Roger Woodman

Autonomous vehicles (AVs) operating in shared urban environments, often referred to as “pods”, will constantly have to interact with pedestrians. As a result, an effective strategy will be required for pods to continue operating, while in close proximity to people. This strategy could be in terms of active negotiation, where a pod identifies a person and gives way; or a more passive strategy, such as requiring pods to travel close together in platoons, in order to reduce the number of individual vehicle encounters. For this latter example, it is critical to understand how the spaces between pods and AVs in general are perceived by pedestrians, and what factors will persuade and dissuade crossing. Therefore, this paper seeks to understand this relationship, and presents results from a pedestrian gap acceptance study for platoons. To ensure the safety of participants, a virtual environment was used instead of real vehicles. The goal of the experiment described in this paper, is to understand the gap acceptance behaviour of participants, when presented with a platoon of pods in different environments. The experiment evaluated four vehicle speeds, from 1 km/h to 16 km/h, four temporal gaps, from 2 s to 5 s, and two environments. These environments were a typical road layout, with footpath and line markings, and a shared space, where all markings and separation between pod and pedestrian were removed. For each scenario, participants were asked if they would cross between the pods and how safe they felt about the situation, recorded as a Likert score. The results suggest that people are more likely to attempt to cross between a platoon of pods when they are travelling closer together in a shared space (no line markings or separation between vehicles and pedestrian), compared to a road environment (separated by raised pavement and road markings). However, it was also found that people’s subjective rating of safeness was higher in the road environment, when presented with a platoon of pods, compared to the shared space.

This paper presents results from a series of focus groups, aimed at enhancing technical engineering system requirements, for a public transport system, encompassing a fleet of platooning low-speed autonomous vehicles (LSAV; aka pods) in urban areas. A critical review of the pods was conducted, as part of a series of technical workshops, to examine the key areas of the system that could affect users and other stakeholders, such as businesses and the public. These initial findings were used to inform a series of focus groups, aimed at identifying the public's views of multiple autonomous vehicles being deployed in a pedestrianised area that can join and form platoons. Analysis of findings from the focus groups suggests that while people view platooning public transport vehicles favourably as a passenger, they have some concerns from a pedestrian perspective. Thematic analysis was applied to these findings and a systematic approach was used to identify where subjective outputs could be formalised to inform requirements. Finally, a step-by-step requirements elicitation process is presented that illustrates the method used to convert qualitative user data to objective engineering requirements.

Robot manufacturers will be required to demonstrate objectively that all reasonably foreseeable hazards have been identified in any robotic product design that is to be marketed commercially. This is problematic for autonomous mobile robots because conventional methods, which have been developed for automatic systems do not assist safety analysts in identifying non-mission interactions with environmental features that are not directly associated with the robot’s design mission, and which may comprise the majority of the required tasks of autonomous robots. In this paper we develop a new variant of preliminary hazard analysis that is explicitly aimed at identifying non-mission interactions by means of new sets of guidewords not normally found in existing variants. We develop the required features of the method and describe its application to several small trials conducted at Bristol Robotics Laboratory in the 2011–2012 period.

In recent years there has been a concerted effort to address many of the safety issues associated with physical human–robot interaction (pHRI). However, a number of challenges remain. For personal robots, and those intended to operate in unstructured environments, the problem of safety is compounded. In this paper we argue that traditional system design techniques fail to capture the complexities associated with dynamic environments. We present an overview of our safety-driven control system and its implementation methodology. The methodology builds on traditional functional hazard analysis, with the addition of processes aimed at improving the safety of autonomous personal robots. This will be achieved with the use of a safety system developed during the hazard analysis stage. This safety system, called the safety protection system, will initially be used to verify that safety constraints, identified during hazard analysis, have been implemented appropriately. Subsequently it will serve as a high-level safety enforcer, by governing the actions of the robot and preventing the control layer from performing unsafe operations. To demonstrate the effectiveness of the design, a series of experiments have been conducted using a MobileRobots PeopleBot. Finally, results are presented demonstrating how faults injected into a controller can be consistently identified and handled by the safety protection system.

Autonomy in mobile robots and other embodied systems means the performance of an intended task or mission in an environment without human intervention. However, many autonomous robots are generally required to operate in their environments for extended periods, often exceeding the duration of their intended missions. This can result in periods when robots must perform tasks to ensure their continued availability and safety between the end of one mission and the start of the next. Additionally, the open and unstructured nature of many environments will mean that robots will be required to perform tasks that do not fall within their direct mission specification, but are required to manage unplanned interactions with other environmental features. We argue that current system engineering and safety assessment methods are insufficient for the problem of designing this class of autonomous robots, as they do not reliably identify the non-mission aspects of extended operation. In particular, this applies to the problem of hazard identification.

Robotic systems require rigorous analysis at all stages of development to ensure system safety. The manufacturing industry has developed many of the robotic design methods used today. These methods were adapted from design practices taken from other industrial sectors [3]. Incorporated into the design process are proven techniques such as hazard analysis, failure analysis and testing. In addition a number of robotic safety standards have been developed; most notably ISO 10218-1 [4]. As discussed by [1], these methods are not appropriate for designing safe robots operating in unstructured environments, due to the high complexity associated with a system that must adapt to a changing environment.

This paper presents a novel robot control architecture for use with personal robots, and argues its potential for improving the safety of these types of system, when compared to existing approaches. The proposed architecture design separates the control system into two distinct areas, one area responsible for safe operation and the other for coordinating tasks. The architecture design is formed in a hierarchical structure, composed of low-level deliberative control modules and high-level behavioural safety modules. It is argued that as a result of removing safety considerations from the design of task routines, increasingly complex tasks can be completed safely, which are both more flexible to environmental changes and easier to coordinate.

In recent years there has been a concerted effort to address many of the safety issues associated with physical human-robot interaction (pHRI). However, a number of challenges remain. For personal robots, and those intended to operate in unstructured environments, the problem of safety is compounded. We believe that the safety issue is a primary factor in wide scale adoption of personal robots, and until these issues are addressed, commercial enterprises will be unlikely to invest heavily in their development. In this thesis we argue that traditional system design techniques fail to capture the complexities associated with dynamic environments. This is based on a careful analysis of current design processes, which looks at how effectively they identify hazards that may arise in typical environments that a personal robot may be required to operate in. Based on this investigation, we show how the adoption of a hazard check list that highlights particular hazardous areas, can be used to improve current hazard analysis techniques. A novel safety-driven control system architecture is presented, which attempts to address many of the weaknesses identified with the present designs found in the literature. The new architecture design centres around safety, and the concept of a 'safety policy' is introduced. These safety policies are shown to be an effective way of describing safety systems as a set of rules that dictate how the system should behave in potentially hazardous situations. A safety analysis methodology is introduced, which integrates both our hazard analysis technique and the implementation of the safety layer of our control system. This methodology builds on traditional functional hazard analysis, with the addition of processes aimed to improve the safety of personal robots. This is achieved with the use of a safety system, developed during the hazard analysis stage. This safety system, called the safety protection system, is initially used to verify that safety constraints, identified during hazard analysis, have been implemented appropriately. Subsequently it serves as a high-level safety enforcer, by governing the actions of the robot and preventing the control layer from performing unsafe operations. To demonstrate the effectiveness of the design, a series of experiments have been conducted using both simulation environments and physical hardware. These experiments demonstrate the effectiveness of the safety-driven control system for performing tasks safely, while maintaining a high level of availability.

Face recognition has received much interest in the last decade, as the need for reliable personal identification security has become ever more critical.  At present for face recognition to be a viable personal identification method an accurate low cost solution is required.  Many two-dimensional (2D) face recognition systems have been implemented, which demonstrate the potential of using face recognition, although at present these systems are often unreliable.  To produce more reliable face recognition systems, a recent trend has been to use three-dimensional (3D) data, which as research has shown is more accurate and robust than traditional 2D techniques.  3D face recognition systems, however, are often expensive relative to 2D alternatives, as precise capture equipment is required.  This research project has identified photometric stereo as a low cost 3D capture system, which has been little researched in the area of face recognition.  An investigation is presented, which evaluates the capabilities of photometric stereo for use in the area of face recognition by means of a number of experiments.  These experiments are conducted using a photometric stereo system, designed and implemented for this research project.

""Fuzzy logic systems have been seen by many to be an alternative method of control, to more classical approaches, such as PID controllers. The main advantage of fuzzy controllers is there ability to cope, to a certain degree, with changes in the system being controlled. This ability usually comes at the cost of accuracy when compared to conventional controllers.

This report discusses the use of an Evolutionary Algorithm (EA) as a method to improve the accuracy of a Mamdani fuzzy controller. The motivation behind this approach is to find a technique, which could be used to optimise any fuzzy controller to improve accuracy. Thus, to produce a controller which is able to cope with certain changes in the system with the benefits of fuzzy logic, such as linguistic interpretability and the accuracy of conventional controllers.""

During recent times traditional personal identification methods have come under much scrutiny. This has mainly been due to increase fraud and attention surrounding terrorist activities. To meet the modern day needs of personal identification, a number of more robust methods have been researched. These methods, which aim to uniquely identify individuals using information about a person’s physical make up, are known as biometrics. An investigation into fourteen such biometric techniques has been conducted by Jain et al. (2004). Their research identifies face recognition as the only method with the potential of providing a non-intrusive system that could be publicly accepted in today’s society.

At present, two main factors restrain the widespread use of face recognition technology; these factors are system reliability and cost. The issue of system reliability has been addressed in many research projects, with recent advances in 3D imaging technology, reported by Zhao and Chellappa (2006), demonstrating increased levels of recognition accuracy compared to traditional 2D systems. The use of 3D data to produce 3D face models has been shown as an effective approach for handling a number of key difficulties in face recognition, which can degrade system performance. These issues are associated with illumination variance, head pose orientation and changes in facial expression. However, although 3D imaging systems have been shown to outperform equivalent 2D systems, more accurate equipment is often required at a greater expense.

"This article compares and contrasts a number of Adaptive Heuristic Critic (AHC) algorithms, designed to explore a simulated 2D grid for a goal value. Learning is achieved with the use of a Temporal Difference (TD).

The first algorithm presented, performs a basic random search for the goal. The other algorithms build on this basic search, with the use a type of Tabu search which adds negative reinforcement to previously visited locations in order to avoid them, this information is then either discard or retained for subsequent runs. In addition two selection methods are explored, one using a strict method of selecting the highest immediate reward and a second using a variable stochastic element. The implementation of these algorithms and selection methods are then tested in simulation and an analysis made of the effectiveness of each."

Multitasking and real-time, in the field of operating systems, are antonymous. Normally all general-purpose operating systems, such as Windows and Mac OS, are multitasking and non real-time. Whereas in the embedded system market operating systems exist that use any of the permutations of multitasking and real-time. The computers used for real-time operating systems (RTOS), compared to most general-purpose computers, need to more reliable and tolerant of changing environmental conditions and be able to cope with varying computation loads.