Mobile Robot Control 2024 Ultron:Solution 2: Difference between revisions
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====Artificial Potential Field==== | ====Artificial Potential Field==== | ||
The '''Artificial Potential Field (APF)''' algorithm achieves obstacle avoidance and navigation by simulating a potential field. This algorithm combines attractive and repulsive forces, and determines the direction and speed of the robot's movement by calculating the resultant force direction. | |||
Revision as of 10:16, 17 May 2024
Methodology
Artificial Potential Field
The Artificial Potential Field (APF) algorithm achieves obstacle avoidance and navigation by simulating a potential field. This algorithm combines attractive and repulsive forces, and determines the direction and speed of the robot's movement by calculating the resultant force direction.
Dynamic Window Approach
The Dynamic Window Approach (DWA) algorithm simulates motion trajectories in velocity space [math]\displaystyle{ (v, \omega) }[/math] for a certain period of time. It evaluates these trajectories using an evaluation function and selects the optimal trajectory corresponding to [math]\displaystyle{ (v, \omega) }[/math] to drive the robot's motion.
Consider velocities which have to be
- Possible: velocities are limited by robot’s dynamics
[math]\displaystyle{ V_s = \{(v, \omega) \mid v \in [v_{\min}, v_{\max}] \land \omega \in [\omega_{\min}, \omega_{\max}]\} }[/math]
- Admissible: robot can stop before reaching the closest obstacle
[math]\displaystyle{ V_a = \{(v, \omega) \mid v \leq \sqrt{2 d(v, \omega) \dot{v_b}} \land \omega \leq \sqrt{2 d(v, \omega) \dot{\omega_b}}\} }[/math]
- Reachable: velocity and acceleration constraints (dynamic window)
[math]\displaystyle{ V_d = \{(v, \omega) \mid v \in [v_a - \dot{v} t, v_a + \dot{v} t] \land \omega \in [\omega_a - \dot{\omega} t, \omega_a + \dot{\omega} t]\} }[/math]
Intersection of possible, admissible and reachable velocities provides the search space: [math]\displaystyle{ V_r = V_s \cap V_a \cap V_d }[/math]
for k = 1:len(ω_range) for i = 0:N x(i + 1) = x(i) + Δt * v_range(j) * cos(θ(i)) y(i + 1) = y(i) + Δt * v_range(j) * sin(θ(i)) θ(i + 1) = θ(i) + Δt * ω_range(k) end end
Then the objective function is introduced to score the trajectories and select the optimal trajectory.
[math]\displaystyle{ G(v, \omega) = \sigma ( k_h h(v, \omega) + k_d d(v, \omega) + k_s s(v, \omega) ) }[/math]
- [math]\displaystyle{ h(v, \omega) }[/math]: target heading towards goal
- [math]\displaystyle{ d(v, \omega) }[/math]: distance to closest obstacle on trajectory
- [math]\displaystyle{ s(v, \omega) }[/math]: forward velocity