Guillen D.,CSIC - Institute of Environmental Assessment And Water Research |
Ginebreda A.,CSIC - Institute of Environmental Assessment And Water Research |
Farre M.,CSIC - Institute of Environmental Assessment And Water Research |
Darbra R.M.,Polytechnic University of Catalonia |
And 3 more authors.
Science of the Total Environment | Year: 2012
The extensive and intensive use of chemicals in our developed, highly technological society includes more than 100,000 chemical substances. Significant scientific evidence has lead to the recognition that their improper use and release may result in undesirable and harmful side-effects on both the human and ecosystem health. To cope with them, appropriate risk assessment processes and related prioritization schemes have been developed in order to provide the necessary scientific support for regulatory procedures. In the present paper, two of the elements that constitute the core of risk assessment, namely occurrence and hazard effects, have been discussed. Recent advances in analytical chemistry (sample pre-treatment and instrumental equipment, etc.) have allowed for more comprehensive monitoring of environmental pollution reaching limits of detection up to sub ngL-1. Alternative to analytical measurements, occurrence models can provide risk managers with a very interesting approach for estimating environmental concentrations from real or hypothetical scenarios. The most representative prioritization schemes used for issuing lists of concerning chemicals have also been examined and put in the context of existing environmental policies for protection strategies and regulations. Finally, new challenges in the field of risk-assessment have been outlined, including those posed by new materials (i.e., nanomaterials), transformation products, multi-chemical exposure, or extension of the risk assessment process to the whole ecosystem. © 2012 Elsevier B.V.
Until now, 3D printing has been limited to various types of solids; however, a new study has shown how to print highly complex hydraulic systems from both solids and liquids that makes it easier to build labs on a chip for medical and pharmaceutical uses, and liquid channels for chemical testing and analysis. In what could be a significant move towards the rapid fabrication of functional machines, such robots also have potential applications in areas such as facilitating disaster relief in dangerous situations. Scientists from the Computer Science and Artificial Intelligence Laboratory at MIT automatically produced 3D printed dynamic robot bodies and parts that needed no previous assembly from a commercially available multi-material 3D inkjet printer based on only a single-step process. Using a 3D printer to produce robots is a viable alternative to doing so by hand, which requires huge effort, or through automation, which has not yet reached the necessary level of sophistication. This “printable hydraulics” approach, which provides a design template that can be tailored for robots of different sizes, shapes and functions, was used to produce a small six-legged robot with a dozen hydraulic pumps embedded within it, only requiring the minimal addition of the electronics and a battery before being operational. As team leader Daniela Rus points out, “3D printing offers a way forward, allowing us to automatically produce complex, functional, hydraulically powered robots that can be put to immediate use”. Such printable robots could also be quickly and cheaply produced, and have less electronic components than standard robots. A paper on their research was recently accepted for the 2016 IEEE International Conference on Robotics and Automation (ICRA). In the technique, the printer deposited individual droplets of material of only 20–30 microns in diameter, by layer from the bottom up, with different materials being deposited in different parts for each layer. A high-intensity UV light then solidified the materials but not the liquids. The printer can use many types of material, although each layer is made up of a photopolymer that is solid and a non-curing material that is liquid. They showcased the technique by 3D printing linear bellows actuators, gear pumps, soft grippers as well as the hexapod robot. The hexapod weighed about 1.5 pounds and was under six inches long, and moved using a single DC motor turning a crankshaft that pumps fluid to the robot’s legs. However, it took 22 hours to print, not long considering its complexity, but the team hopes this can be achieved faster by improving on the engineering and resolution of the printers.
News Article | February 11, 2016
A team of researchers led by Prof. Davide Scaramuzza have developed a way to train drones to follow forest trails in an effort to assist search and rescue missions for lost hikers. According to the research, Prof. Scaramuzza's team figured out a method of machine learning through Deep Neural Networks (DNNs) which enables an unsupervised drone to determine the direction of a path using an on-board camera. The system was created by first setting up a hiker with three cameras that cover about 180 degrees of visual information: one positioned straight ahead, one placed 30 degrees to the left and the other 30 degrees to the right so that there is a slight overlap in the captured video. The hiker was instructed to always look ahead in the direction of the path since the front camera will provide the information for the trail. The raw data (PDF) used was eight hours' worth of footage of approximately 7 kilometers of hiking trail between an altitude of 300 and 1,200 meters. The footages were taken at different times of the day and under different weather conditions. The results were surprising when it was tested because the autonomous quadcopter was able to navigate a completely new trail and stay on course as well as, and sometimes even better, than humans. The same path and test was done with two humans against the drone to determine how effective the DNNs based machine learning was and, on one test, the quadcopter was successful 85.2 percent of the time as opposed to the two people who were accurate 86.5 and 82 percent of the time. A second test with different conditions resulted in the quadcopter being accurate 95 percent of the time when the two people were 91 and 88 percent accurate. Watch the video explanation of the research below. "Now that our drones have learned to recognize and follow forest trails, we must teach them to recognize humans," Prof. Scaramuzza said. A drone that could recognize proper trails and humans will certainly be of great assistance to rescue operations, moreso if it can also detect vital signs like the Lynx 6-A. The research titled "A Machine Learning Approach to Visual Perception of Forest Trails for Mobile Robots" appeared in the IEEE Robotics and Automation Letters (RA-L) and will be presented during the IEEE International Conference on Robotics and Automation (ICRA'16) in May.
In a pair of projects announced this week, researchers from MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL) demonstrated software that allow drones to stop on a dime to make hairpin movements over, under, and around some 26 distinct obstacles in a simulated "forest." One team's video shows a small quadrotor doing donuts and figure-eights through an obstacle course of strings and PVC pipes. Weighing just over an ounce and clocking in at 3 and a half inches from rotor to rotor, the drone can fly through the 10-square-foot space at speeds upwards of 1 meter per second. The team's algorithms, which are available online and were previously used to plan footsteps for CSAIL's Atlas robot at last year's DARPA Robotics Challenge, segment space into "obstacle-free regions" and then link them together to find a single collision-free route. "Rather than plan paths based on the number of obstacles in the environment, it's much more manageable to look at the inverse: the segments of space that are 'free' for the drone to travel through," says recent graduate Benoit Landry '14 MNG '15, who was first author on a related paper just accepted to the IEEE International Conference on Robotics and Automation (ICRA). "Using free-space segments is a more 'glass-half-full' approach that works far better for drones in small, cluttered spaces." In a second CSAIL project, PhD student Anirudha Majumdar showed off a fixed-wing plane that is guaranteed to avoid obstacles without any advanced knowledge of the space, and even in the face of wind gusts and other dynamics. His approach was to pre-program a library of dozens of distinct "funnels" that represent the worst-case behavior of the system, calculated via a rigorous verification algorithm. "As the drone flies, it continuously searches through the library to stitch together a series of paths that are computationally guaranteed to avoid obstacles," says Majumdar, who was lead author on a related technical report. "Many of the individual funnels will not be collision-free, but with a large-enough library you can be certain that your route will be clear." Both papers were co-authored by MIT professor Russ Tedrake; the ICRA paper, which will be presented in May in Sweden, was also co-written by PhD students Robin Deits and Peter R. Florence. A bird might make it seem simple, but flight is a highly complicated endeavor. A flying object can change position in six distinct directions—forward/backward ("surge"), up/down ("heave"), left/right ("sway"), and by rotating front-to-back ("pitch"), side-to-side ("roll"), and horizontally ("yaw"). "At every moment in time there are 12 distinct numbers needed to describe where the system it is and how quickly it is moving, on top of simultaneously tracking other objects in the space that could get in your way," says Majumdar. "Most techniques typically can't handle this sort of complexity in real-time." One common motion-planning approach is to sample the whole space through algorithms like the "rapidly-exploring random tree." Although often effective, sampling-based approaches are generally less efficient and have trouble navigating small gaps between obstacles. Landry's team opted to use Deits' new free-space-based technique, which he calls the "Iterative Regional Inflation by semidefinite programming" algorithm (IRIS). They then coupled IRIS with a "mixed-integer semidefinite program" (MISDP) that assigns specific flight movements to each "space-free region" and then executes the full plan. To sense its surroundings, the drone used motion-capture optical sensors and an on-board inertial measurement unit (IMU) that help estimate the precise positioning of obstacles. "I'm most impressed by the team's ingenious technique of combining on- and off-board sensors to determine the drone's location," says Jingjin Yu, an assistant professor of computer science at Rutgers University. "This is key to the system's ability to create unique routes for each set of obstacles." In its current form, MISDP has been optimized such that it can't do real-time planning; it takes an average of 10 minutes to create a route for the obstacle course. But Landry says that making certain sacrifices would let them generate plans much more quickly. "For example, you could define 'free-space regions' more broadly as links between areas where two or more free-space regions overlap," says Landry. "That would let you solve for a general motion-plan through those links, and then fill in the details with specific paths inside of the chosen regions. Currently we solve both problems at the same time to lower energy consumption, but if we wanted to run plans faster that would be a good option." Majumdar's software, meanwhile, generates more conservative plans, but can do so in real-time. He first developed a library of 40 to 50 trajectories that are each given an outer bound that the drone is guaranteed to remain within. These bounds can be visualized as "funnels" that the planning algorithm chooses between to stitch together a sequence of steps that allow the drone to plan its flying on the fly. A flexible approach like this comes with a high level of guarantees that the software will work, even in the face of uncertainties with both the surroundings and the hardware itself. The algorithm can easily be extended to drones of different sizes and payloads, as well as ground vehicles and walking robots. As for the environment, imagine the drone choosing between making a forceful roll maneuver that will avoid a tree by a large margin, versus flying straight and avoiding a tree by a small amount. "A traditional approach might prefer the first since avoiding obstacles by a significant amount seems 'safer,'" Majumdar says. "But a move like that actually may be riskier because it's more susceptible to wind gusts. Our method makes these decisions in real-time, which is critical if we want drones to move out of the labs and operate in real-world scenarios." CSAIL researchers have been working on this problem for many years. Professor Nick Roy has been honing algorithms for drones to develop maps and avoid objects in real-time; in November a team led by PhD student Andrew Barry published a video demonstrating algorithms that allow a drone to dart between trees at speeds of 30 miles per hour. While these two drones cannot travel quite as fast as Barry's, their maneuvers are generally more complex, meaning that they can navigate in smaller, denser environments. "Enabling dynamic flight of small, off-the-shelf quadcopters is a marvelous achievement, and one that has many potential applications," Yu says. "With additional development, I can imagine these machines being used as probes in hard-to-reach places, from exploring caves to doing search-and-rescue in collapsed buildings." Landry, who now works for 3D Robotics in California, is hopeful that other academics will build on and refine the researchers' work, which is all open-source and available on github. "A big challenge for industry is determining which technologies are actually mature enough to use in real products," Landry says. "The best way to do that is to conduct experiments that focus on all of the corner cases and can demonstrate that algorithms like these will actually work 99.999 percent of the time." More information: Aggressive Quadrotor Flight through Cluttered Environments Using Mixed Integer Programming. groups.csail.mit.edu/robotics-center/public_papers/Landry15b.pdf
ICRA expects wind energy capacity addition during the current fiscal year to grow 20% over the last year to about 2800 MW and will be driven both by the IPP and non-IPP segments. In the rating agency’s view, the demand drivers for the wind energy sector remain favourable in the long run. This is mainly aided by strong policy support in place at the Centre and in key states which have wind potential, favourable regulatory framework in the form of renewable purchase obligation (RPO) regulations, as well as the cost competitiveness of wind-based energy vis-à-vis conventional energy sources. The National Institute of Wind Energy (NIWE), Chennai, India has launched two online maps, one each for wind and solar radiation. The Wind Energy Resources Map of India has been launched at 100 meter above the ground, while the solar radiation map has been set up at ground level on the online Geographic Information System platform.