Mobile Robot Control 2024 Ultron:Final Report

(Group deadline: 21:00 28 June)

Introduction

High-Level System Description

（Yidan)

System Architecture

State Flow

(Liz)

Data Flow

The data flow diagram illustrates the interactions between input sources, processing modules, and output commands for the robots. There are three major input sources: the laser and odometry come from the robot sensors, and the map is uploaded by the user. The map provides the existing representation of the restaurant environment, including the table positions and order information. The laser detects obstacles in the environment. The odometry supplies information regarding the robot’s position and velocity.

The Localization module first receives the data from all three inputs to determine the robot's position relative to the map. Over time, the robot's theoretical and actual positions will eventually diverge because of accumulated errors from odometry conversions between theoretical and real movement. Therefore, it’s fundamental to ensure that the robot knows its exact position within the environment during its movement for accurate navigation. The position is then forward to both Global Navigation and Local Navigation modules.

Global Navigation uses the map to plan an optimal path from the robot’s current position to the destination through the combination of PRM and A* algorithms and updates the current target point for Local Navigation. Local Navigation uses the current position, the target point, and the laser data to adjust the robot's path dynamically, avoiding obstacles and navigating through tight spaces. It provides control commands for linear and angle velocity to guide the robot's motion.

The Stuck Detection module handles abnormal status during the local navigation. If the robot is within 0.2 meters of an obstacle according to laser data or cannot find a path to reach its target points due to an unknown static obstacle or being stuck in a local minimum, the stuck detection module is triggered. It stopped the robot and initiated a path re-planning through the Global Navigation module.

The process involves a continuous feedback loop, constantly monitoring and adjusting the robot's movements based on inputs and environments to ensure safe and efficient navigation towards its destination.

System components

Initialization

(Lu)

Localization

（Aori）

Global Navigation

（Yidan, Hao）

Local Navigation

Dynamic Window Approach

Given a target point by global navigation algorithm and positions of obstacle by laser sensor, local navigation aims to calculate an available path to avoid collision with the obstacles as well as to move towards the target point. The outputs of all local navigation algorithms shown in lectures are directly the desired velocities [math]\displaystyle{ (v, \omega) }[/math] ([math]\displaystyle{ v }[/math] is linear and [math]\displaystyle{ \omega }[/math] is angular velocity) of the robot. There is no need to perform a path following task.

In exercising, we implemented both Artificial Potential Filed (APF) Algorithm and Dynamic Window Approach (DWA). At first, we chose APF as our local navigation algorithm because of its robustness and simplicity. However, during the test we found that APF is not ideal for vertex handling and the robot will oscillate when driving through a corridor, whereas DWA can generate smoother trajectories and shows great potential for dealing with complex scenarios. And DWA is designed to deal with the constraints imposed by limited velocities and accelerations, because it is derived from the motion dynamics of the mobile robot. This is well suited to the realities of Hero. Moreover, DWA contains the idea of optimal control, which can improve the efficiency of the robot. Thus, we switched to DWA.

Distance between obstacles and trajectory

DWA performs reactive collision avoidance. When computing the next moving command, DWA considers only a short time interval, within which the robot's velocities [math]\displaystyle{ (v, \omega) }[/math] are considered to be constants. The algorithm will evaluate the results of all possible velocities and select the optimal one. Instead of using the simplified version introduced in lectures, we use the original version of this algorithm proposed by D. Fox, etc. The main difference is that the original algorithm uses a more refined geometric model when calculating the distance between the obstacle and the predicted trajectory, taking both the trajectory and the robot size into account. As shown in figure on the right, the obstacles that are not on the trajectory will be ignored.

Parameter Tuning

Simulation Results

Test on Hero

Interaction

(Lu)

Final Challenge Results

Results Analysis

（Hao）

Future Improvement

（Nan）