Dr Alexandre Mendes

This paper addresses several issues related to the approximate solution of the Single Machine Scheduling problem with sequence-dependent setup times using metaheuristic methods. Instances with known optimal solution are solved using a memetic algorithm and a multiple start approach. A fitness landscape analysis is also conducted on a subset of instances to understand the behavior of the two approaches during the optimization process. We also present a novel way to create instances with known optimal solutions from the optimally solved asymmetric travelling salesman problem (ATSP) instances. Finally we argue for the test set of instances to be used in future works as a convenient performance benchmark.

Rail traffic planning and scheduling problems have been challenging academy and industry for a few decades. Specifically, problems in the short term and real-time horizons deal with simultaneous decision-making of trains, stations and terminals. Approaches focused on decentralised decision-making have been successful in delivering real-world committed solutions. This work focuses on decentralised real-time decision-making in a closed freight rail network and applies multi-agent deep reinforcement learning (MADRL) to find efficient timetables. We apply the MADRL model to solve the traffic decisions arising in the Hunter Valley Coal Chain (HVCC) in New South Wales, Australia. The approach uses the same simulation model currently in use for capacity planning of the system, thus allowing tests with real data. The environment is modelled as a decentralised, partially observed Markov decision process (dec-POMDP), where the train, load point, and dump station agents decide upon train movements based on local observations. The observations follow a novel state encoding strategy for rail traffic management composed of nine layers. We benefit from this strategy to apply a decentralised execution with a centralised learning approach through proximal policy optimisation. The experiments revealed a significant performance improvement for the ten instances tested, which reproduce the challenges faced in the HVCC operations. The approach is suitable for varied levels of rail network complexity, generating efficient solutions without scaling issues. The MADRL outperformed the heuristic in use by HVCC's simulation model and a high-performance genetic algorithm in all instances, reaching performance improvements of up to 72.00% and 47.42%, respectively. Therefore, the framework with the MADRL and the simulation model allows its application with real world instances in an efficient and reliable way. These results show the method's consistency and draw a safe path towards a decentralised rail traffic management system.

The ability of an autonomous agent to self-localise within its environment is critically dependent on its ability to make accurate observations of static, salient features. This notion has driven considerable research into the development and improvement of feature extraction and object recognition algorithms, both within RoboCup and the robotics community at large. Instead, this paper focuses on the rarely-considered issue imposed by the limited field of view of humanoid robots; namely, determining an optimal policy for actuating a robot's head, to ensure it observes regions of the environment that will maximise the positional information provided. The complexity of this task is magnified by a number of common computational issues; specifically high dimensional state spaces and noisy environmental observations. This paper details the application of motivated reinforcement learning to partially overcome these issues, leading to an 11% improvement (relative to the null case of uniformly distributed actuation policies) in self-localisation and ball-localisation for an agent trained online for less than one hour. The method is demonstrated as a viable method for improving self-localisation in robotics, without the need for further optimisation of object recognition or tuning of probabilistic filters. © 2014 Springer-Verlag Berlin Heidelberg.

As modern autonomous robots have improved in their ability to demonstrate human-like motor skills and reasoning, the size and complexity of software systems have increased proportionally, with developers actively working to leverage the full processing performance of next-generation computational hardware. This software complexity corresponds with increased difficulty in debugging low-level coding issues, with the traditional methodology of inferring such issues from emergent high-level behaviour rapidly approaching intractability. This paper details the development and functionality of NUbugger: a visual, real-time and open source robot debugging utility that provides the user with comprehensive information regarding low-level functionality. This represents a paradigm shift from corrective to preventative debugging, and concrete examples of the application of NUbugger to the identification of fundamental implementation errors are described. The system implementation facilitates simple and rapid extension or modification, making it a useful utility for debugging any similar complex robotic framework. © 2014 Springer-Verlag Berlin Heidelberg.

Humans have the subconscious ability to create simple abstractions from observations of their physical environment. The ability to consider the colour of an object in terms of "red" or "blue", rather than spatial distributions of reflected light wavelengths, is vital in processing and communicating information about important features within our local environment. The real-time identification of such features in image processing necessitates the software implementation of such a process; segmenting an image into regions of salient colour, and in doing so reducing the information stored and processed from 3-dimensional pixel values to a simple colour class label. This paper details a method by which colour segmentation may be performed offline and stored in a static look-up table, allowing for constant time dimensionality reduction in an arbitrary environment of coloured features. The machine learning framework requires no human supervision, and its performance is evaluated in terms of feature classification performance within a RoboCup robot soccer environment. The developed system is demonstrated to yield an 8% improvement over slower traditional methods of manual colour mapping. © 2014 Springer-Verlag Berlin Heidelberg.

This paper presents a hybrid approach, composed of a genetic algorithm and a linear programming method, to achieve an efficient pipeline network operation. The pipeline network optimization consists of the determination of pump scheduling over a short-Term horizon, usually one or more days ahead. The resulting mathematical problem has a dynamic and combinatorial characteristic, in which a sub-optimal solution was obtained through these two mathematical tools in a short computational time. The approach was applied in a Pipeline Network to a study case based on the Patagonia Argentina, which is comprised of 16 tanks and linked pumps, with 66 kilometers of pipelines, that transport the production of more than 100 wells to a pre-processing plant. The goal was to obtain a constant input flow rate at the plant respecting physical and chemical processes requirements.