Getting from a position we label $a$ to some point we expect to exist we label $b$ is one of the basic capabilities we need from intelligent systems. It is not as intelligent as, for instance, developing a business plan, but it does involve planning, often the use of a map, recognition of landmarks if the path has been traversed or partly traversed before, and avoidance of an accident.
For an automated vehicle, avoiding objects can be synonymous with saving lives. For an automated lawnmower, avoiding objects is not quite as high risk but still key to the safe mowing of the lawn. Obstacle and pedestrian avoidance is important. It may be an operation carried out a thousand times a microsecond by computers in consumer products used globally just a few decades from this writing.
Route Planning, Obstacle Avoidance, and Map Maintenance
A* and Dijkstra are not obstacle avoidance algorithms. They are route planning algorithms.
They do, however, require that edges connecting vertices are traversable. They do not process nontraversability of edges. All edges are traversable because none are built through obstructions.
The cost in these strategies is related to time and distance, not risk of accident, so the aversion is to long or slow paths through the graph of vertices and edges that represents the transportation option map.
They always require me to have points in place to traverse.
Yes, the vertices and edges are not discovered as a part of A* or Dijkstra. The existence of a map is assumed, and the visual and auditory ques that humans would use to mow a lawn, route a train, fly a plane, or drive a truck or car are not connected to algorithmic choices.
The awareness, which we can consider a repeatedly updated milestone in planning, sought in the question is, when seeking a path $a \Rightarrow b$, can we maintain a directed graph based on visual (and perhaps auditory) queues? This is a step toward the important AI goal of corroborating map information with real time ascertainable immediate environment. It is also a step toward real time collaboration between autonomous vehicles to maintain a globally shared map.
The maintaining of a map is required in automated driving to cope with when a street or lane is closed and it is one possible way to deal with foot traffic. In automated vacuum cleaning, map maintenance is required to deal with both foot traffic, toys or clothes on the floor, or re-positioned furnishings.
Building a Map from Scratch
The specific system envisioned in the question is an image where a blob can be distinguished in an image when the camera is pointed at $b$ from $a$, assuming we at least have the coordinates of $b$ relative to $a$ so we can point the camera toward it. Can we teach a network to construct the points or array of points that allows passage around the blob at a safe clearance distance?
Yes, but we may not need to. Assuming that as the vehicle or robot moves it will reevaluate, we can determine which side to aim toward based on which angle of approach is closest to the angle of $b$. Unless we know something about what may be after the blob, that will always be most likely shortest path. However, the blob may have a sign in front of it with an arrow pointing left. That is where convolution methods and other machine learning techniques can plan an important role.
Reductionism Probably Not Applicable
The broader question asked is whether one can "[teach a network] to get from point $a$ to point $b$ is another possible application for an architecture of transportation automation that includes artificial learning networks.
However, a reductionist strategy is not the best for chaotic systems. It is not from learning simple maps that complex maps can be maintained or utilized well, it is from learning complex maps. In other words, with most of the current machine learning approaches, we do not teach the machine like we would a child, simple first.
We want the statistical distribution of permutations in the concept class being learned to be the same between the training examples and the scenarios over which the learned behavior is expected to work.
Web searches are an excellent way to find information today. Searching on the web has replaced many long standing news and information sources, but telling the difference between transient hype and information that is likely to be both pertinent and valid and that it will continue to be a year from now is still an art.
It isn't smart to completely dismiss what the web searches return for a simple list of words that describes what it is we seek. And Google's acquisition of DeepMind and the bright minded game developers may provide partial or complete solutions for the kinds of problems this question asks, but it is wise to diversify our information sources.
We can buy some books, join some discussions, watch some video recorded lectures, refine our searches, and reading some scholarly articles. One key to finding authentic and accurate information is to, as a scientist should, look for corroboration between theory and actual experimental results.
Nonetheless, it was an educated guess at a web search and a few educated guesses for clicks from the returned results that led to this excellent article which outlines the various map usage options for engineers.
Engineering Route Planning Algorithms, Daniel Delling, Peter Sanders, Dominik Schultes, and Dorothea Wagner, 2009
It does not discuss the construction of maps from scratch, corroboration of maps with visual observations, or the maintenance of map information locally or more globally.
The High Challenge of Real Time Map and Route Adjustment
This array of capabilities is not new. Seeking in robotic devices the ability to find paths without maps, build maps from discovery, and then use them later began with Claude Shannon and his mouse Theseus. It was a maze learning mouse he devised and built in 1952, quite a remarkable achievement since the supercomputers of that time have similar computing power to low end smart phones of today.
Shannon is one of the key people in the development of digital systems, probably the most important of them all. Reading anything he wrote about information theory is not only pertinent to this problem, but also any other problem you are likely to encounter in the development of virtually any type of system today.
Certainly, information theory is key to this problem of automated map-making, map using, and maintaining one already made, all of which require in varying degrees the processing of visual and auditory information in real time. What percentage of these systems will end up falling under the label Artificial Intelligence and what is straightforward applications of engineering formulae is anyone's guess.
But consider the range of products that rely on the development of these transport automation technologies.
- Parcel pick-up and delivery
- Mail pick-up and delivery
- Other road motor vehicles
- Commercial, private, and military aircraft
- Vacuum cleaners
- Lawn mowers, trimmers, and leaf blowers
- Robots that specialize in doing the laundry
- Robots that specialize in snow and ice removal
- Robots that go find things we need right now and put them back later
Buses are distinguished from other vehicles and listed first because they are probably the easiest to automate. Buses have the greatest weekly route consistency and strictest constraints on lane changes.
This last one is the king of map-making and map reading in multiple dimensions and across arbitrary domains. We had hope for dogs, and dogs can have fun fetching things we've thrown. Retrievers have been bred to retrieve fowl we may have shot for dinner, but they don't have the capacity for fetching things we haven't hunted or thrown for them to fetch. In film-making and some business circles, the role has a name: Gofer.
Perhaps robots can be engineered with AI to the point where their breadth of understanding, both on the natural language end and on the side of organizing a map of where things should be or likely are stored. This is still essentially an $a \Rightarrow b$ problem, but with more dimensions of height, which may involve shelf level and closet door sophistication.
We can see that the $a \Rightarrow b$ scenario may be the most important first step toward a great new line of products to improve road safety and conserve resources, including energy and time.