The vacuum cleaner robot should have a mechanism such as the artificial intelligence to solve the problem of cleaning the entire environment areas taking into account some factors such as the number of turns and the length of the trajectory. This robot’s mechanism or mission is known as coverage area route planning (PPCR). In this study, we suggest an evolutionary strategy to solving the issue of PPCR in a room setting. The latter is based on Genetic Algorithms (GA) which, consist of several steps to get the solutions. Each gene represents a robot location, and certain chromosomes also represent a mini-path. Moreover, this algorithm assists the robot in navigating the surroundings by avoiding obstacles with the use of several sensors. The results of simulation and comparative studies show that the suggested strategy is successful and efficient.
What algorithms are used in robotics?
MSVM, LSTM, MCTS, and CNN are examples of supervised learning algorithms. Q learning, DQN, double DQN, and dueling DQN are examples of optimal value reinforcement learning algorithms. The policy gradient technique, actor-critic algorithm, A3C, A2C, DPG, DDPG, TRPO, and PPO are examples of policy gradient algorithms.
What is the Roomba algorithm?
Visual simultaneous location and mapping, or VSLAM, is a navigation method used by these vacuums.
How are robot vacuums programmed?
They are configured to spin and advance until the gadget detects a clear passage. The location of the bumper determines the direction of the robotic cleaner. For example, if a vacuum cleaner detects an obstruction on its left bumper, it moves to the right side to avoid the object.
What kind of AI does Roomba use?
It employs actual artificial intelligence. Now, when you let the bot loose on your living room, it will scan the room size, identify obstacles, and remember how to clean the carpet and which routes work best.