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Formation planning and control for multi-agent robotic systems enables tasks to be completed more efficiently and robustly compared to using a single agent. Applications are found in fields such as agriculture, mining, autonomousvehicle platooning, surveillance, space exploration, etc. In this paper, a complete framework for formation path planning for holonomic ground robots in an obstacle-rich environment is proposed. The method utilizes the Fast Marching Square (FM2) path planning algorithm, and a formation keeping approach which falls within the Leader-Follower category. Contrary to most related works, the role of leader is dynamically assigned to avoid unnecessary rotation of the formation. Furthermore, the roles of the followers are also dynamically assigned to fit the current geometry of the formation. A flexible spring-damper system prevents inter-robot collisions and helps maintain the formation shape. An obstacle avoidance step at the end of the pipeline keeps the spring forces from driving robots into obstacles. The framework is tested on a formation consisting of three Unitree Go1 quadruped robots, both in the Gazebo simulation environment and in lab experiments. The results are successful and indicate that the method is feasible, although further work is needed to adjust the role assignment for larger formations, combine the framework with Simultaneous Localization and Mapping (SLAM) and provide a more robust handling of dynamic obstacles.

Formation planning and control for multi-agent robotic systems enables tasks to be completed more efficiently and robustly compared to using a single agent. Applications are found in fields such as agriculture, mining, autonomousvehicle platooning, surveillance, space exploration, etc. In this paper, a complete framework for formation path planning for holonomic ground robots in an obstacle-rich environment is proposed. The method utilizes the Fast Marching Square (FM2) path planning algorithm, and a formation keeping approach which falls within the Leader-Follower category. Contrary to most related works, the role of leader is dynamically assigned to avoid unnecessary rotation of the formation. Furthermore, the roles of the followers are also dynamically assigned to fit the current geometry of the formation. A flexible spring-damper system prevents inter-robot collisions and helps maintain the formation shape. An obstacle avoidance step at the end of the pipeline keeps the spring forces from driving robots into obstacles. The framework is tested on a formation consisting of three Unitree Go1 quadruped robots, both in the Gazebo simulation environment and in lab experiments. The results are successful and indicate that the method is feasible, although further work is needed to adjust the role assignment for larger formations, combine the framework with Simultaneous Localization and Mapping (SLAM) and provide a more robust handling of dynamic obstacles.

In previous research, the necessity of non-rigid body simulators was stated to be a requirement for long, slender bodies. However, it remains unclear for which vehicle design parameters non-rigid body effects become destabilising. A set of 2+N linearly independent equations of motions can be combined in a single matrix to determine the motion of a flexible launch vehicle with N slosh masses. The flexibility of the vehicle is modelled using the method of assumed modes. The sloshing motion is modelled by assuming that the slosh mass behaves like a pendulum. To couple the different motions, a constraint matrix is used. The model is developed in Matlab Simulink. Using non-quiescent starting conditions with no external forces acting on the vehicle except gravity, it was found that the energy of the system remained constant. The flight data from the Stratos III launch vehicle was obtained and compared to the simulated non-rigid body model data. It was found that slosh motion can result in destabilisation when this vehicle experiences a sudden decrease in acceleration. The destabilisation that occurred could not be observed when rigid body equations of motion were used. Based on a sensitivity analysis over a range of vehicle parameters, it is recommended for length over diameter (L/D) ratios above 20 to consider the use of non-rigid body models. From this ratio, the maximum and total flexibility strongly increase. For L/D ratios below 20, flexibility is negligible and sloshing becomes the main perturbing factor. It was found that a PID controller that is designed using a rigid body model is still able to stabilise the non-rigid body during flight. Oscillations occurred in both the rigid and non-rigid body model when a single set of gains was used. For both models, the oscillations were removed by introducing gain scheduling., Aerospace Engineering

In previous research, the necessity of non-rigid body simulators was stated to be a requirement for long, slender bodies. However, it remains unclear for which vehicle design parameters non-rigid body effects become destabilising. A set of 2+N linearly independent equations of motions can be combined in a single matrix to determine the motion of a flexible launch vehicle with N slosh masses. The flexibility of the vehicle is modelled using the method of assumed modes. The sloshing motion is modelled by assuming that the slosh mass behaves like a pendulum. To couple the different motions, a constraint matrix is used. The model is developed in Matlab Simulink. Using non-quiescent starting conditions with no external forces acting on the vehicle except gravity, it was found that the energy of the system remained constant. The flight data from the Stratos III launch vehicle was obtained and compared to the simulated non-rigid body model data. It was found that slosh motion can result in destabilisation when this vehicle experiences a sudden decrease in acceleration. The destabilisation that occurred could not be observed when rigid body equations of motion were used. Based on a sensitivity analysis over a range of vehicle parameters, it is recommended for length over diameter (L/D) ratios above 20 to consider the use of non-rigid body models. From this ratio, the maximum and total flexibility strongly increase. For L/D ratios below 20, flexibility is negligible and sloshing becomes the main perturbing factor. It was found that a PID controller that is designed using a rigid body model is still able to stabilise the non-rigid body during flight. Oscillations occurred in both the rigid and non-rigid body model when a single set of gains was used. For both models, the oscillations were removed by introducing gain scheduling., Aerospace Engineering

This thesis addresses the challenging problem of three-dimensional (3-D) reconstruction of a fast-moving human, using a single moving camera which captures both depth and colour information (RGB-D). Our objective is to find solutions to the challenges arising from the high camera motion and articulated human motions. We have developed an effective system which uses the camera pose, skeleton detection, and multi-scale information, to produce a robust reconstruction framework for 3-D modelling of fast-moving humans. The outcome of the research is useful for several applications of human performance capture systems in sports, the arts, and animation industries.

Recent advances in commodity depth or 3D sensing technologies have enabled us to move closer to the goal of accurately sensing and modeling the 3D representations of complex dynamic scenes. Indeed, in domains such as virtual reality, security, surveillance and e-health, there is now a greater demand for aff ordable and flexible vision systems which are capable of acquiring high quality 3D reconstructions. Available commodity RGB-D cameras, though easily accessible, have limited fi eld-of-view, and acquire noisy and low-resolution measurements which restricts their direct usage in building such vision systems. This thesis targets these limitations and builds approaches around commodity 3D sensing technologies to acquire noise-free and feature preserving full 3D reconstructions of dynamic scenes containing, static or moving, rigid or non-rigid objects. A mono-view system based on a single RGB-D camera is incapable of acquiring full 360 degrees 3D reconstruction of a dynamic scene instantaneously. For this purpose, a multi-view system composed of several RGB-D cameras covering the whole scene is used. In the first part of this thesis, the domain of correctly aligning the information acquired from RGB-D cameras in a multi-view system to provide full and textured 3D reconstructions of dynamic scenes, instantaneously, is explored. This is achieved by solving the extrinsic calibration problem. This thesis proposes an extrinsic calibration framework which uses the 2D photometric and 3D geometric information, acquired with RGB-D cameras, according to their relative (in)accuracies, a ffected by the presence of noise, in a single weighted bi-objective optimization. An iterative scheme is also proposed, which estimates the parameters of noise model aff ecting both 2D and 3D measurements, and solves the extrinsic calibration problem simultaneously. Results show improvement in calibration accuracy as compared to state-of-art methods. In the second part of this thesis, the domain

Recent advances in commodity depth or 3D sensing technologies have enabled us to move closer to the goal of accurately sensing and modeling the 3D representations of complex dynamic scenes. Indeed, in domains such as virtual reality, security, surveillance and e-health, there is now a greater demand for aff ordable and flexible vision systems which are capable of acquiring high quality 3D reconstructions. Available commodity RGB-D cameras, though easily accessible, have limited fi eld-of-view, and acquire noisy and low-resolution measurements which restricts their direct usage in building such vision systems. This thesis targets these limitations and builds approaches around commodity 3D sensing technologies to acquire noise-free and feature preserving full 3D reconstructions of dynamic scenes containing, static or moving, rigid or non-rigid objects. A mono-view system based on a single RGB-D camera is incapable of acquiring full 360 degrees 3D reconstruction of a dynamic scene instantaneously. For this purpose, a multi-view system composed of several RGB-D cameras covering the whole scene is used. In the first part of this thesis, the domain of correctly aligning the information acquired from RGB-D cameras in a multi-view system to provide full and textured 3D reconstructions of dynamic scenes, instantaneously, is explored. This is achieved by solving the extrinsic calibration problem. This thesis proposes an extrinsic calibration framework which uses the 2D photometric and 3D geometric information, acquired with RGB-D cameras, according to their relative (in)accuracies, a ffected by the presence of noise, in a single weighted bi-objective optimization. An iterative scheme is also proposed, which estimates the parameters of noise model aff ecting both 2D and 3D measurements, and solves the extrinsic calibration problem simultaneously. Results show improvement in calibration accuracy as compared to state-of-art methods. In the second part of this thesis, the domain

Trajectory basis Non-Rigid Structure From Motion (NRSFM) currently faces two problems: the limit of reconstructability and the need to tune the basis size for different sequences. This paper provides a novel theoretical bound on 3D reconstruction error, arguing that the existing definition of reconstructability is fundamentally flawed in that it fails to consider system condition. This insight motivates a novel strategy whereby the trajectory's response to a set of high-pass filters is minimised. The new approach eliminates the need to tune the basis size and is more efficient for long sequences. Additionally, the truncated DCT basis is shown to have a dual interpretation as a high-pass filter. The success of trajectory filter reconstruction is demonstrated quantitatively on synthetic projections of real motion capture sequences and qualitatively on real image sequences.

Image registration is a fundamental problem that can be found in a diverse range of fields within the research community. It is used in areas such as engineering, science, medicine, robotics, computer vision and image processing, which often require the process of developing a spatial mapping between sets of data. Registration plays a crucial role in the medical imaging field where continual advances in imaging modalities, including MRI, CT and PET, allow the generation of 3D images that explicitly outline detailed in vivo information of not only human anatomy, but also human function. Mutual Information (MI) is a popular entropy-based similarity measure which has found use in a large number of image registration applications. Stemming from information theory, this measure generally outperforms most other intensity-based measures in multimodal applications as it does not assume the existence of any specific relationship between image intensities. It only assumes a statistical dependence. The basic concept behind any approach using MI is to find a transformation, which when applied to an image, will maximise the MI between two images. This thesis presents research using MI in three major topics encompassed by the computer vision and medical imaging field: rigid image registration, stereo vision, and non-rigid image registration. In the rigid domain, a novel gradient-based registration algorithm (MIGH) is proposed that uses Parzen windows to estimate image density functions and Gauss-Hermite quadrature to estimate the image entropies. The use of this quadrature technique provides an effective and efficient way of estimating entropy while bypassing the need to draw a second sample of image intensities (a procedure required in previous Parzen-based MI registration approaches). It is possible to achieve identical results with the MIGH algorithm when compared to current state of the art MI-based techniques. These results are achieved using half the previously required sam