Техническое задание


1️⃣ System Architecture

An AI robot typically consists of the following subsystems:

🔹 1. Perception Layer

Collects environmental data using:

  • RGB / Depth cameras

  • LiDAR

  • Ultrasonic sensors

  • IMUs (Inertial Measurement Units)

  • Microphones

Data is processed using:

  • Computer Vision (e.g., object detection, SLAM)

  • Signal processing

  • Sensor fusion algorithms


🔹 2. Cognition / Intelligence Layer

Implements AI models such as:

  • Machine Learning (ML)

  • Deep Learning (CNNs, RNNs, Transformers)

  • Reinforcement Learning (RL)

  • Path planning algorithms (A*, Dijkstra)

  • Probabilistic reasoning (Bayesian networks)

This layer handles:

  • Object recognition

  • Localization and mapping

  • Motion planning

  • Decision-making

  • Adaptive learning


🔹 3. Control Layer

Executes actions using:

  • PID controllers

  • Model Predictive Control (MPC)

  • Kinematic and dynamic models

  • Real-time feedback loops

Ensures stability, precision, and safety.


🔹 4. Actuation Layer

Includes:

  • Servo motors

  • Stepper motors

  • Robotic arms

  • Wheels / Leg mechanisms

  • Grippers

Converts control signals into physical movement.


2️⃣ Software Frameworks

AI robots are often built using:

  • Robot Operating System (ROS) for communication and modular architecture

  • TensorFlow or PyTorch for AI model training

  • Embedded systems (e.g., microcontrollers, SBCs like NVIDIA Jetson)


3️⃣ Operational Characteristics

An AI robot demonstrates:

  • Autonomy – Operates without continuous human control

  • Adaptability – Learns from data and improves performance

  • Real-time Processing – Responds within milliseconds

  • Environmental Awareness – Maintains internal world models


4️⃣ Mathematical Foundation

AI robots rely on:

  • Linear algebra (matrix operations)

  • Probability & statistics

  • Optimization algorithms

  • Control theory

  • Differential equations (robot dynamics)


🔬 Formal Definition

An AI robot is an autonomous robotic system that employs machine learning, perception algorithms, and control theory to interpret sensory data, generate adaptive decisions, and execute coordinated physical actions in dynamic environments.


Откликнулись

1
Разработчик 1
Оценка
Проекты
0
0%
Арбитраж
0
Просрочено
0
Свободен
2
Разработчик 2
Оценка
(2)
Проекты
3
33%
Арбитраж
1
0% / 100%
Просрочено
0
Свободен
3
Разработчик 3
Оценка
(16)
Проекты
35
23%
Арбитраж
4
0% / 50%
Просрочено
2
6%
Работает
4
Разработчик 4
Оценка
(5)
Проекты
6
67%
Арбитраж
0
Просрочено
0
Работает
Похожие заказы
I want to create a loss recovery account.I have a zone recovery EA.It sometimes gives more buy sell entries than the target. This causes a lot of loss. I want to create an EA to reduce that loss by 40% to 50%. Example: My EA has lost $2200 with a buy sell entry. Now the new EA will give an entry that can reduce the loss from $2200 to $700
I need any highly profitable MT5 robot which trades any sythetic indices on deriv very profitably. It should have good risk management and any good strategy The EA should have good risk management and can trade small accounts like 50 - 100USD Developers who have already made robots have higher chance
Mk 30+ USD
I need a fully automated trading robot designed to generate consistent profits while strictly controlling risk and minimizing losses. The robot should use a combination of strategies, including trend-following, scalping, and price action, and must be able to adapt to different market conditions such as trending and ranging markets. It should analyze the market using indicators like Moving Averages, RSI, MACD, and
1. IF price forms: - Higher highs + higher lows → TREND = BUY - Lower highs + lower lows → TREND = SELL ELSE → NO TRADE 2. IF: - Trend = BUY - Price retraces to support zone - Bullish engulfing candle forms - TDI green crosses above red (optional) THEN: - Execute BUY 3. IF: - Trend = SELL - Price retraces to resistance - Bearish engulfing forms - TDI confirms THEN: - Execute SELL 4. Risk per trade = 1% of account Lot
Apply with a screen of your work . Symbol Specific Logic . Live Chart Optimization Check the Core logic . [back tests as well] Change points to pips . Create buffer for the zone
I already have an MT5 trading bot called Omega Bot and I am looking for a marketer / seller who can help me find real buyers. Commission: 100 USD per successful sale Bot details: - MT5 only - Gold and Silver - M15 - ORB strategy - Asia and New York sessions - Automatic risk management - Break-even - Trailing stop - Spread filter - Beginner friendly - Ready settings file - Trial version available - Support after sale
Fair Value Gap Expert , Optimize the core logic for live chart . [Filters are working] Lets ace the trailing stop . Change points to pip . Project will start from next week
have the Beatrix Inventor Expert Advisor (EA) that was profitable in the past but has been losing money recently. I need an experienced EA developer/optimizer to study the trade history (especially Stop Loss hits, drawdown periods, SL/TP behavior, win/loss ratio, etc.) and recommend + implement specific tweaks so it becomes consistently profitable again. Your job: 1. Deep analysis of why the EA is no longer
Required Filters are working as per specification and requirement . Stop Loss Trailing needs correct execution for live chart . Need a little advice on trailing stop loss correction . Live chart only
Please explain all the details, including the entry and exit conditions . Refine signal trigger execution . Optimize live chart performance . Ensure stable and clean code structure : Stable and clean code is important . Otherwise its a mess . Apply with as much accurate structure you foresee . requests for details of the project will be ignored

Информация о проекте

Бюджет
30 - 50 USD