by Heidi Norman
Using an approach first trialled on automated London Underground trains in the 1960s, each autonomous machine is in constant communication with a central control computer. No robot can move until it has sought, and received, permission. Like an automated aircraft control tower, the central computer system is aware of the position of all autonomous and nonautonomous vehicles in the mine, and directs the robots to use the most efficient and safe path.
Autonomous trucks are not the only machines chatting with the central controller in the Pilbara. They have recently been joined by some new friends – the autonomous drill rigs. These slow-moving platforms carry out the first step in the iron ore operation – drilling holes into the ore body, up to 16 metres deep. Each hole is then filled with explosives and detonated to break up the rock, dirt and ore. In the Pilbara, a million blast holes are drilled each year. And it’s not just a matter of brute force.
‘The drill rigs are real scalpels, it’s high precision stuff, down to sub-centimetre positioning of the machine,’ says McGagh. Using their high precision GPS, laser and optical cameras, the drill rigs carefully shuffle into position, then use tilt sensors connected to jacks, to set themselves perfectly level before they begin to drill. As it drills, the robot continually analyses the rock it is cutting through, sending real-time data about the ore body back to human mine operators, who then know much more about the next batch of ore before it has even been blasted. ‘We get better quality data from the autonomous machines, and that’s worth a lot of money to us,’ says McGagh. The promise of added value benefits such as this convinced the mining industry to invest in autonomous systems, Durrant-Whyte recalls. ‘I remember taking people from Rio Tinto to the automated container terminal in Brisbane, watching the lights go on when I explained to them that what they were seeing was an automated terminal, not a bunch of robots. Immediately they got the vision that what they wanted was an automated mine, not an automated truck. That really kicked off the work we did with Rio.’
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It’s hard to form an emotional connection with an automated mining truck. They are brutally strong, tirelessly efficient, but not terribly charismatic.
Perhaps that’s why humanoid robots still garner so much attention, despite not being especially useful. We can’t help but try to nurture them along, as if they were clumsy toddlers, and it’s diverted a lot of time and resources away from other areas of robotics research. ‘I used to tell my students, I reckon anthropomorphic approaches to robotics had put back the field by a decade. And I’d now modify that to say two decades,’ says Durrant-Whyte.
So what the average field robot may lack in charm, it more than makes up for in other ways – something I come to appreciate when I visit Sydney’s centre for field robotics, and see many of them in the metal.
The centre is one of the world’s biggest, Stefan Williams tells me as he shows me around. Like several of his colleagues, Williams is a ‘lifer’ at the centre. The personable Canadian arrived in 1998 after coming to the country to begin a PhD under Durrant-Whyte’s supervision. He finished in 2001 but has remained ever since, and is now a professor.
At the centre, I quickly come to appreciate there are jobs robots can now handle with aplomb, and others where automation just doesn’t do a good enough job yet. It’s coffee time, and as we walk through the workshop, everyone is huddled around the big old manually operated espresso machine, grinding the beans, frothing milk – and ignoring the shiny automatic espresso machine in the corner. Barista jobs will be safe for a good while yet, it seems.
The centre works on three types of field robot: land robots; robots that fly; and underwater robots, the subject of Williams’ research. In the workshop, all three forms were on show – most with their cases opened up and their electronic entrails spilled out across the workshop bench. But they evoke little pathos; there’s a dispassionate, geeky intrigue to seeing the insides of these extremely advanced gadgets.
An intact field robot stands in a corner. Painted bright red, and with a curved outer shell, he’s called Ladybird. He looks perky, with one side of his shell raised as if in salute.
Like his insect namesake, he’s a good thing to have in your veggie patch.
Agriculture is another Australian industry that is ripe for robots. It is notoriously hard to source farm labour, especially around harvest time – and particularly as labourers can earn far more in mining. The National Farmers’ Federation estimates the farm labour shortage at up to 100 000 workers, and has lobbied hard for more seasonal workers to be allowed in from overseas. In response the federal government trialled, and in 2012 made permanent, a seasonal worker program that grants temporary entry to workers from the Pacific to help gather the country’s harvest. Agriculture is not as lucrative as mining, so research funding has been tighter, nevertheless the first efforts of agricultural robots are starting to bear fruit. Broadacre crops such as wheat or rice are relatively simple for robots to tend and harvest; it’s a case of adding autonomous systems to existing tractors and harvesters.
The much bigger challenge – and where Ladybird comes in – is with fruit and vegetable crops. ‘Harvesting is where most of the labour costs are,’ says Salah Sukkarieh, who leads the team developing Ladybird.
Like Williams, Sukkarieh also has been at the centre since joining to start a PhD with Durrant-Whyte in the late 1990s – his research topic was to help develop the AutoStrad’s navigation system. ‘My big interest is optimisation and efficiency,’ he tells me – and in finding real-world situations where his work can be quickly applied. The expertise he has developed in intelligent software systems for autonomous decision-making turns out to have all sorts of applications outside robotics. He’s working with Qantas on an automated system for advanced flight planning to maximise fleet efficiency.
But that’s easy compared to his work with Ladybird. Harvesting fresh produce is enormously difficult for a robot. The Ladybird has to contend with changing light, wind, rain and dust. But these are the least of a farming robot’s worries. While every shipping container is the same, every plant is unique. The fruit is always in a different spot, often hidden by leaves, and can vary greatly in size, shape and colour. The robot then has to decide if it’s ripe for picking, and then pluck it without squashing the produce. Even the geekiest shopper would soon tire of tomatoes dented by robot fingers.
Sukkarieh is tackling one challenge at a time: first master perception; then try simple tasks such as pruning and weeding; and finally work on harvesting.
When it comes to perception, Sukkarieh’s first machines didn’t even get their wheels dirty. For the past ten years, he has been pioneering unmanned aerial vehicles – UAVs, or drones as they are now known.
‘Drones have suddenly become of much wider interest, but we had a major UAV program started in the ’90s,’ says Durrant-Whyte. ‘We did programs that were significantly beyond what you often see now.’ The team was the first to fly multiple autonomous UAVs in formation. Each plane was equipped with a different type of sensor, allowing them to collectively build a picture of the environment below.
The technique is now being used to map weeds and invasive animals in the northern tablelands, says Sukkarieh. As a fringe benefit, the hovering drones exploit their rotors downdraft to spray weeds with herbicide.
But more recently he’s been working on farm robots that keep their wheels on the ground. ‘Over the last two years the focus has been on machine perception – what type of sensors do I use, what are the algorithms that I have to put together?’ That’s useful technology in its own right, for example for autonomously monitoring crop yield, or assessing crop health, or keeping an eye out for weeds or insect pests.
‘We’re in a period where that technology can be spun off and used,’ says Sukkarieh. Applications for these kinds of technologies aren’t limited to agriculture. For the past six years, Marathon Targets has been manufacturing the world’s first ‘smart targets’ for live fire training by specialist military marksmen. With humanoid to
rsos and heads, but wheels instead of legs, these machines pilot themselves around training grounds while snipers take careful aim. And they react to their environment – if a robot gets hit, neighbouring robots will scatter.
Back on the farm, the next step for Sukkarieh is to combine perception with simple plant manipulation, such as spraying or pruning. Once that’s mastered, it will be time to tackle the ultimate challenge: harvesting.
Ladybird will take the kind of sensor technology Sukkarieh has developed for tree crop robots, and apply it to vegetables. But that’s just the start. The team has plans to play with other sensors, such as instruments to sniff crops to help determine whether a veggie is ripe. And the team is also working to combine data from all of Ladybird’s sensors to try to identify the tell-tale signals that will show whether a plant is sick or merely thirsty. Brute force computer power could no doubt crack the problem, but Ladybird is a mobile platform running on batteries and can’t simply guzzle electricity to solve a task – the challenge is to be as computationally efficient as possible.
Tucked under its shiny red shell, Ladybird also has a robotic arm.
‘It will be to spray or even mechanically weed,’ Sukkarieh says. Perhaps one day a small fleet of Ladybirds will be guided to weed or disease hotspots by a UAV or two keeping an airborne eye on the whole farm. And ultimately, these UAVs will send in the Ladybirds when there is produce to be harvested. Farmers are clearly excited by the prospect. In June the Australian Vegetable Industry’s peak body, Ausveg, named Sukkarieh researcher of the year for his work on Ladybird.
‘I think Australia will maintain its leadership in this field,’ Sukkarieh says.
‘We have diverse industries interested in field robotics, and investing in R&D for field robotics, and it will stay like that I think.’
Rio Tinto is still investing. In 2007 it funded the formation of the Rio Tinto Centre for Mine Automation at the ACFR at Sydney. The collaboration is still going strong, and there are many projects in the works. As in agriculture, robot technology is not ready to take on every task in the mine.
The loaders that scoop up the blasted ore and dump it into the autonomous trucks are still human-operated. However carefully the ore is blasted, the result is a complex environment that demands a skilled human.
‘The loader operator is using that incredible computing power called the human brain to visually look at the blasted rock pile and interact with it,’ says McGagh. It will be a long time before we have the computing power and sensor technology to replace the human operator for this job, he says.
Other targets are closer to hand. Next year the company plans to introduce autonomous trains – that will weigh 32 000 tonnes, fully loaded – to bring the ore from the mines to the ports where it is loaded on to ships and exported. The company has a roadmap projecting the next several generations of its autonomous systems. But the system will never entirely run the show, says McGagh – they will always be tools under human direction. The maintenance staff, geologists and blast planners will always be on site. ‘There will never be a mine without people. Never,’ he says.
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Durrant-Whyte has led the creation of many field robots over the years, but he is proudest of the dockside AutoStrads he developed when he first came to Australia.
‘The reason for that is, it works – we walked away,’ he says. ‘What amazes me now is, everyone in the terminal, the truckies who drive up, they don’t pay any attention at all, they just expect these massive robots to do these things. And that, I think, is the tick of success.
‘For mining, the same thing will happen in the next few years. When people like me are no longer involved, you will know it’s a success.’
I, wormbot: the next step in artificial intelligence
Uncharted waters
An uneasy alliance
Honest placebos
Jane McCredie
There is one treatment for conditions ranging from depression to back pain to irritable bowel syndrome that produces impressive results with minimal side effects. Yet clinicians are often reluctant to prescribe it.
Why? Because this particular silver bullet is the placebo effect.
Although the mechanisms of placebo remain largely mysterious, the belief that we are receiving a helpful treatment somehow seems to have the ability to make us feel better.
And there’s the rub. If we need to believe in it for placebo treatment to work, then surely the clinicians dispensing it would have to practise a certain level of deception.
But maybe it’s not that clear cut.
Canadian psychologist Dr Susan Huculak argues we need more open discussion about the role of placebo in medicine as well as clear guidelines for clinicians on how and when to use it.
Competing viewpoints on placebo – from scorn to veneration – have impeded progress, Huculak writes in an opinion article that uses psychiatry as a case study.
Clinical trial researchers tend to see placebo as no more than a contaminant, and they strive to design trials to minimise its effects, she writes, quoting one research team writing that the placebo ‘problem’ is ‘probably the most common reason for negative trials’.
So it’s not the ‘drugs not working as they’re supposed to’ problem that’s at fault here then. Phew.
In any case, Dr Huculak asks, should researchers be seeking to reduce the placebo effect in the first place?
‘If the effects following placebo are as real and potent as the drug effects (i.e. are legitimate), it would make little sense to try and reduce them’, she writes.
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This may be particularly relevant in psychiatry where a number of studies have shown antidepressants are no more effective than placebo in mild to moderate depression, despite showing efficacy in severe depression.
Given the known side effects of antidepressants, should we be exploring placebo as a treatment in less-severe forms of the disease?
Clinicians, Dr Huculak writes, are ‘generally uncomfortable or even baffled’ when it comes to the placebo effect.
Despite that discomfort, several studies have suggested many doctors do use placebos in clinical practice – whether it’s a recommendation of vitamins, an unwarranted prescription for antibiotics, or a subtherapeutic dose of antidepressants.
One of the reasons for the enduring popularity of alternative health therapies may be that the practitioners are particularly skilled at stimulating a placebo response.
Deliberate use of a placebo without the patient’s knowledge raises clear ethical issues, but is deception always necessary for the placebo effect to work?
It seems extraordinary, but maybe not.
Researchers from the Harvard Medical School conducted a controlled trial in irritable bowel syndrome, with placebo as the active treatment. The control in this case was no treatment at all, though participants in both arms received the same level of interaction with providers.
Participants in the active treatment (i.e. placebo) arm of the trial were told they were being given ‘placebo pills made of an inert substance, like sugar pills, that have been shown in clinical studies to produce significant improvement in irritable bowel syndrome symptoms through mind–body self-healing processes’.
Those given the sugar pills experienced significantly greater improvements in their condition than those who did not get the placebo.
So there’s the question: could openly giving a placebo, accompanied by the information that placebos have been shown to work, offer an ethical way to harness this powerful tool?
Will a statin a day really keep the doctor away?
Why aren’t we dead yet?
Revisiting Milgram’s shocking obedience experiments
Imagine there’s new metrics(it’s easy if you try)
Jenny Martin
Academia has become obsessed with metrics. Institutions jostle for the ‘top’ positions in international rankings, departments are evaluated nationally to identify the ‘best’, and in
dividuals are lined up against one another to find the ‘leaders’.
Let’s take international rankings as an example. The rationale behind rankings such as the Times Higher Education World Rankings, the QS World University Rankings and the Shanghai Jiao Tong University Academic Ranking of World Universities is to allow people to make informed decisions about where to study, teach and conduct research. It follows then that a higher rank will mean more students, especially international students, which in turn means more money coming into the university.
Indicators used to calculate these rankings include things like academic reputation, research income, number of publications, number of citations to these papers, industry income, and the ratios of faculty:student, international:local student, international:local staff, and doctoral:bachelors student. Data is gathered from detailed surveys sent to academics, employers, and universities, as well as from companies that specialise in providing research citation data (Thomson Reuters, Scopus etc.).
There are two things that are troubling about these indicators. The first is that there are no upper limits where there probably should be. Considerable effort is expended by institutions to increase each indicator with the aim of getting a top spot. If we extrapolate, without applying upper bounds, what could be the consequences of behaviours driven by these metrics?
Hmm, let’s see. If the number of PhD students is a key indicator and there is no upper bound, this might lead to oversupply. In turn, this might result in PhD student disenchantment with academia during the course of their studies. If large numbers of PhD graduates are being produced, it’s likely that a considerable proportion of early career researchers will be unable to find positions in academia. The pressures on PhD students to focus on producing research papers would lead to a lack of time and opportunity to ‘explore (other) aspects relevant to their future career options’. This situation might produce a generation of highly intelligent, highly qualified PhD students/early career researchers who feel like failures.
