10

For testing simple algorithms, you might be able to get by with a 2D simulator. There are a few out there that I am aware of: Stage: http://playerstage.sourceforge.net/index.php?src=stage STDR: http://stdr-simulator-ros-pkg.github.io/ Stage is an older, but useful, simulator which has integration with ROS (http: //wiki.ros.org/stage_ros) which will allow ...


8

The function $f$ comes from the equation of motion for the inverted pendulum problem (inverted pendulum alone, not including the motion of the wheeled platform). If you consider your figure but ignore the side-to-side motion of the cart, then the equilibrium of moments about the hinge is: $\sum M = m g l \sin \theta - b\frac{d \theta}{dt} $ Where $m$ is ...


5

First, here's what you CAN do with those sensors. Assuming you are not constantly accelerating you can use the accelerometer to know which direction is "down" (the gyro can be used as well for faster updates). If there aren't any magnetic field disturbances, you can also use the compass to know which direction is forward. Usually this is done using either an ...


4

The mechanics of your vehicle are not extremely relevant here; I will assume that the motion your vehicle induces on the sensors will be within their specifications. Entire volumes have been written on "sensor fusion", which is the act of combining measurements from multiple sensors (e.g. your gyro, accelerometer, and compass). Doing this 100% accurately ...


3

I think yes it can but how? My options are here: Static system for conventional systems It should stand or hang to line/pole/special place like birds. Please watch video for an example: https://www.youtube.com/watch?v=MvRTALJp8DM Dynamic system Harmless/secure distance present day wireless charging methods: use low energy harvesting drone or an ...


3

Sure, a drone can land on a powerline. That's a standard task like the "peg in hole problem" for robotarms. The aim is to maneuver a UAV near to a highvoltage line and eating all the energy. The earliest paper was written in 2009 and has a nice plotchart of the Electro-Magnetic Field on page 8 Powerline perching with a fixed-wing UAV for directing the UAV in ...


3

I think the problem you're going to find is that machine learning requires learning. If the goals or objective vary just a little, then manually adjusting the software shouldn't be too difficult (if you programmed it well), and it might only take a few trials for the AI to adjust to the new scenario. If the objectives change so much that you're looking at "...


3

I cannot comment on 'most common', but I can definitely share several tools and research efforts towards using FPGA for deep-learning. See my survey paper on FPGA-based accelerators for CNN which reviews 75+ recent papers. Some of these research projects have released their code, such as DNNWeaver. Also, see tools from companies such as Xilinx. Finally, see ...


3

Many reainforcement learning methods require descrete actions. As you indentified, increasing and decreasing the values is one option. If it is an adaptive PID, then it might take some time to incerase the parameters if you only have an increase by the factor of 0.1. I would recommend more then one increasing factor as possible action. Increase by 0.1, 1, 10,...


3

I read a bit more and realized that in RL states and rewards accept a wide variety of interpretations and this is the real complexity nowadays of this learning problem. In case of PID values, problem can be formulated as the following: imagine a Kp value, it represents a state. Next state could be increase or decrease 0.1. Same with the next state, and so ...


3

It actually makes sense that the dot product in both cases is the same (zero) because the dot product of two vectors does not consider the vectors' origins. Or in other words the math for the dot product places the two vectors at the same origin. In this sense there is no way to distinguish converging or diverging vectors. I think what you need to do is to ...


3

Regarding methodologies and tools, I recommend Chris Eliasmith's How to Build a Brain. It presents the Semantic Pointer Architecture (SPA), a cognitive model that has been realized in the open source Nengo toolkit. I have read the book's introduction and some of Eliasmith's papers, and so far the approach looks very promising.


3

You've asked more than one question, so I'll try to answer them in order. The Robotics community has not yet hit the limits of current hardware, so very little work is being done on the exotic cutting edge like neuromorphic hardware. The exception to this is software neural nets, which have come in and out of fashion for decades, and the Nv artificial ...


3

You need enough domain knowledge to be able to tell if someone is bullshitting you or not, to be able to determine when someone has an achievable or unachievable project idea, to be able to determine who has talent and who doesn't, etc. And money. A lot of money. Quality engineers don't work for free.


2

Unless you are talking about something higher-order (like learning the behavior of an autonomous agent and predicting how it will move in the future), what you are looking for is to create a simple physics simulator. It will be closed-form, unless you are asking how to build a system that can "learn" the laws of physics.


2

The recently open-sourced V-REP simulator may suite your needs. I found it more approachable than Gazebo, and it can run on Windows, OSX, and Linux. Their tutorials are fairly straight forward. There are a ton of different ways to interface with it programmatically (including with ROS). It looks like there is even a tutorial for making a hexapod, which you ...


2

If you have no prior experience in robotics, I would recommend one well known book, Craig - Introduction of Robotics: Mechanics and Control. This should explain well the basics of robotic manipulators. For artificial intelligence (AI), I would recommend Russel, Norvig - Artificial Intelligence, a Modern Approach, although ANNs are not the central topic of ...


2

If you're only trying to walk forward the fitness function could be the distance covered by the biped. If you're also trying to control the heading, you could define a slightly more complex function which correlates the covered distance and the heading input.


2

One page linked says the algorithm is fine for the ATmega328's 2KB of SRAM, another is for the ATmega2560's 8KB of SRAM. Your MSP430G2553 has 0.5KB of RAM, so I think that's the primary reason you can't get it to run on that controller. There are other microcontrollers in the MSP430 line that have more memory - if you look at the other LaunchPads you can ...


2

Nathan Ratliff has documented some very nice papers in Control Theory and Motion Optimization. You can access them here and here, resp. Particularly related to decision making problems, you might want to check Geoff Hollinger and Gaurav Sukhatme's course. The have a good list of readings in a logical flow. In general robotics, you may want to look at ...


2

I think there are some confusing statements or inaccuracies in this question regarding fundamental concepts in robotics, control engineerging and AI/ML. I am not sure if it helps to list these or not, so I will not I just wanted to draw attention to this fact if somebody tries to used the question as a reference. However, to answer the final question: ...


2

You can printout your weights and bias value from your model in TensorFlow, PyTorch or other deep learning library. If you want to convert your model to ESP32, you must create your own code to define your feed forward network. It's very simple, like create a matrix multiplication program and also create your own activation function. Then, you take weights ...


1

I am not aware of any online repository that particularly collects seminal papers in robotics. But I think anyone working or having worked in robotics for some time would more or less have their own collection of the so-called seminal papers in their field. So, here are motion planning-related papers that I think are within the scope of this question. (...


1

In general, yes. Just create the network so that it can be run on another machine or the cloud for training. Training basically consists of running many, many test scenarios through the network, and this gradually assigns values to each 'neuron' and connection (I oversimplify slightly). Once the network is trained, you can copy the trained network onto ...


1

in my opinion one possibility on how the robot could find people with fire is by using image processing. You can obtain a real time Infra red image of the outside world and then check for levels of heat measured. Using recognition alogirthms the robot could decide whether a particular figure is a human or not and if it is a human then by checking the heat ...


1

First make sure you have the camera's drivers available for the operating system you'll be running your code on. If drivers are available for your platform check what resolutions and fps(s) you can read from the camera using the driver. Once you have those things in place you'll be able to do anything in cv that will accept raw (YUV or YUV2 or whatever ...


1

If the university project is not directly about making a drone detection system using visual technic, it may not be worth the effort to connect the "camera" to the computer. (You may have to install new drivers, etc). However, technically it's quite possible to run a feature-detection-code once you can integrate the camera to that code. There are several ...


1

I don't know that the DMP method actually simulates physics as much as it copies what it saw, which was an actual, physical system. Note that "qualitative" means essentially "looks like", where "quantitative" means "numerically". Qualitative physics simulations are those that look like real physics, but they are not. If I hold a dart in my hand and walk it ...


1

Ok, It sounds like you either saw an arm with hydraulic actuators or a Hollywood special-effects movie (they really like hoses in their robots). Assuming you saw real hydraulic actuators, then I need to start by saying that is only cost effective if you need a lot of force. The majority of robots use electric actuators, specifically servo motors. Hydraulic ...


1

HRL has been embodied in a robot in multiple cases. In a reaching, shelving robot. In a robot learning how to stand-up. In robot navigation. However, how HRL applied in each of these cases varies. The first uses HRL to manipulate Dynamic Movement Primitives, while the second, older method focuses moreso on learning state space values.


Only top voted, non community-wiki answers of a minimum length are eligible