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In modern transportation, traffic congestion has become an urgent problem in large and medium-sized cities. In smart transportation systems, it is an effective solution to design load balancing path planning algorithms that can dynamically adapt to traffic conditions in order to avoid congestion. In this work, a traffic path planning algorithm based on data prediction (TPPDP) is proposed to find the path with the shortest travel time, which is built on a predictive model based on historical traffic data and current traffic information. Furthermore, a path planning algorithm based on data prediction with load balancing (TPPDP-LB) is also proposed, which combines the predicted information and the number of concurrent requests to achieve the path with shortest travel time while maintaining global load balancing. A specific distributed computing framework for TPPDP-LB algorithm is designed to reduce the runtime of the algorithm. The simulation results proved that both TPPDP and TPPDP-LB algorithms have the advantage of shortest travel time, and TPPDP-LB algorithm achieves load balancing of computing. It is also proved that the distributed computing framework designed for TPPDP-LP algorithm can effectively reduce the runtime of system as well as keep the accuracy of algorithm.

This study investigates the task assignment problem where a fleet of dispersed vehicles needs to visit multiple target locations in a time-invariant drift field with obstacles while trying to minimise the vehicles' total travel time. The vehicles have different capabilities, and each kind of vehicles can visit a certain type of the target locations; each target location might require to be visited more than once by different kinds of vehicles. The task assignment problem has been proven to be NP-hard. A path planning algorithm is first designed to minimise the time for a vehicle to travel between two given locations through the drift field while avoiding any obstacle. The path planning algorithm provides the travel cost matrix for the target assignment, and generates routes once the target locations are assigned to the vehicles. Then, a distributed algorithm is proposed to assign the target locations to the vehicles using only local communication. The algorithm guarantees that all the visiting demands of every target will be satisfied within a total travel time that is at worst twice of the optimal when the travel cost matrix is symmetric. Numerical simulations show that the algorithm can lead to solutions close to the optimal.

The aim of this work is to design a robotic bioprinting platform able to fabricate a three-dimensional structure onto irregular surfaces. With respect to the limitations of current in vitro bioprinting approach, widely used in scaffold-based tissue engineering – handling difficulty, risk of contamination, shape not matching with the defect site – this robotic bioprinter can offer an innovative solution allowing in situ bioprinting, a direct dispensing of biological materials onto and into the damaged site. The robotic platform was developed starting from the 5 degrees-of-freedom open source MOVEO robot from BCN3D. The hardware and the software of the original project were re-engineered to control the robot using LinuxCNC, a path planning algorithm was developed in Matlab®, and the end-effector was equipped with a pneumatic extruder. The algorithm automatically projects any generic printing pattern on the surface on which the scaffold will be 3D bioprinted. For each point, the algorithm calculates the joint angles to keep the end effector always perpendicular to the surface. A g-code file is then exported to Linux CNC adding parameters to control the air pressure and the printing speed. The robotic platform was tested to evaluate its performances. Resolution (~200 ​μm) and repeatability were estimated and preliminary in situ bioprinting tests were performed onto different irregular surfaces, including a physiologically relevant bone model.

In the operation of a broad range of industrial processes, the uses of robots whose trajectories are constrained by the velocity of the parts actuated (tools, end effectors, joints and the like) more often than not play a significant role. Keeping our focus on systems for material deposition such as painting, glueing, aerosol spraying but nowadays also additive manufacturing techniques like FDM processes, it is noticeable that a key parameter is the control of the flow of material in accordance with the trajectory velocity of the parts being actuated. According to the specific requirements and goals of different technology, it is possible to generate different trajectories. In this paper, we propose an original path planning algorithm based on the use of Bezier curves aimed at assuring regulation of the velocity and a uniform distribution of the extruded material referring to an innovative additive manufacturing technology. In particular, the paper presents a path planning technology developed for an application where it is necessary to maintain a constant velocity along the length of the trajectory aimed at improving the technological processes on the basis of an innovative additive technology. The paper further presents a working application with a machine prototype so as to demonstrate the viability and performance of the work under consideration.

This thesis presents a computational model of the surgical stitching tasks and a path planning algorithm for robotic assisted stitching. The overall goal of the research is to enable surgical robots to perform automatic suturing. Suturing comprises several distinct steps, one of them is the stitching. During stitching, reaching the desired exit point is difficult because it must be accomplished without direct visual feedback. Moreover, the stitching is a time consuming procedure repeated multiple times during suturing. Therefore, it would be desirable to enhance the surgical robots with the ability of performing automatic suturing. The focus of this work is on the automation of the stitching task. The thesis presents a model based path planning algorithm for the autonomous stitching. The method uses a nonlinear model for the curved needle - soft tissue interaction. The tissue is modeled as a deformable object using continuum mechanics tools. This thesis uses a mesh free deformable tissue model namely, Reproducing Kernel Particle Method (RKPM). RKPM was chosen as it has been proven to accurately handle large deformation and requires no re-meshing algorithms. This method has the potential to be more realistic in modeling various material characteristics by using appropriate strain energy functions. The stitching task is simulated using a constrained deformable model; the deformable tissue is constrained by the interaction with the curved needle. The stitching model was used for needle trajectory path planning during stitching. This new path planning algorithm for the robotic stitching was developed, implemented, and evaluated. Several simulations and experiments were conducted. The first group of simulations comprised random insertions from different insertion points without planning to assess the modeling method and the trajectory of the needle inside the tissue. Then the parameters of the simulations were set according to the measured experimental parameters. The proposed path planning method was tested using a surgical ETHICON needle of type SH 1=2 Circle with the radius of 8:88mm attached to a robotic manipulator. The needle was held by a grasper which is attached to the robotic arm. The experimental results illustrate that the path planned curved needle insertions are fifty percent more accurate than the unplanned ones. The results also show that this open loop approach is sensitive to model parameters. Master of Applied Science (MASc)