CS401 Homework 2 - Solved

Question 1
(a)   Imagine you want your robot to perform navigation tasks, which approach would you choose?

(b)  What are the benefits of the behavior based paradigm?

(c)  Which approaches will win in the long run?

Question 2
  (a) How to generate uniform, perpendicular, attractive, repulse, tangential forces for a robot and obstacles with known positions? Provide related mathematical formulas.

  (b) Please simulate the motions of a robot with the given force fields from the following figure.

Provide codes and plots of simulation results. You can follow the instruction to complete the file potential_fields.py and run question2_run.py in source folder to generate the gif as shown below:

Uniform
Perpendicular
Attractive
 
Repulsive
Tangential
 
 
Question 3 - Extra Credit
Simulate a robot can reach the goal without sticking into a local trap. Provide codes and plots of simulation results. You can follow the instruction to complete the file question3_run.py in the source folder to generate the animation as shown below:

Coding instruction
All the assignments are performed based on intelligent robotics simulator, which provides the common robot motion model and plot functions that we can focus on the robotics algorithm directly.

Install the intelligent robotics simulator
git clone -b edu https://github.com/hanruihua/intelligent-robot-simulator.git cd intelligent-robot-simulator pip install -e . 

Note: Please confirm that this repository is under the edu branch.

Common used API
 env.robot.state: 2*1 matrix, The position of the robot with x and y

env.obs_line_states: A list of line coordinates. Each line includes the position of two points (x1, y1, x2, y2).

 env.robot.goal: 3*1 matrix, The goal of the robot with x, y and theta. env.render(): Show current figure at each step

Code for question2
There are three files for question2 in the source folder, question2.yaml, question2_run.py, and potential_fields.py.

  First is the parameter set for the simulation environment.

 Second is the run file to show perform the robot and potential field algorithm.

Third is the potential field class which defines the primitive potential fields as shown in the mentioned figure.

You should complete the file potential_fields.py and run question2_run.py to show the simulation results. You can set the parameter animation = True in question2_run.py to generate the gif file. In addition, you can change the number of arguments of coefficient in the potential_fields.py by your need.

Note1: we provide the function which calculates the minimum distance between a point and a segment.

Note2: The robot is with omnidirectional wheel for simplicity

Code for question3
There are two files for question3 in the source folder, question3.yaml, question3_run.py. The potential_fields.py that completed in question2 can also be used for this task. You should complete the file question3_run.py to simulate a robot to reach the goal without sticking into a local trap. hint: define the different parameters for the force of repulsive in the two sides of the lines

Note: The robot is with omnidirectional wheel for simplicity