Transit Performance Measures in California

This research is the result of a California Department of Transportation (Caltrans) request to assess the most commonly available transit performance measures in California. Caltrans wanted to understand performance measures and data used by Metropolitan Planning Organizations (MPOs) and transit agencies to help it develop statewide measures. This report serves as a summary reference guide to help Caltrans understand the numerous and diverse performance measures used by MPOs and transit agencies in California. First, investigators review the available literature to identify a complete transit performance framework for the purposes of organizing agency measures, metrics, and data sources. Next, they review the latest transit performance measures documented in planning reports for the four largest MPOs in California (San Francisco Bay Area, Los Angeles, San Diego, and Sacramento). Researchers pay special attention to the transit performance measures used by these MPOs, because these measures are available for the majority of California’s population. Finally, investigators summarize 231 performance measures used by a total 26 local transit agencies in the State of California, based on transit planning documents available on the internet

Real Time Motion Planning for Path Coverage with Applications in Ocean Surveying

Ocean surveying is the acquisition of acoustic data representing various features of the seafloor and the water above it, including water depth, seafloor composition, the presence of fish, and more. Historically, this was a task performed solely by manned vessels, but with advances in robotics and sensor technology, autonomous surface vehicles (ASVs) with sonar equipment are beginning to supplement and replace their more costly crewed counterparts. The popularity of these vessels calls for advances in software to control them. In this thesis we define the problem of path coverage to represent and generalize that of ocean surveying, and propose a real-time motion planning algorithm to solve it. We prove theorems of completeness and local asymptotic optimality regarding the proposed algorithm, and evaluate it in a simulated environment. We also discover a lack of robustness in the Dubins vehicle model when applied to real-time motion planning. We implement a model-predictive controller and other components for an autonomous surveying system, and evaluate it in simulation. The system documented in this thesis takes a step towards fully autonomous ocean surveying, and proposes further extensions that get even closer to that goal

REACTIVE MOTION REPLANNING FOR HUMAN-ROBOT COLLABORATION

Negli ultimi anni si è assistito a un incremento significativo di robot che condividono lo spazio di lavoro con operatori umani, per combinare la rapidità e la precisione proprie dei robot con l'adattabilità e l'intelligenza umana. Tuttavia, questa integrazione ha introdotto nuove sfide in termini di sicurezza ed efficienza della collaborazione. I robot devono essere in grado di adattarsi prontamente ai cambiamenti nell'ambiente circostante, come i movimenti degli operatori, adeguando in tempo reale il loro percorso per evitare collisioni, preferibilmente senza interruzioni. Inoltre, nelle operazioni di collaborazione tra uomo e robot, le traiettorie ripianificate devono rispettare i protocolli di sicurezza, al fine di evitare rallentamenti e fermate dovute alla prossimità eccessiva del robot all'operatore. In questo contesto è fondamentale fornire soluzioni di alta qualità in tempi rapidi per garantire la reattività del robot. Le tecniche di ripianificazione tradizionali tendono a faticare in ambienti complessi, soprattutto quando si tratta di robot con molti gradi di libertà e numerosi ostacoli di dimensioni considerevoli. La presente tesi affronta queste sfide proponendo un nuovo algoritmo sampling-based di ripianificazione del percorso per manipolatori robotici. Questo approccio sfrutta percorsi pre-calcolati per generare rapidamente nuove soluzioni in poche centinaia di millisecondi. Inoltre, incorpora una funzione di costo che guida l'algoritmo verso soluzioni che rispettano lo standard di sicurezza ISO/TS 15066, riducendo così gli interventi di sicurezza e promuovendo una cooperazione efficiente tra uomo e robot. Viene inoltre presentata un'architettura per gestire il processo di ripianificazione durante l'esecuzione del percorso del robot. Infine, viene introdotto uno strumento software che semplifica l'implementazione e il testing degli algoritmi di ripianificazione del percorso. Simulazioni ed esperimenti condotti su robot reali dimostrano le prestazioni superiori del metodo proposto rispetto ad altre tecniche popolari.In recent years, there has been a significant increase in robots sharing workspace with human operators, combining the speed and precision inherent to robots with human adaptability and intelligence. However, this integration has introduced new challenges in terms of safety and collaborative efficiency. Robots now need to swiftly adjust to dynamic changes in their environment, such as the movements of operators, altering their path in real-time to avoid collisions, ideally without any disruptions. Moreover, in human-robot collaborations, replanned trajectories should adhere to safety protocols, preventing safety-induced slowdowns or stops caused by the robot's proximity to the operator. In this context, quickly providing high-quality solutions is crucial for ensuring the robot's responsiveness. Conventional replanning techniques often fall short in complex environments, especially for robots with numerous degrees of freedom contending with sizable obstacles. This thesis tackles these challenges by introducing a novel sampling-based path replanning algorithm tailored for robotic manipulators. This approach exploits pre-computed paths to generate new solutions in a few hundred milliseconds. Additionally, it integrates a cost function that steers the algorithm towards solutions that strictly adhere to the ISO/TS 15066 safety standard, thereby minimizing the need for safety interventions and fostering efficient cooperation between humans and robots. Furthermore, an architecture for managing the replanning process during the execution of the robot's path is introduced. Finally, a software tool is presented to streamline the implementation and testing of path replanning algorithms. Simulations and experiments conducted on real robots demonstrate the superior performance of the proposed method compared to other popular techniques

Lean Thinking: Theory, Application and Dissemination

This book was written and compiled by the University of Huddersfield to share the learnings and experiences of seven years of Knowledge Transfer Partnership (KTP) and Economic and Social Research Council (ESRC) funded projects with the National Health Service (NHS). The focus of these projects was the implementation of Lean thinking and optimising strategic decision making processes. Each of these projects led to major local improvements and this book explains how they were achieved and compiles the lessons learnt. The book is split into three chapters; Lean Thinking Theory, Lean Thinking Applied and Lean Thinking Dissemination

Transit Performance Measures in California

This research is the result of a California Department of Transportation (Caltrans) request to assess the most commonly available transit performance measures in California. Caltrans wanted to understand performance measures and data used by Metropolitan Planning Organizations (MPOs) and transit agencies to help it develop statewide measures. This report serves as a summary reference guide to help Caltrans understand the numerous and diverse performance measures used by MPOs and transit agencies in California. First, investigators review the available literature to identify a complete transit performance framework for the purposes of organizing agency measures, metrics, and data sources. Next, they review the latest transit performance measures documented in planning reports for the four largest MPOs in California (San Francisco Bay Area, Los Angeles, San Diego, and Sacramento). Researchers pay special attention to the transit performance measures used by these MPOs, because these measures are available for the majority of California’s population. Finally, investigators summarize 231 performance measures used by a total 26 local transit agencies in the State of California, based on transit planning documents available on the internet

Auction-Based Task Allocation and Motion Planning for Multi-Robot Systems with Human Supervision

This paper presents a task allocation strategy for a multi-robot system with a human supervisor. The multi-robot system consists of a team of heterogeneous robots with different capabilities that operate in a dynamic scenario that can change in the robots’ capabilities or in the operational requirements. The human supervisor can intervene in the operation scenario by approving the final plan before its execution or forcing a robot to execute a specific task. The proposed task allocation strategy leverages an auction-based method in combination with a sampling-based multi-goal motion planning. The latter is used to evaluate the costs of execution of tasks based on realistic features of paths. The proposed architecture enables the allocation of tasks accounting for priorities and precedence constraints, as well as the quick re-allocation of tasks after a dynamic perturbation occurs –a crucial feature when the human supervisor preempts the outcome of the algorithm and makes manual adjustments. An extensive simulation campaign in a rescue scenario validates our approach in dynamic scenarios comprising a sensor failure of a robot, a total failure of a robot, and a human-driven re-allocation. We highlight the benefits of the proposed multi-goal strategy by comparing it with single-goal motion planning strategies at the state of the art. Finally, we provide evidence for the system efficiency by demonstrating the powerful synergistic combination of the auction-based allocation and the multi-goal motion planning approach

Managing a Fleet of Autonomous Mobile Robots (AMR) using Cloud Robotics Platform

In this paper, we provide details of implementing a system for managing a fleet of autonomous mobile robots (AMR) operating in a factory or a warehouse premise. While the robots are themselves autonomous in its motion and obstacle avoidance capability, the target destination for each robot is provided by a global planner. The global planner and the ground vehicles (robots) constitute a multi agent system (MAS) which communicate with each other over a wireless network. Three different approaches are explored for implementation. The first two approaches make use of the distributed computing based Networked Robotics architecture and communication framework of Robot Operating System (ROS) itself while the third approach uses Rapyuta Cloud Robotics framework for this implementation. The comparative performance of these approaches are analyzed through simulation as well as real world experiment with actual robots. These analyses provide an in-depth understanding of the inner working of the Cloud Robotics Platform in contrast to the usual ROS framework. The insight gained through this exercise will be valuable for students as well as practicing engineers interested in implementing similar systems else where. In the process, we also identify few critical limitations of the current Rapyuta platform and provide suggestions to overcome them.Comment: 14 pages, 15 figures, journal pape

High-DOF Motion Planning in Dynamic Environments using Trajectory Optimization

Motion planning is an important problem in robotics, computer-aided design, and simulated environments. Recently, robots with a high number of controllable joints are increasingly used for different applications, including in dynamic environments with humans and other moving objects. In this thesis, we address three main challenges related to motion planning algorithms for high-DOF robots in dynamic environments: 1) how to compute a feasible and constrained motion trajectory in dynamic environments; 2) how to improve the performance of realtime computations for high-DOF robots; 3) how to model the uncertainty in the environment representation and the motion of the obstacles. We present a novel optimization-based algorithm for motion planning in dynamic environments. We model various constraints corresponding to smoothness, as well as kinematics and dynamics bounds, as a cost function, and perform stochastic trajectory optimization to compute feasible high-dimensional trajectories. In order to handle arbitrary dynamic obstacles, we use a replanning framework that interleaves planning with execution. We also parallelize our approach on multiple CPU or GPU cores to improve the performance and perform realtime computations. In order to deal with the uncertainty of dynamic environments, we present an efficient probabilistic collision detection algorithm that takes into account noisy sensor data. We predict the future obstacle motion as Gaussian distributions, and compute the bounded collision probability between a high-DOF robot and obstacles. We highlight the performance of our algorithms in simulated environments as well as with a 7-DOF Fetch arm.Doctor of Philosoph