entity