Characterisation of a nuclear cave environment utilising an autonomous swarm of heterogeneous robots

As nuclear facilities come to the end of their operational lifetime, safe decommissioning becomes a more prevalent issue. In many such facilities there exist ‘nuclear caves’. These caves constitute areas that may have been entered infrequently, or even not at all, since the construction of the facility. Due to this, the topography and nature of the contents of these nuclear caves may be unknown in a number of critical aspects, such as the location of dangerous substances or significant physical blockages to movement around the cave. In order to aid safe decommissioning, autonomous robotic systems capable of characterising nuclear cave environments are desired. The research put forward in this thesis seeks to answer the question: is it possible to utilise a heterogeneous swarm of autonomous robots for the remote characterisation of a nuclear cave environment? This is achieved through examination of the three key components comprising a heterogeneous swarm: sensing, locomotion and control. It will be shown that a heterogeneous swarm is not only capable of performing this task, it is preferable to a homogeneous swarm. This is due to the increased sensory and locomotive capabilities, coupled with more efficient explorational prowess when compared to a homogeneous swarm

Climbing Robot for Steel Bridge Inspection: Design Challenges

Inspection of bridges often requires high risk operations such as working at heights, in confined spaces, in hazardous environments; or sites inaccessible by humans. There is significant motivation for robotic solutions which can carry out these inspection tasks. When inspection robots are deployed in real world inspection scenarios, it is inevitable that unforeseen challenges will be encountered. Since 2011, the New South Wales Roads & Maritime Services and the Centre of Excellence for Autonomous Systems at the University of Technology, Sydney, have been working together to develop an innovative climbing robot to inspect high risk locations on the Sydney Harbour Bridge. Many engineering challenges have been faced throughout the development of several prototype climbing robots, and through field trials in the archways of the Sydney Harbour Bridge. This paper will highlight some of the key challenges faced in designing a climbing robot for inspection, and then present an inchworm inspired robot which addresses many of these challenges

Infrastructure robotics: Research challenges and opportunities

Infrastructure robotics is about research on and development of methodologies that enable robotic systems to be used in civil infrastructure inspection, maintenance and rehabilitation. This paper briefly discusses the current research challenges and opportunities in infrastructure robotics, and presents a review of the research activities and projects in this field at the Centre for Autonomous Systems, University of Technology Sydney

Airborne chemical sensing with mobile robots

Airborne chemical sensing with mobile robots has been an active research areasince the beginning of the 1990s. This article presents a review of research work in this field,including gas distribution mapping, trail guidance, and the different subtasks of gas sourcelocalisation. Due to the difficulty of modelling gas distribution in a real world environmentwith currently available simulation techniques, we focus largely on experimental work and donot consider publications that are purely based on simulations

Long-term experiments with an adaptive spherical view representation for navigation in changing environments

Real-world environments such as houses and offices change over time, meaning that a mobile robot’s map will become out of date. In this work, we introduce a method to update the reference views in a hybrid metric-topological map so that a mobile robot can continue to localize itself in a changing environment. The updating mechanism, based on the multi-store model of human memory, incorporates a spherical metric representation of the observed visual features for each node in the map, which enables the robot to estimate its heading and navigate using multi-view geometry, as well as representing the local 3D geometry of the environment. A series of experiments demonstrate the persistence performance of the proposed system in real changing environments, including analysis of the long-term stability

Practical constraints on real time Bayesian filtering for NDE applications

An experimental evaluation of Bayesian positional filtering algorithms applied to mobile robots for Non-Destructive Evaluation is presented using multiple positional sensing data – a real time, on-robot implementation of an Extended Kalman and Particle filter was used to control a robot performing representative raster scanning of a sample. Both absolute and relative positioning were employed – the absolute being an indoor acoustic GPS system that required careful calibration. The performance of the tracking algorithms are compared in terms of computational cost and the accuracy of trajectory estimates. It is demonstrated that for real time NDE scanning, the Extended Kalman Filter is a more sensible choice given the high computational overhead for the Particle filter

Reconfiguration of a climbing robot in an all-terrain hexapod robot

This work presents the reconfiguration from a previous climbing robot to an all-terrain robot for applications in outdoor environments. The original robot is a six-legged climbing robot for high payloads. This robot has used special electromagnetic feet in order to support itself on vertical ferromagnetic walls to carry out specific tasks. The reconfigured all-terrain hexapod robot will be able to perform different applications on the ground, for example, as inspection platform for humanitarian demining tasks. In this case, the reconfigured hexapod robot will load a scanning manipulator arm with a specific metal detector as end-effector. With the implementation of the scanning manipulator on the hexapod robot, several tasks about search and localisation of antipersonnel mines would be carried out. The robot legs have a SCARA configuration, which allows low energy consumption when the robot performs trajectories on a quasi-flat terrain.Peer reviewe

Efficient algorithms and a two-stage framework for autonomous exploration of complex 3D environments using a climbing robot

University of Technology Sydney. Faculty of Engineering and Information Technology.Enabling robots to autonomously explore complex 3D environments is crucial in facilitating the automation of many real-world tasks. There exist many algorithms for exploring unknown environments with autonomous robots. Most of these are restricted to the 2D case, or to cases where the robot can be abstracted as a holonomic point robot. Algorithms that deal with the 3D case restrict the robot’s possible positions to the 2D plane, or assume that the robot can freely move through any empty space, like an idealised quadrocopter. This thesis presents a two-stage exploration framework that allows robots to consider any adherable surface in a 3D environment as a potential position from which to conduct exploration. The framework is therefore suitable to any robotic platform that must at all times maintain contact with a surface, but where this surface need not be the floor plane. A Nearest Neighbours Exploration Approach (NNEA) is developed to accomplish exploration of the environment immediately surrounding the robot when the robot is fixed to a position on a surface. In this approach, the Next Best Viewpoint is selected first by evaluating and choosing between candidate viewpoints that are within a bounded range of the robot’s current position. NNEA is demonstrated in experiments in a real bridge environment for the case of a high degrees of freedom (DOF) robot arm with a fixed base. NNEA is shown to result in faster exploration times in the case of a high-DOF robot arm in a fixed base position. Four frontier detection algorithms are proposed and investigated for determining the set of frontiers—the boundary between known and unknown space—after each map update. The resulting frontiers are used to limit which candidate positions need to be considered for exploration. The novel frontier detection algorithms are compared to other state of the art algorithms and are found to be suited for efficient frontier detection in different situations. A novel graph-based method for selecting the Next Best Base location (NBB) is presented in which the map is used to create an updated graph of possible positions for the robot base, sampled from all surfaces. Positions that are sufficiently close to the frontiers are selected as candidate positions for the robot to move to next. The information that could be gained from each reachable candidate position is estimated. A cost function determines which candidate is the best to move to next, and the robot moves to that position to take another sequence of scans. This method is demonstrated in simulations and experiments to be efficient in minimising the computation required to select and move to the NBB. The exploration framework and the developed algorithms and approach are demonstrated in simulation in an environment made up of unconnected surfaces, large enough that the robot is required to repeatedly move through the environment in order to fully explore it. The framework is shown to result in efficient exploration of the observable environment