Practical path planning and obstacle avoidance for autonomous mowing

There is a need for systems which can autonomously perform coverage tasks on large outdoor areas. Unfortunately, the state-of-the-art is to use GPS based localization, which is not suitable for precise operations near trees and other obstructions. In this paper we present a robotic platform for autonomous coverage tasks. The system architecture integrates laser based localization and mapping using the Atlas Framework with Rapidly-Exploring Random Trees path planning and Virtual Force Field obstacle avoidance. We demonstrate the performance of the system in simulation as well as with real world experiments.

Intelligent collision avoidance, control and guidance

This paper reviews a variety of advanced signal processing algorithms that have been developed at the University of Southampton as part of the Prometheus (Programme for European traffic flow with highest efficiency and unprecedented safety) programme to achieve an intelligent driver warning system (IDWS). The IDWS includes the detection of road edges, lanes, obstacles and their tracking and identification, estimates of time to collision, and behavioural modelling of drivers for a variety of scenarios. The underlying algorithms are briefly discussed in support of the IDWS.

Collision avoidance between UAV clusters using swarm intelligence techniques

In this article, several basic swarming laws for Unmanned Aerial Vehicles (UAVs) are developed for both two-dimensional (2D) plane and three-dimensional (3D) space. Effects of these basic laws on the group behaviour of swarms of UAVs are studied. It is shown that when cohesion rule is applied an equilibrium condition is reached in which all the UAVs settle at the same altitude on a circle of constant radius. It is also proved analytically that this equilibrium condition is stable for all values of velocity and acceleration. A decentralised autonomous decision-making approach that achieves collision avoidance without any central authority is also proposed in this article. Algorithms are developed with the help of these swarming laws for two types of collision avoidance, Group-wise and Individual, in 2D plane and 3D space. Effect of various parameters are studied on both types of collision avoidance schemes through extensive simulations.

On the moderators of trait avoidance motivation in predicting cognitive biases and adjustment

The present research focused on motivational and personality traits measuring individual differences in the experience of negative affect, in reactivity to negative events, and in the tendency to avoid threats. In this thesis, such traits (i.e., neuroticism and dispositional avoidance motivation) are jointly referred to as trait avoidance motivation. The seven studies presented here examined the moderators of such traits in predicting risk judgments, negatively biased processing, and adjustment. Given that trait avoidance motivation encompasses reactivity to negative events and tendency to avoid threats, it can be considered surprising that this trait does not seem to be related to risk judgments and that it seems to be inconsistently related to negatively biased information processing. Previous work thus suggests that some variable(s) moderate these relations. Furthermore, recent research has suggested that despite the close connection between trait avoidance motivation and (mal)adjustment, measures of cognitive performance may moderate this connection. However, it is unclear whether this moderation is due to different response processes between individuals with different cognitive tendencies or abilities, or to the genuinely buffering effect of high cognitive ability against the negative consequences of high trait avoidance motivation. Studies 1-3 showed that there is a modest direct relation between trait avoidance motivation and risk judgments, but studies 2-3 demonstrated that state motivation moderates this relation. In particular, individuals in an avoidance state made high risk judgments regardless of their level of trait avoidance motivation. This result explained the disparity between the theoretical conceptualization of avoidance motivation and the results of previous studies suggesting that the relation between trait avoidance motivation and risk judgments is weak or nonexistent. Studies 5-6 examined threat identification tendency as a moderator for the relationship between trait avoidance motivation and negatively biased processing. However, no evidence for such moderation was found. Furthermore, in line with previous work, the results of studies 5-6 suggested that trait avoidance motivation is inconsistently related to negatively biased processing, implying that theories concerning traits and information processing may need refining. Study 7 examined cognitive ability as a moderator for the relation between trait avoidance motivation and adjustment, and demonstrated that cognitive ability moderates the relation between trait avoidance motivation and indicators of both self-reported and objectively measured adjustment. Thus, the results of Study 7 supported the buffer explanation for the moderating influence of cognitive performance. To summarize, the results showed that it is possible to find factors that consistently moderate the relations between traits and important outcomes (e.g. adjustment). Identifying such factors and studying their interplay with traits is one of the most important goals of current personality research. The present thesis contributed to this line of work in relation to trait avoidance motivation.

A Reactive Inverse PN Algorithm for Collision Avoidance among Multiple Unmanned Aerial Vehicles

While performing a mission, multiple Unmanned Aerial Vehicles (UAVs) need to avoid each other to prevent collisions among them. In this paper, we design a collision avoidance algorithm to resolve the conflict among UAVs that are on a collision course while flying to heir respective destinations. The collision avoidance algorithm consist of each UAV that is on a collision course reactively executing a maneuver that will, as in `inverse' Proportional Navigation (PN), increase Line of Sight (LOS) rate between them, resulting in a `pulling out' of collision course. The algorithm is tested for high density traffic scenarios as well as for robustness in the presence of noise.

More than naming and shaming needed to stop corporate tax avoidance

The report of the Senate Economics References Committee inquiry into corporate tax avoidance comes with the subtitle – “You cannot tax what you cannot see”, with a strong focus on increased transparency. The majority of the 17 recommendations in the interim report relate to improved transparency of the tax affairs of corporate taxpayers. This is a significant step in the right direction. Recent experiences in the war on corporate tax avoidance both in Australia and overseas confirm that “information is power”. Most notably, we have seen increased transparency changing the behaviour of multinational enterprises as well as inducing governments to act.

Government cracks down on tax avoidance through foreign investment rules

The Turnbull Government announced yet another measure aimed at addressing tax base erosion and profit shifting, placing additional requirements on new foreign investment under the existing national interest test. In the last 12 months Australia has seen various reforms within the tax system. However, this latest initiative is a shift as it links Australia’s tax regime with its foreign investment regime. It sends a broader signal to the market that Australia will look beyond the collection of tax revenues to a consideration of national interest.

The pulling power of chocolate: Effects of approach-avoidance training on approach bias and consumption

Previous research has shown that action tendencies to approach alcohol may be modified using computerized ApproacheAvoidance Task (AAT), and that this impacted on subsequent consumption. A recent paper in this journal (Becker, Jostman, Wiers, & Holland, 2015) failed to show significant training effects for food in three studies: Nor did it find effects on subsequent consumption. However, avoidance training to high calorie foods was tested against a control rather than Approach training. The present study used a more comparable paradigm to the alcohol studies. It randomly assigned 90 participants to ‘approach’ or ‘avoid’ chocolate images on the AAT, and then asked them to taste and rate chocolates. A significant interaction of condition and time showed that training to avoid chocolate resulted in faster avoidance responses to chocolate images, compared with training to approach it. Consistent with Becker et al.'s Study 3, no effect was found on amounts of chocolate consumed, although a newly published study in this journal (Schumacher, Kemps, & Tiggemann, 2016) did do so. The collective evidence does not as yet provide solid basis for the application of AAT training to reduction of problematic food consumption, although clinical trials have yet to be conducted.

Thin capitalisation – the multinational tax avoidance strategy the budget forgot

We have already seen major amendments to Australia’s tax regime to tackle base erosion and profit shifting (BEPS). Several more significant measures were announced in the federal budget, most notably the diverted profits tax, aimed at multinationals which shift tax to a lower taxing jurisdiction. Yet to date, a very simple tax minimisation strategy has been largely ignored in the ongoing reforms and was ignored in the federal budget. Excessive debt loading is a problem that not been afforded the same attention as other aggressive tax planning strategies adopted by multinationals.

Ship collision avoidance and COLREGS compliance using simulation-based control behavior selection with predictive hazard assessment

This paper describes a concept for a collision avoidance system for ships, which is based on model predictive control. A finite set of alternative control behaviors are generated by varying two parameters: offsets to the guidance course angle commanded to the autopilot and changes to the propulsion command ranging from nominal speed to full reverse. Using simulated predictions of the trajectories of the obstacles and ship, compliance with the Convention on the International Regulations for Preventing Collisions at Sea and collision hazards associated with each of the alternative control behaviors are evaluated on a finite prediction horizon, and the optimal control behavior is selected. Robustness to sensing error, predicted obstacle behavior, and environmental conditions can be ensured by evaluating multiple scenarios for each control behavior. The method is conceptually and computationally simple and yet quite versatile as it can account for the dynamics of the ship, the dynamics of the steering and propulsion system, forces due to wind and ocean current, and any number of obstacles. Simulations show that the method is effective and can manage complex scenarios with multiple dynamic obstacles and uncertainty associated with sensors and predictions.

Evolving Philosophies on Autonomous Obstacle/Collision Avoidance of Unmanned Aerial Vehicles

Much of the benefits of deploying unmanned aerial vehicles can be derived from autonomous missions. For such missions, however, sense-and-avoid capability (i.e., the ability to detect potential collisions and avoid them) is a critical requirement. Collision avoidance can be broadly classified into global and local path-planning algorithms, both of which need to be addressed in a successful mission. Whereas global path planning (which is mainly done offline) broadly lays out a path that reaches the goal point, local collision-avoidance algorithms, which are usually fast, reactive, and carried out online, ensure safety of the vehicle from unexpected and unforeseen obstacles/collisions. Even though many techniques for both global and local collision avoidance have been proposed in the recent literature, there is a great interest around the globe to solve this important problem comprehensively and efficiently and such techniques are still evolving. This paper presents a brief overview of a few promising and evolving ideas on collision avoidance for unmanned aerial vehicles, with a preferential bias toward local collision avoidance.

Rendezvous of Multiple UAVs with Collision Avoidance Using Consensus

This paper addresses the problem of multiple unmanned aerial vehicle (UAV) rendezvous when the UAVs have to perform maneuvers to avoid collisions with other UAVs. The proposed solution consists of using velocity control and a wandering maneuver, if needed, of the UAVs based on a consensus among them on the estimated time of arrival at the point of the rendezvous. This algorithm, with a slight modification is shown to be useful in tracking stationary or slowly moving targets with a standoff distance. The proposed algorithm is simple and computationally efficient. The simulation results demonstrate the efficacy of the proposed approach. DOI: 10.1061/(ASCE)AS.1943-5525.0000145. (C) 2012 American Society of Civil Engineers.

Mycobacterium tuberculosis mutT1 (Rv2985) and ADPRase (Rv1700) proteins constitute a two-stage mechanism of 8-Oxo-dGTP and 8-Oxo-GTP detoxification and adenosine to cytidine mutation avoidance

Approximately one third of the world population is infected with Mycobacterium tuberculosis, the causative agent of tuberculosis. A better understanding of the pathogen biology is crucial to develop new tools/strategies to tackle its spread and treatment. In the host macrophages, the pathogen is exposed to reactive oxygen species, known to damage dGTP and GTP to 8-oxo-dGTP and 8-oxo-GTP, respectively. Incorporation of the damaged nucleotides in nucleic acids is detrimental to organisms. MutT proteins, belonging to a class of Nudix hydrolases, hydrolyze 8-oxo-G nucleoside triphosphates/diphosphates to the corresponding nucleoside monophosphates and sanitize the nucleotide pool. Mycobacteria possess several MutT proteins. However, a functional homolog of Escherichia coli MutT has not been identified. Here, we characterized MtuMutT1 and Rv1700 proteins of M. tuberculosis. Unlike other MutT proteins, MtuMutT1 converts 8-oxo-dGTP to 8-oxo-dGDP, and 8-oxo-GTP to 8-oxo-GDP. Rv1700 then converts them to the corresponding nucleoside monophosphates. This observation suggests the presence of a two-stage mechanism of 8-oxo-dGTP/8-oxo-GTP detoxification in mycobacteria. MtuMutT1 converts 8-oxo-dGTP to 8-oxo-dGDP with a K-m of similar to 50 mu M and V-max of similar to 0.9 pmol/min per ng of protein, and Rv1700 converts 8-oxo-dGDP to 8-oxo-dGMP with a K-m of similar to 9.5 mu M and V-max of similar to 0.04 pmol/min per ng of protein. Together, MtuMutT1 and Rv1700 offer maximal rescue to E. coli for its MutT deficiency by decreasing A to C mutations (a hallmark of MutT deficiency). We suggest that the concerted action of MtuMutT1 and Rv1700 plays a crucial role in survival of bacteria against oxidative stress.

A Note on Risk Acceptance, Bankruptcy Avoidance and Riskiness Measures

In this work we clarify the relationships between riskiness, risk acceptance and bankruptcy avoidance. We distinguish between the restriction on the current wealth required to make a gamble acceptable to the decision maker and the restriction on the current wealth required to guarantee no bankruptcy if a gamble is accepted. We focus on the measure of riskiness proposed by Foster and Hart.