Singularity-free Guiding Vector Field for Robot Navigation

Most of the existing path-following navigation algorithms cannot guarantee global convergence to desired paths or enable following self-intersected desired paths due to the existence of singular points where navigation algorithms return unreliable or even no solutions. One typical example arises in vector-field guided path-following (VF-PF) navigation algorithms. These algorithms are based on a vector field, and the singular points are exactly where the vector field diminishes. In this paper, we show that it is mathematically impossible for conventional VF-PF algorithms to achieve global convergence to desired paths that are self-intersected or even just simple closed (precisely, homeomorphic to the unit circle). Motivated by this new impossibility result, we propose a novel method to transform self-intersected or simple closed desired paths to non-self-intersected and unbounded (precisely, homeomorphic to the real line) counterparts in a higher-dimensional space. Corresponding to this new desired path, we construct a singularity-free guiding vector field on a higher-dimensional space. The integral curves of this new guiding vector field is thus exploited to enable global convergence to the higher-dimensional desired path, and therefore the projection of the integral curves on a lower-dimensional subspace converge to the physical (lower-dimensional) desired path. Rigorous theoretical analysis is carried out for the theoretical results using dynamical systems theory. In addition, we show both by theoretical analysis and numerical simulations that our proposed method is an extension combining conventional VF-PF algorithms and trajectory tracking algorithms. Finally, to show the practical value of our proposed approach for complex engineering systems, we conduct outdoor experiments with a fixed-wing airplane in windy environment to follow both 2D and 3D desired paths.Comment: Accepted for publication in IEEE Trransactions on Robotics (T-RO

Communication-Aware Multi-Target Tracking Guidance for Cooperative UAVs with Gimbaled Vision Sensors in Urban Environments

Department of Mechanical Enginering (Mechanical Engineering)This paper proposes the unified cooperative multi-target tracking algorithm, which considers the sensing range and communication in an urban environment. The objective function of the proposed algorithm is composed of two terms. The first-term is formulated by using FIM. Since Fisher information matrix can be utilized to quantify the information gathered by the sensors, we can formulate an objective function that reflects the constraints like the sensor field of view(FOV). Also, by reflecting parameters related to communication, communication with the ground station can be considered. However, if the target is outside the sensing range or occluded by the building continuously, UAVs cannot capture this target in the prediction step of receding horizon method when the first-term is used only. To solve this problem, the second-term, which is made up of relative distance between targets and UAVs, is proposed. In this situation, the uncertainty increases because the target information cannot be obtained. As the uncertainty increases, the increasing weight is multiplied by the second-term to generate a path to reduce the distance to this target. If the distance to the target is within the sensing range by using this term, the target can be tracked again by using the first-term because the uncertainty decreases by the sensing. The main contributions of this thesis are as follows. First, UAVs can create a path and a gimbal command to get useful information by considering the limited sensing capability. Second, by considering communication, the communication stability has been improved and the amount of information in the ground station has been increased. Lastly, in the prediction step of the receding horizon method, the target can be tracked even when information about the target is not gathered.ope

Active visual tracking in multi-agent scenarios

PhD thesisCamera-equipped robots (agents) can autonomously follow people to provide continuous assistance in wide areas, e.g. museums and airports. Each agent serves one person (target) at a time and aims to maintain its target centred on the camera’s image plane with a certain size (active visual tracking) without colliding with other agents and targets in its proximity. It is essential that each agent accurately estimates the state of itself and that of nearby targets and agents over time (i.e. tracking) to perform collision-free active visual tracking. Agents can track themselves with either on-board sensors (e.g. cameras or inertial sensors) or external tracking systems (e.g. multi-camera systems). However, on-board sensing alone is not sufficient for tracking nearby targets due to occlusions in crowded scenes, where an external multi-camera system can help. To address scalability of wide-area applications and accurate tracking, this thesis proposes a novel collaborative framework where agents track nearby targets jointly with wireless ceiling-mounted static cameras in a distributed manner. Distributed tracking enables each agent to achieve agreed state estimates of targets via iteratively communicating with neighbouring static cameras. However, such iterative neighbourhood communication may cause poor communication quality (i.e. packet loss/error) due to limited bandwidth, which worsens tracking accuracy. This thesis proposes the formation of coalitions among static cameras prior to distributed tracking based on a marginal information utility that accounts for both the communication quality and the local tracking confidence. Agents move on demand when hearing requests from nearby static cameras. Each agent independently selects its target with limited scene knowledge and computes its robotic control for collision-free active visual tracking. Collision avoidance among robots and targets can be achieved by the Optimal Reciprocal Collision Avoidance (ORCA) method. To further address view maintenance during collision avoidance manoeuvres, this thesis proposes an ORCA-based method with adaptive responsibility sharing and heading-aware robotic control mapping. Experimental results show that the proposed methods achieve higher tracking accuracy and better view maintenance compared with the state-of-the-art methods.Queen Mary University of London and Chinese Scholarship Council

Ground target tracking using UAV with input constraints

This paper provides a solution to the problem of ground target tracking using an unmanned aerial vehicle (UAV) with control input constraints. Target tracking control with input constraints is an important and challenging topic in the study of UAVs. In order to achieve precise target tracking in the presence of constant background wind and target motion, this paper proposes a saturated heading rate controller based on a guidance vector field while the airspeed is held constant. This proposed approach guarantees the global convergence of the UAV to a desired circular orbit around a target. To estimate unknown constant background wind and target motion, an adaptive observer with bounded estimate is developed. Simulation results demonstrate the effectiveness of the proposed approach