This one is a bit short. It's Gu Long's fault, not mine. :)
FOUR
The stone cell was ghastly and chilly, but Gongsun Jing started to sweat. Drops of soybean-sized cold sweats streamed down his pale face one after another.
Young Master Zhu looked at him, his gaze as tender as when he looked at his own hands.
“You must know it!” he said in a gentle voice.
“Know…know what?”
“Know who is thanking you.”
Clenching his fists, Gongsun Jing suddenly turned around and dashed out.
“Well, he really is a nice guy. What a pity that nice guys are always said to not live very long…,” Young Master Zhu murmured with another sigh.
“Suppose it is true that there are only seven people who are capable of getting through these thirteen traps. Who would they be?”
“One is for sure. No matter how you count them, he’d count as one of the seven.”
Imagine that you have won a shopping spree sweepstake at the local Walmart. Assuming that you know the layout of the store pretty well and have common sense on how much general merchandises are worth. Now for the next 2 minutes anything you grab are yours to keep for free, what would you do? Would you just start grabbing everything that are close to you such as orange juice, eggs, sausages, and breakfast cereals, or would you dash straight to that 60-inch LCD TV (or that 5 Carat diamond ring if you are a woman) at the furthest corner of the store? What is the best path you should take to maximize the total monetary value of the shopping spree?
Now just to make it a little bit more complicated, what if you only have 1 minute to grab things? Maybe you are asked to start from the cashier's lane and must return to the cashier's lane before your time runs out? What if your shopping cart was tinkered with and it doesn't roll backward? What if getting that 5 Carat diamond ring required a Walmart employee to unlock three things before you can get to it? What if you forgot to bring your glasses and everything looked totally blurry in front of your eyes? Looks like the wonderful dream of winning the sweepstake has just turned into a nightmare! "Why are you making it so hard for me?" you moan. And I shrug and tell you that those are all the challenges I face when I plan a coverage path for a UAV in support of Wilderness Search and Rescue operations.
The benefit of adding a UAV to the Wilderness Search and Rescue team is that now you have an eye in the sky and you can cover large areas quickly and also reach areas that are difficult for human on foot to reach sooner. When planning a coverage path for the UAV with a gimbaled camera, what we really care about is the path of the camera footprint. In our path-planning approach, we use a 4-connect grid to represent the probability distribution of where it is likely to find the missing person. Even though a fix-wing UAV might need to roll and follow a curvy path when it turns, the gimbaled camera can automatically adjust itself to always point straight down and the path of the camera footprint can include sharp 90 degrees turns. As the camera footprint covers an area, it "vacuums up" the probability within that area, and obviously the more probability we can "vacuums up" along the path, the more likely the UAV can spot the missing person.
When we evaluate how good a UAV path is, we focus on two factors: flight time and the amount of probability accumulated along the path. If a desired flight time is set (maybe due to the fact that the battery on the UAV only lasts for one hour), then the more probability we can accumulate, the better the path. If a desired amount of probability is expected (e.g., 80% probability of spotting the person from the UAV), then the sooner we can reach that goal, the better the path. My research focuses on the first type of case only where we plan a path for a given flight duration.
So how good or efficient is the path generated by our algorithm? A natural way to evaluate this is to compare the amount of probability the UAV accumulates along our path against what the UAV can accumulate along the best possible path and compute a percentage. A path with 50% efficiency means the path is half as good as the best we can do. The irony here is that we don't know what the best possible (optimal) path is, and searching for this optimal path might take a long time (years) especially when the probability distribution is complex (like how it is in real search and rescue scenarios), and it defeats the purpose of finding the missing person quickly. Many factors can also affect how the optimal path might turn out and change the total amount of probability accumulated if the UAV follows that path. Here I'll list the ones we must deal with.
1. Desired flight time
If the search region is very small, the UAV has 100% probability of detection (say we are searching in the Bonneville Salt Flats), and you have plenty of UAV flight time to completely comb the area many times, then life gets easier and you can be pretty sure that you will spot the missing person if the UAV follows a lawnmower or Zamboni flight pattern (assuming the missing person stays in a fixed location). If the search region is very large and you have a very short UAV flight time, then maybe there are areas you simple can never reach given the short flight duration. Remember the 2-minute shopping spree vs. a 1-minute one?
2. Starting position and possibly the ending position
If the UAV starts from the middle of the search region, the optimal path will most definitely not be the same as the one when the UAV starts from the edge of the search region. And if the UAV must return to a desired ending position (maybe for easy retrieval or returning to command base), time must be allocated for the UAV for the return flight. Ideally, while flying back, the UAV should still try to cover areas with high probabilities. In the example of the shopping spree case, if you are required to start from the cashier's lane and also return there before time runs out, you probably still want to grab things on your way back, maybe choosing a different route back.
3. Type of UAV (fix-wing vs. copter)
A fix-wing UAV must keep moving in the air in order to get enough lift to maintain airborne. It also cannot fly backward. But a copter type UAV doesn't have these restrictions. It can hover over a spot or fly backward anytime it wants. Therefore, the type of UAV we use can really change how the optimal path looks like. Remember the shopping cart that was tinkered with in your shopping experience?
4. Task difficulty (probability of detection)
Although the UAV provides the bird's eye view of the search area, some times we look but we don't see. Maybe because the dense vegetation makes spotting the missing person a very difficult task; maybe the weather is really bad with lowers the visibility; maybe the missing person is wearing a green jacket that blends in with the surroundings. This means the probability of detection might vary from case to case and search area to search area. When the probability of detection is low, maybe we should send the UAV to cover the area multiple times so we can search better. This factor really adds complexity to the evaluation of a UAV's path's efficiency. When it takes 30 seconds for the Walmart employee to unlock everything and get that 5 Carat diamond ring for you, is it worth the wait? Or maybe grabbing all those unlocked watches at $50 a piece in the neighboring section sounds like a better idea now?
Given all these complicated factors, I still need to find out how well my path-planning algorithm is performing in different search scenarios. In the following blog posts in this series, I'll go through each factor and discuss how we can reasonably evaluate the efficiency of a search path without knowing the optimal solution.
Video of the Day:
A video my friend Bev made when I showed her around the BYU campus!
Top Conferences
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RSS -- Robotics: Science and Systems Conference
RSS is a single-track conference held annually that brings together researchers working on algorithmic or mathematical foundations of robotics, robotics applications, and analysis of robotics systems. The very low average acceptance rate of 25% makes the conference a very selective one. Accepted papers cover a wide range of topics such as kinematics/dynamic control, planning/algorithms, manipulation, human-robot interaction, robot perception, estimation and learning for robotic systems, and etc. One thing great about this conference is that all proceedings are available online for free
RSS is also a relatively new conference. The first ever RSS was held in 2005. However, the conference is growing quickly, attracting lead researchers in the robotics community with an expected attendance of over 400 for the next RSS conference. The conference also includes several workshops and tutorials. I have not submitted anything to the RSS conference in the past. It would be really nice if I could get a paper published here.
The next RSS conference RSS 2012 will be held at Sydney, Australia.
Conference Dates: June 27-July 1, 2012 (Roughly)
Submission Deadline: January 17, 2012 (Roughly)
SMC -- IEEE International Conference on Systems, Man, and Cybernetics
The SMC conference is a multi-track conference held annually. It provides an international forum for researchers and practitioners to report the latest innovations,
summarize the state-of-the-art, and exchange ideas and advances in all aspects of systems engineering, human-machine systems, and emerging cybernetics. Wikipedia defines the word Cybernetics as "the interdisciplinary study of the structure of regulatory systems." Cybernetics is closely related to information theory, control theory and systems theory.
The SMC conference is sponsored by the Systems, Man, and Cybernetics Society, whose mission is: "... to serve the interests of its members and the community at large by promoting the theory, practice, and interdisciplinary aspects of systems science and engineering, human-machine systems, and cybernetics. It is accomplished through conferences, publications, and other activities that contribute to the professional needs of its members."
My interest in the conference lies in the human-machine systems track, especially under the topics of adjustable autonomy, human centered design, and human-robot interaction. This would be a good place to publish research related to UAV (Unmanned Aerial Vehicle) and search and rescue robotics.
I have never submitted anything to this conference before and I can't find any information on the acceptance rate for the conference. But one thing for sure, this is not one of those "come and greet" conferences and all papers submitted go through a serious peer-review process.
The next SMC conference SMC 2011 will be held at Anchorage, Alaska, USA.
Conference Dates: October 9-12, 2011
Submission Deadline: April 1, 2011
The next SMC conference you can submit paper to is SMC 2012, which will be held in Seoul, Korea.
Conference Dates: October 7-10, 2012
Submission Deadline: April 1, 2012 (Roughly)
A 3D probability distribution surface can represent the likelihood of certain events in a specific region where a higher point on the surface could mean it is more likely for the event to happen. For example, a 3D probability distribution surface created for a Wilderness Search and Rescue (WiSAR) operation, whether systematically or manually or with a hybrid, can show the searchers areas where it is more likely to find the missing person. The distribution map can be used to better allocate search resources and to generate flight paths for an Unmanned Aerial Vehicle (UAV).
An example 3D probability distribution surface
Because different path-planning algorithms may be better suited for different probability distributions (I appeal to the No-Free-Lunch theorem), identifying the type of distribution beforehand can help us decide what algorithm to use for the path-planning task. In our decision process, we particularly care about how many modes the probability distribution has. So how can we automatically identify all the modes in a 3D probability distribution surface? Here I'll describe the algorithm we used.
In our case, the 3D probability distribution surface is represented by a matrix/table where each value represents the height of the point. You can think of this distribution as a gray-scale image where the gray value of each pixel represent the height of the point. And we use a Local Hill Climbing type algorithm with 8-connected neighbors.
1. Down sample the distribution
If the distribution map is very large, it might be a good idea to down sample the distribution to improve algorithm speed. We assume the surface is noise-free. If the surface is noisy, we can also smooth it with a Gaussian filter (think image processing).
2. Check for a uniform distribution (a flat surface)
It is a good idea to check if the probability distribution is a uniform distribution. Just check to see if all values in the matrix are identical. If a uniform distribution is identified, we know the distribution has 0 mode and we are done.
3. Local Hill Climbing with Memory
Start from the a point of the surface and then check its neighbors (8-connected). As soon as a neighbor with the same or better value is found, we "climb" to that point. The process is repeated until we reach a point (hilltop) where all neighbors have smaller values. As we "climb" and check neighbors, we mark all the points we visited along the way. And when we check neighbors, we only check points we have not visited before. This way we avoid finding a mode we had found before. Once we find a "mode", we can start from another unvisited point on the surface and do another Local Hill Climbing. Here I use quotes around the word mode because we are not sure if the "mode" we found is a real mode.
4. Make sure the "mode" we found is a real mode
An Even-Height Great Wall
The "mode" we found using Local Hill Climbing might not actually be a real mode. It might be right next to a mode previously found and have a lower value (because we only checked unvisited neighbors in the previous step). It might also be part of another flat-surface mode where the mode consists of multiple points with identical values (think of a hilltop that looks like a plateau or think of a ridge). Things get even more complicated with special distributions such as this one on the right. And the "mode" point we found might be connected to a previously found mode through other points with the same value (e.g, the "mode" point is the end point of the short branch in the middle of the image.
Therefore, we need to keep track of all points leading to the final "mode" point that have identical values and check all the visited neighbors of these points, making sure this flat surface is not part of a previously found mode. If these points make up a real new mode, we mark these points with a unique mode count id (e.g, mode 3). If they are only part of a previous found mode, we mark these points so (e.g., mode 2). If one of them is right next to a previously found mode but have lower value, we mark these points as non-mode points. This step is almost like performing a Connected-Component Labeling operation in Computer Vision.
At the end of the algorithm run, we will have a count of how many modes the probability distribution has and also a map with all the mode points marked. With the Even-Height Great Wall distribution, the map would look just like the image (white pixels marking mode points) with 1 mode. And within Milli-seconds, the algorithm can identify the 4 modes in the example 3D surface above.
That's it! If you ever need to do this for your projects, you now know how!
Recursive functions work great for local hill climbing until you get a stack overflow.
Top Conferences
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IROS -- IEEE/RSJ International Conference on Intelligent Robots and Systems
IROS is a multi-track conference held annually. It is a premiere international conference in robotics and intelligent systems, bringing together an international community of researchers, educators and practitioners in the field to discuss the latest advancements in robotics and intelligent systems.
Every year, thousands of people all over the world attend the IROS conference and a large number of papers get published in the conference. For example, the IROS 2011 conference received 2541 papers and proposals and accepted 790 papers, with a 32% acceptance rate. However, the acceptance rate for IROS is normally much higher at around 55%. Every year the IROS has a different theme. The theme for IROS 2011 is Human-Centered Robotics, and the theme for IROS 2012 is Robotics for Quality of Life and Sustainable Development. However, people generally ignore the theme and submit whatever they have. I have been fortunate enough to attend the IROS conference in 2009 and published a paper on UAV path planning there.
The next IROS conference IROS 2011 will be held at San Francisco, California, USA.
Conference Dates: September 25-30, 2011
Submission Deadline: March 28, 2011
The next IROS conference you can submit a paper to is IROS 2012. It will be held at Vilamoura, Algarve, Portual.
Conference Dates: October 7-10, 2012
Submission Deadline: March 10, 2012
ICRA -- IEEE International Conference on Robotics and Automation
ICRA is a multi-track premiere conference in the robotics community held annually. It is in the same league with IROS and is also a major international conference with large attendance. The ICRA 2011 conference held in Shanghai, China welcomed more than 1,500 people around the world. The acceptance rate for ICRA is about 45%.
ICRA also has yearly themes. The ICRA 2011 conference's theme was "Better Robots, Better Life". The ICRA 2012 conference theme will be "Robots and Automation: Innovation for Tomorrow's Needs". Again, if you are thinking about submitting something to ICRA, don't worry about the themes. Just submit what you have on whatever topic, as long as it is related to robots or automation.
I have submitted a paper to ICRA before, but very unfortunately, the paper fell into the hands of several electrical engineer reviewers because I picked the wrong key words. They seem to hold grudges against computer science researchers. The same paper was accepted at IROS without any major revision. It is likely that I'll be submitting to ICRA again in the future, but I will be super careful about what key words to use this time!!
The next ICRA conference ICRA 2012 will be held at St. Paul, Minnesota, USA.
Conference Dates: May 14-18, 2012
Submission Deadline: September 16, 2011
MABEL (not an acronym) is the name of a bipedal "humanoid" robot created by researchers at University of Michigan. It just got its fame recently because it could run in a human-like gait at speeds up to 3.06 meters/second. That is 6.8 miles per hour. That is the world record for a bipedal robot with knees.
MABEL was originally built in collaboration with Jonathan Hurst, then a doctor student at the Robotics Institute at Carnegie Mellon University. Then researchers at U-M spent years improving the feed-back system in MABEL's training. MABEL was intentionally built to look like a human, with a heavier torso and light flexible legs. When it is running, MABEL is in the air for 40 percent of each stride, which is almost like a real runner. The robot can self balance in real time with a closed-loop control, and switch gaits as commanded autonomously. It can even transition from completely flat surface to uneven grounds. The video below shows MABEL running.
The researchers envision that two-legged robots can travel over rough terrains and function better in places built for humans. They can be used to enable wheelchair-bound people to walk again or to be used for robot rescuers that can step over small obstacles. Biped robots certainly have the advantage over wheeled robots when it comes to bumpy surface or stairs, however one important factor is that biped robots look more human-like compared to say a three-legged or 6-legged robot. The truth is that many four-legged animals run much faster than us two-legged human, and multiple-legged insects handle uneven surface much better than two legged ones. But would you rather have a two-legged humanoid robot serving you a drink or an eight-legged spider-looking one? Actually even the same MABEL robot walking backward looks more like a bird and seems weird (see video below).
I could envision multiple-legged robots to be very useful in space colonization. Most likely the surface terrain at another planet would not be flat, and the multiple-legged robots could easily transport goods for human and explore the planet surface more efficiently.
Back to the MABEL robot. It's hard to describe the feeling when I watched the robot running with large strides. There's certainly some uncanny valley effects there, but there's something beautiful about the strides, because they looked natural. Now we just have to put a soccer ball in front of it and then teach it to dribble and shoot...
To find out more about the MABEL robot, visit the researcher's project page here.
Video of the Day:
An interesting Logitech commercial showing a biped humanoid blending in the human environment. Enjoy!
Top Conferences
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HRI -- ACM/IEEE International Conference on Human-Robot Interaction
HRI is a single-track, highly selective annual international conference that seeks to showcase the very best interdisciplinary and multidisciplinary research in human-robot interaction with roots in social psychology, cognitive science, HCI, human factors, artificial intelligence, robotics, organizational behavior, anthropology and many more. HRI is a relatively new and small conference because the HRI field is relatively new. The 1st HRI conference was actually held here in Salt Lake City, Utah, in March 2006, and my advisor, Dr. Michael A. Goodrich, was the General Chair for the conference. It is very unfortunate that I only started grad school two months after the 1st HRI conference and missed this great opportunity. *Sigh* HRI has been growing rapidly and gaining attentions from many research groups and institutions. The last HRI conference (6th) had attendance exceeding 300 participants. HRI is also a top-tier conference with an acceptance rate between 19% and 25%. As the conference becomes more and more popular, researchers from many research disciplines (e.g., human factors, cognitive science, psychology, linguistics, etc.) began participating in the conference.
The venue of the HRI conference rotates among North America, Europe, and Asia. I have been lucky enough to attend the conference twice, once in 2010 and once in 2011. In 2010, I attended the HRI Young Pioneer Workshop. The workshop is a great event because you not only get to make friends with a bunch of young researchers in the HRI field before the conference starts, you also get to see what other young researchers are working on. Besides, NSF is generous enough to cover a good portion of the airfare, which is great help for poor grad students. I liked the workshop so much that I joined the organizing committee for the next year's HRI Young Pioneer Workshop, and also hosted the panel discussion at the workshop. That was also the reason why I was able to attend HRI 2011. Also in both HRI 2010 and HRI 2011, I guarded my advisor's late-breaking report poster sessions because he couldn't make it.
I have never submitted anything to the main HRI conference. Since this is the top conference in my research field, I'd like to publish something before I graduate.
The next HRI conference HRI 2012 (the 7th) will be held at Boston, Massachusetts, USA.
Conference Dates: March 5-8, 2012
Submission Deadline: September 9, 2011
CHI -- ACM/SIGCHI Human Factors in Computing Systems - the CHI Conference
CHI is considered the most prestigious conference in the field of human-computer interaction.It is a multi-track conference held annually. Because of the heavy interests and involvement from industry leaders, large tech companies such as Microsoft, Google, and Apple are frequent participants and organizers of the conference. CHI is a top-tier conference with acceptance rate between 20% and 25%.
Human-Computer Interaction is a broad field that includes both software and hardware. The goal of HCI is to develop methods, interfaces, interaction techniques to make computer devices more usable and receptive to the user's needs. These days computer devices could include a wide variety of things such as cell phones, tablets, game consoles, or gadgets. Thanks to the advancement of sensor technologies, a whole set of rich interaction techniques have emerged to work with gestures, device orientations, and motion of the device.
Many of the HCI design principles, interface designs, and interaction techniques are relevant in Human-Robot Interaction. After all, a robot must have some kind of computer embedded (whether tiny or full-size, whether one or multiple). In many HRI tasks, the human user could very well be interacting with the robot through a regular computer or a touch screen device (think tele-presence, for example). I have never attended the CHI conference before, but I have heard a lot about it from Dr. Dan Olsen at BYU because he was always some kind of chair in the CHI organizing committee. In fact, he'll be the paper chair in the next CHI conference.
The next CHI conference CHI 2012 will be held at Austin, Texas, USA.
Conference Dates: May 5-10, 2012
Submission Deadline: September 23, 2011
Every time when you clip your finger nails, think what you have achieved since you last clipped your finger nail. If you can remember what you did, then you have not wasted your life.
Miao Shaotian walked at the very end, gripping the pair of golden rings tightly in his hands, so tight that the blue veins on the back of his hands almost popped out.
He shouldn’t have come, but he must.
That merchandise seemed to be emitting a strange field of attraction, sucking him close one step after another. He was not going to give it up until the last moment.
Two statue-like guards stood at the entrance of the underground tunnel. Then for every dozen steps forward, two more guards stood along the way, their faces as grim as the green stones in the walls.
A rampant green dragon was carved onto the stone walls.
It was said that the Green Dragon Clan had three hundred and sixty-five secret branches. This was undoubtedly one of them.
At the end of the underground tunnel stood a gate made of very thick iron railings.
Gongsun Jing took out a large chain of keys from his waist band and then opened three locks with three of them. Only then did the two guards behind the iron bars pulled the gate open.
But this was still not the last gate.
“I know many people can get in here. The guards here are not difficult to deal with. But from here onward would prove to be an arduous task,” Gongsun Jing explained.
“Why’s that?” Young Master Zhu asked.
“Between here and that stone door over there, there are a total of thirteen hidden traps. I can guarantee that there are no more than seven people in the entire world who could successfully get through all thirteen traps.”
“Luckily I am definitely not one of those seven people,” Young Master Zhu heaved a sigh.
“Why don’t you give it a try?” Gongsun Jing smiled even more politely.
“Perhaps I’ll give it a try at a later time, but not right now,” Young Master Zhu said.
“Why not now?” asked Gongsun Jing.
“Because I am perfectly happy staying alive,” replied Young Master Zhu.
The distance between the iron bars and the stone door was actually not far, but after hearing Gongsun Jing’s words, the path seemed to be ten times farther, and the stone door seemed to be even heavier.
Gongsun Jing used another three keys to unlock the door.
Behind the two-foot thick stone door was a nine-foot wide stone cell.
The room was ghastly and chilly as if it were the center of an ancient emperor’s tomb, only that a giant iron chest sat at the spot instead of the coffin.
Opening the iron chest of course required another three keys, but that was not the end of it because there was a small iron chest inside the giant one.
“Such maximum security perhaps deserves some higher prices from us,” Young Master Zhu said with a sigh.
“Young Master Zhu is very clever indeed,” said Gongsun Jing with a big smile.
Taking out the small iron chest, he unlocked it and opened the lid, but all of a sudden, his affable smile disappeared and his face looked as if someone had just shoved a rotten tomato down his throat.
The small iron chest turned out to be empty except a single piece of paper.
The paper only showed nine words, “Thank you! You are such a nice guy!”
Now support the translator Lanny by following my blog and leaving comments! :)
Video of the Day:
Excerpt from the Shanghai World Expo Closing Ceremony Concert - Fusion of Art and Music. Enjoy!
Santa Cruz police officers arresting a woman at a location
flagged by a computer program as high risk for car burglaries.
(Photo Credit: ERICA GOODE)
I came across an article recently talking about how Santa Cruz police department has been testing a new method where they use computer programs to predict when and where crimes are likely to happen and then send cops to that area for proactive policing. Although the movie "Minority Report" by Tom Cruise immediately came to my mind, that was actually not the same. The computer program was developed by a group of researchers consisting of two mathematicians, an anthropologist, and a criminologist. The program uses a mathematical model to read in crime data in the same area for the past 8 years and then predict time and location of areas with high probability for certain type of crimes. The program apparently can read in new data daily. This kind of program is attracting interests from law enforcement agencies because the agencies are getting a lot more calls for service when the number of staff is much less due to poor economy. This requires them to deploy resources more effectively. The article did not disclose much detail about the mathematical model (because it is a news article, not a research paper, duh), but it is probably safe to assume the model tries to identify patterns from past crime data and then assign probabilities to each grid cell (500 feet by 500 feet).
A multi-modal probability distribution predicting likely places
to find the missing person and a UAV path generated by algorithm.
I found this article especially interesting because in my research I am solving a similar problem with a similar approach. My research focuses on how an Unmanned Aerial Vehicle (UAV) can be used more efficiently and effectively by searchers and rescuers in wilderness search and rescue operations. One part of the problem is trying to predict where are likely places the lost person might be found. Another part of the problem is to generate an efficient path for the UAV so it will cover those high probability areas well with limited flight time. I've also developed a mathematical model (a Bayesian approach) that uses terrain features to predict the lost person's movement behavior, and also incorporate human behavior patterns from past data in the form of GPS track logs.
In both cases, the problems arise because of limited resources.
It is very important to remember that no one has the real crystal ball, so predictions can not be 100% correct. Also the prediction is in the form of a probability distribution, meaning in the long run (with many cases), the predictions are likely correct a good percentage of the time, but for each individual cases, the prediction could very possibly be wrong. This applies to both predictive policing and wilderness search and rescue.
Another important question to ask is how do you know your model is good and useful. This is difficult to answer because, again, we don't know the future. It is possible to pretend part of our past data is actually from "the future," but there are many metrics, what if the model performed well with respect to one metric, but performed terribly with respect to another metric? Which metric to use might be related to the individual case. For example, should number of arrests be used to measure the effectiveness of the system? Maybe by sending police officers to certain areas would scare off criminals and actually result in a reduction in number of arrests.
The predictive policing problem probably holds an advantage over the wilderness search and rescue problem because a lot more crimes are committed than people getting lost in the wilderness resulting in a much richer dataset. Also path planning for police offices is a multiagent problem while we only give the searchers and rescuers one UAV.
One problem with such predictive systems is that users might grow to fully rely on the system. This is an issue of Trust in Automation. Under-trust might waste resources, but over-trust might also lead to bad consequences. One thing to remember is that no matter how complicated the mathematical model is, it is still a simplified version of the reality. Therefore, calibrating the user's trust becomes an important issue and the user really need to know the strength of the AI system and also the weakness of the AI system. The product of the AI system should be complementary to the user to reduce the user's work and remind the user places that might be overlooked. The user also should be able to incorporate his/her domain knowledge/experience into the AI system to manage the autonomy. In my research, I am actually designing tools that allow users to take advantage of their expertise and manage autonomy at three different scales. I'll probably talk more about that in another blog post.
Anyway, it's good to see Artificial Intelligence used in yet another real life applications!
You shall not carry a brass knuckle in Texas because it is considered an illegal weapon (but in California you'll be just fine). Don't you love the complication of the US legal system, which by the way, serves big corporations really well.
On July 4th, 1997, which also happened to be the Independence Day of the United States, the Mars Pathfinder successfully landed on Mars. It made history because it was:
The third lander (since the two Vikings) successfully landing on Mars.
The first time a bouncing air bag landing mechanism was used for a lander.
The first time a robot rover was successfully deployed.
The first time a space mission was broadcasted on the Internet live.
After the successful landing, images of the mysterious red planet from the planet surface was broadcasted "live" on the Internet. This event had profound and extraordinary impacts on the public interests in space exploration, robotics technology, and web technologies, and inspired a generation of potential roboticists.
The Mars Pathfinder consisted of a lander and lightweight wheeled robotic rover named Sojourner (named after a a nineteenth-century black feminist and campaigner for the abolition of slavery). It was wrapped in large airbags. After entering the Martian atmosphere, a parachute was first deployed to slow down the falling of the capsule. Then a self-inflating airbag system in the shape of a tetrahedral was released, which "soft" landed on the terrain surface of Mars and rolled and bounced up and down all over the place. After the tetrahedral finally stopped rolling, the airbags were deflated and the lander unfolded itself, letting lose of the robotic rover. It is simply mind-boggling to see how the lander and the rover survived such vigorous movements, especially when one would have expected the scientific equipments on board to be very delicate devices. The video below shows some animations and footage of the landing process.
The main objective of the mission was to demonstrate it is possible to perform extraterrestrial exploration with low cost. As added benefit, the Mars Pathfinder also conducted some scientific experiments with a cameras, atmospheric structure instruments, and a spectrometer on the rover. The rover had six independently-controlled wheels and performed rock analysis as it roved about not far from the lander. The video below shows some footage of the rover moving about.
Roughly three months later, the mission control lost contact with the Pathfinder, but the mission had exceeded its goals just during the first month. Although still visible from Mars Reconnaissance Orbiter up high in the Martian sky, the robot (system) had become fully autonomous and just wondered about like a lonely ghost. Just like its name suggests, it had finally broken free from its human masters and became a free, uh, robot!
When I interned at NASA Ames in California in 2009, I was very fortunate to spot a prototype of the Sojourner Rover at the Intelligent Robotics Group (see pic on the left). I am strong believer in space colonization because we must "spread the seeds of human civilization" before we totally destroy our planet earth. And to make space colonization possible, we totally need robots that can build habitats for us. I wish the government would spend more on robotics and space exploration instead of sending troops to other countries to torture their citizens under the name of spreading "democracy" and "freedom".
Anyway, if you want to find out more about the Mars Pathfinder, you can watch "The Pathfinders" Documentary on YouTube.
Picture of the Day:
Photo of a meteor taken by astronaut from the International Space Station.
