Mobile Robot Control 2020 Group 10
Team members:
Arend-Jan van Noorden (1378147)
Sjors Boutkan (0938234)
Jeroen van Meurs (0946114)
Koen van Gorkom (0954964)
Friso Buurman (0906665)
Escape Room
Design Document
The design document of group 10 can be found here.
Strategy
Solving the escape room is quite simple: find the corridor and drive through it. This document givesan overview of the system being build to solve this task.Finding the corridor is done by detecting the two outside corners marking the start of the corridor.Since the room is square, no other outside corners will be present in the environment. If these cornerscan’t be found from PICO’s initial location, it’ll follow the walls of the rooms clockwise, until thecorridor (outside corners) are encountered. The corresponding state diagram is shown in Figure1.1. The movement and navigation will be done using set-points and a potential-field algorithmpreventing PICO from hitting any obstacles or walls.
Requirements and Specifications
| Requirements | Specifications |
|---|---|
| PICO should not bump into walls | PICO's measurements are 0.41 m in width by 0.35 m in length |
| The free radius around PICO should be at least 0.21 m | |
| PICO cannot exceed speed limits | Maximum translational velocity is 0.5 m/s |
| Maximum rotational velocity is 1.2 rad/s | |
| Since PICO will be autonomous, it should be aware of its surroundings. | PICO should have an accurate map of its surroundings. |
| LRF range is between 0.01 m and 10 m. | |
| LRF FOV is from -2 to 2 rad, measured over 1000 points of equal increments. | |
| PICO should terminate when the final objective is reached. | PICO should come to a stop after it has crossed the finish line. |
| PICO should reach the final objective as soon as possible. | PICO should cross the finish line within 5 minutes. |
| PICO should not end up in a live lock state. | PICO should not keep repeating one action for over 30 seconds. |
| PICO should not end up in a dead lock state. | Careful coding has to be done to have no dead lock states. |
Components and Functions
Basic architecture of robot code:
All functions need to be run based on a Finite State Machine. As with sensors, all data has noise so these data streams need to be filtered.
The tasks of each component can be divided in several functions each with a specific subroutine.
World model
The world model stores the information gathered by the components and distributes this information to the other components. The information consists of:
- A map of the surrounding area.
- A list of found features.
- The current and previous state.
- The next position to travel to.
The world model contains no functions because the world model is only used to store the world data, so there are no active subroutines present.
Plan
The plan component uses data from the world model to determine the next position PICO needs to go to. It thereby also check if the objective is met.
- checkObjective()
The checkObjective function is the supervisor of the complete system. It controls/executes the states as defined in figure \ref{fig:state-machine} and executes the strategy as described in \ref{sec:strategy}. It will also check for failure of the system by identifying incorrect states, e.g. not moving for a set amount of time, following a wall for too long or having made a complete loop of the room without finding the corridor.
- pathPlanner()
The plan component contains the path-planner function, this functions is responsible to define the setpoints for PICO. These setpoints will be determined based on the state machine and the world model.
Control
The control component makes sure PICO moves along the planned path.
- moveToPoint()
The move to point function will be used to move PICO to the set point generated by the path planner. To prevent that PICO will hit the wall, the world model will be used to determine the trajectory from the current position of PICO to the set point.
Monitoring
The monitor component will try to identify features from the world model data.
- featureDetection()
To determine the corners and walls of the maze the feature detection function will be used based on the world model data.
Perception
The perception component will gather the sensor data, clean it and update the world model with it.
- getLaserData()
To acquire the data points of the laser sensor, the get laser data function will be used. The gathered data will be preprocessed to filter out the disturbances. The processed data will be written to the world model.
- getOdomData()
The odometry date will be read out with the getOdomData function. This date will be written to the world model.
- SLAM()
The gathered data from the laser and odometry sensors will be used to generate a map of the environment using Simultaneous Localization and Mapping (SLAM)
- UpdateVisuals()
A window will be opened that shows all the detected features and the path that PICO is going. This function will update that window
Interfaces
All components interface with the world model, getting data required for their functions and setting data created to be used by other components. The world mode is the central data store. This can also be seen in figure Figure Boven. Differences between the the proposed system and the system originally shown in [1], is that there are no interfaces between the 'resources' and 'capabilities' and the world model. The world model will not be able to get it's own data or control the motors of the system. The interface to the sensors will be through the perception component and the controlling of capabilities will be done by the control component.
Challenge result
Sadly we did not manage to get out of the escape room, below we will discuss why and how we solved it.
Main
After the Maze challenge we analyzed which part of the main program caused the failure which lead to the the wall collision. The problem occurred in the corner detection function, which resulted in the interpretation of a inside corner as an outside corner. The planning algorithm used this corner as target position to search a second outside corner, and let the robot drive to this corner. With the absence of a potential field algorithm, the robot was able to drive through the wall.
The main program for the maze challenge is using the state machine as shown in the Components and functions paragraph, in the following paragraphs is the robot behavior at each state described.
- ORIENTING
In the first state the robot will turn to check if it can see a door or a outside corner. A door is defined if there are two outside corners within 0,5 m and 1,5 m from each other, or a outside corner and a wall within the same range as with the outside corners. If the robot is not able to find a door from its initial position, then there will be searched for a single outside corner. When there is no door or outside corner found then the robot will search for the closest wall to follow.
- SEARCH_FORWARD
If the robot cannot detect any features in the orienting state, then the robot will drive straight forward in the same direction as the initial orientation.
- ALIGN_TO_WALL
When multiple walls are detected the closest wall is determined, the angle between the wall and the robot will be computed to determine the rotation direction. When the robot is aligned within +/- 3 degrees the state will be set to FOLLOW_WALL.
- FOLLOW_WALL
The robot will follow the wall at 20 cm distance till a door or outside corner is found, if this is the case the state will be set to GOTO_DOOR or SEARCH_DOOR. Otherwise the robot will follow the wall if a second wall in front of the is found, when this second wall is closer by then the first wall the ALIGN_TO_WALL state will be called again.
- SEARCH_DOOR
If one outside corner is found a way point will be set 25 cm from the x and y direction with respect of the corner, with this way point there can be guaranteed that the robot will not collide with the wall. When the robot is at this position and the second outside is still not found it will to ORIENTING again, otherwise the robot will be set GOTO_DOOR.
- GOTO_DOOR
When the door is found the planning script will place a way point 25 cm in front of the midpoint of the door, if the robot is at this location the robot will be set to the DRIVE_THROUGH_DOOR state.
- DRIVE_THROUGH_DOOR
In this state the robot will drive to the way point 25 cm in the middle the door, when the robot loses the door the DRIVE_THROUGH_CORRIDOR state is set.
- DRIVE_THROUGH_CORRIDOR
To complete the maze a way point is set in the middle of the furthest corridor walls nodes. To determine the corridor walls there is checked if the walls are parallel to each other, if the perpendicular distance is between the 0,5 m and 1,5 m and if at least two wall nodes can be projected on the other wall to check if the walls are not after each other. When the robot is at this way point the robot has exited the maze and the state will be set to COMPLETED. Below there is shown that the robot is executing SEARCH_DOOR, GOTO_DOOR and DRIVE_THROUGH_CORRIDOR.
Backup
The backup was a simple wall following algorithm. Initially, Pico will drive forwards until it encounters an object in front. Next, Pico will turn 90 degrees to the right. Then, as long as Pico's left side is obstructed, the wall will be followed while making adjustments to align with the wall and not strafe too close to the wall or too far away from the wall. Again, when the front is obstructed as well, Pico will turn 90 degrees to the right and the next wall will be followed. When a gap at the left side is detected, Pico will move forward a little bit, turn left and drive forward into the corridor. If the senses a object in front, Pico will know the opening was only a small gap and not the corridor, in this case Pico will rotate right and continue following the wall.
Unfortunately, the backup attempt failed as well. The problem Pico encountered during the challenge was an edge case which unfortunately did not result in a correct execution of the algorithm. After finding a wall at the front, Pico turned 90 degrees to the right. After the turn all sides were 'free', meaning that Pico thought he was next to a corridor. After turning left it noticed that its front was free and its left side was not. This triggered the state machine to believe it was in the corridor. When the state machine enters this state, the only possible state is the finished state. So after driving through the wall, all sides of Pico were 'free' again thus entering the finished state. Pico drove through the when it thought it was in the corridor. This is because of the specific laser beams that Pico uses for the collision detection. Pico checks three sides, using ten laser beams per side. The front side is the actual front of Pico. The right and left side however, are about 45 degrees from the front instead of 90. Since the actual side of Pico was not monitored, Pico thought he was still some distance away from the wall.
After the challenge a little time was spend to improve the behavior of the wall following algorithm. To this end, the hard 90 degree turns have been replaced by an alignment check and the collision detection was changed that the actual right and left side of Pico are measured. When an obstructing in front is detected, Pico rotates until the obstruction is not visible anymore and the left side is aligned to a wall. This is checked by comparing the ranges of a subset of the of the LRF to the left of Pico. When the ranges are about equal, Pico is aligned. With this improvement, Pico was able to reach the actual finish of the Maze Challenge.
Hospital challenge
Strategy [Sjors]
Components [Friso]
World model [Koen]
The world model is used as a database. This database is separated into two maps: A global map and a grid map. All gathered data is stored in the world model and then can be used by other processes. Besides the data structure the world model also has a couple of functions to update the model and extract data from it. The main function is "initGlobalMap", this is the initialization of the global map. Then the function "addWallToGridMap" is used to update the gridmap with all the statics walls. These functions will be described further below. Next to those main functions there are some smaller functions to update or extract odometry data,laser data, robot position and more.
initGlobalMap This function loads a modified json and turns into into a global map as seen in picture **. Th json file consists of nodes, walls, cabinets and waypoints in front of the cabinets. Every item in this list will be visualized, this is done to make debugging easier. This global map is used as a reference base of the gridmap and used to make a clearer visualization.
addWallToGridMap This function is used to create the basic gridmap. It will use the walls that are supplied by the global map to fill in the gridmap
Planning [Jeroen]
Potential field
Path Planning
The path planning of the robot was previously based upon manual waypoints which were set in the .jason map. The path is determined based upon the order of the cabinets. Each waypoint is placed at key positions in the map, to ensure the robot can travel whererver it needs to travel.
The Dijkstra algorithm is used in order to plan a path trough the waypoints. This algorithm ensures that the best path is chosen. The distance between the waypoints is used as the costs to travel to the waypoints.
During its trajectory, the robot can make links between waypoints invalid if the time the robot takes to travel exceeds the pre-calculated travel time. Exceeding the travel time would mean that the potential field prevents the robot from traveling to the waypoint, therefore blocking the path. Once the blocked link is made invalid, a new path is determined which works its way around said blocked link.
Control
Monitoring
Clutter detection
Localization [Friso]
The Monte Carlo Particle Filter is used in order to calculate the position of the robot within the map. By means of a particle filter, different states of the robot are determined, and then checked for their likeliness. This process is visualized below. The green dots represent all possible positions which are considered. Initially this is a large cloud of particles. This results in a high uncertainty of the position of the robot. The LRF data and the odometry data are used in order to track all positions. By checking all particles and the surroundings, the unlikely positions are eliminated, and finally the position converges to the actual position of the robot.
The localization continues to run, in order to keep the uncertainty of the position of the robot as low as possible.
