Place a regular grid G over the configuration space
Compute the potential field over G
Search G using a best-first algorithm with potential field as the heuristic function
Note: A heuristic function or simply a heuristic is a function that ranks alternatives in various search algorithms at each branching step basing on an available information in order to make a decision which branch is to be followed during a search.
Given a current knowledge about the robot state and the environment, how to select the next sensing action or sequence of actions. A vehicle is moving autonomously through an environment gathering information from sensors. The sensor data are used. to generate the robot actions
Beginning from a starting configuration (x s ,y s , s ) to a goal configuration (x g ,y g , g ) in the presence of a reference trajectory and without it; With and without obstacles; Taking into account the constraints on the velocity, steering angle, the obstacles, and other constraints …
Between two points there are an infinite number of possible trajectories. But not each trajectory from the configuration space represents a feasible trajectory for the robot.
How to move in the best way according to a criterion from the starting to a goal configuration?
The key idea is to use some parameterized family of possible trajectories and thus to reduce the infinite-dimensional problem to a finitely parametrized optimization problem. To characterize the robot motion and to process the sensor information in efficient way, an appropriate criterion is need. So, active sensing is a decision making, global optimization problem subject to constraints .
p : vector of parameters obeying to preset constraints;
Given N number of harmonic functions, the new modified robot trajectory is generated on the basis of the reference trajectory by a lateral deviation as a linear superposition
Gives the possibility to move easily the robot to the desired final point;
Easy to implement;
Multisinusoidal signals are reach excitation signals and often used in the experimental identification. They have proved advantages for control generation of nonholonomic WMR (assure smooth stabilization). For canonical chained systems Brockett (1981) showed that optimal inputs are sinusoids at integrally related frequencies, namely 2 , 2. 2 , …, m/2. 2 .
fmincon finds the constrained minimum of a function of several variables
With small number of sinusoids (N<5) the computational complexity is such that it is easily implemented on-line. With more sinusoidal terms (N>10), the complexity (time, number of computations) is growing up and a powerful computer is required or off-line computation. All the performed experiments prove that the trajectories generated even with N=3 sinusoidal terms respond to the imposed requirements.
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