Mobile robots are very often supposed to move around the deployment area and complete certain tasks in specific locations of the area. Such problems where the robot is supposed to navigate to an arbitrary location in the pre-fed map and complete a task based on the tool to be used, is called a coverage problem. Figure 1 shows such a setup where the robot has a map for the environment, a warehouse in this case; and it is supposed to traverse to the area from any random start point and navigate the area according to the tool and task
Mobile robots are the driving force of the warehouses and factories. Such robots are
required to complete tasks ranging from transporting goods ro routine patrolling. All
these tasks are essentially to be completed autonomously with little to no interference of
humans involved. Thus, the robot needs to make decisions in real-time, on its behavior
while also planning to act upon the repercussions of the probabilistic effects that its
current action has on the future. To delineate the previous statement, consider that the
robot knows that it is supposed to be travelling straight to get to a target point and
suddenly meets an obstacle. In such cases, the robot needs to decide if it should
change its path to come back to the originally planned straight path or if shall compute a
new path to the trajectory - depending upon which of them is computationally/otherwise
expensive or not.
In coverage problems, the robot faces similar issues where it is supposed to act
considering the task it is doing, the tool it is using, the start and end locations fed to it
and the utility constraints(time, fuel used, sensing limitations etc.)
This report discusses the complete approach to deduce methodologies for such tasks that has contingency plans and tries to be inclusive of all the possible scenarios that the robot may run into. The further sections of the this document cover Robot Localization techniques, Coverage Area Decomposition techniques, Coverage Area Navigation techniques and finally its implementation on the WayPoint Robotics’ Vector RMP using its technologies and ROS support.
The ability of robot to recognize its current positioning in the map is of utmost
importance as it determines any further action. It is the most important step for any
mobile robot before it does any movement. If the robot fails to localize itself
deterministically, it can try probabilistic techniques with cues from several small motions
to finally determine its global position in the map. Knowledge about current position and
orientation with reference to the global origin helps it to refer to landmarks in the map for
assistance in navigation and task completion. Figure 2 shows the general approach to
mobile robot localization.
The techniques for robot localization include:
Such techniques generally draw a probable distribution of robot positioning on the map and then further reduce it down to deterministic values using sensor and trial movements. All probabilistic techniques depend upon inputs from perception model ( robot’s idiothetic sources tell it about its own motion, proprioception and robot’s allothetic sources tell it about the world - camera, lidar and laser sensors ) and action model (like odometry information and wheel revolution based distance covered - subject to errors accrued over time)
In this technique, the robot starts with an arbitrarily defined probability distribution of the robot’s position on the map over discrete positions and updates these beliefs over time., The prior probabilities are updated using sensor data like encoder values and laser scans and given it the conditions to discard or support probabilities over the locations. The process of keeping track of probabilities over the complete map, for all discretized possible locations, and re-computing probabilities over all of them makes the process computationally expensive but still helpful as it addresses all the possibilities. The perception model is used to individually update all probabilities over the possible locations using Bayes formula. It could work for both, topological as well as grid maps, but given the generality of the algorithm, it tends to be very inefficient.
This method considers the robot’s possible location on the map as a single
Gaussian probability distribution where the mean and covariance values are repeatedly
updated to get to the best representation of its pose. Kalman Localization works on
using the transition model, that is the robot’s action model that updates its likelihood
factors and the plant model that is the perception model that gives another estimate of
the future pose in the map. It combines information from the two to get final localization
values. The prediction step works on movement prior movement information to predict
new locations and then the same is estimated via the sensors that see the environment;
finally culminating the update by fusing the two information.
An example to this could be that the robot starts with a random Gaussian belief of its position, takes a small move, records this to predict new position with the existing Gaussian belief, then uses sensors to view the surroundings (distance from the edges of the maps and relative position to certain landmarks on the map) and estimate its location on the map and finally using the two hypotheses to find a better Gaussian probability representation. This iterative techniques terminates when further updates make the probabilities to converge and the resultant distribution shows where the robot could best be in the map. Very often, the high-class sensing systems help make this distribution deterministic with a very narrow region of uncertainty.
Another category of localization could scenario where the environment is customized for robot navigation. These cases include easy to sense RFID tags on the floor or shelves of a warehouse, visual landmark coding that represents the corresponding position on the map or otherwise. Such practices are fairly simple but obviously not efficient to be duplicated under time and money constraints. Beacon systems, artificial floor markers and GPS based location estimation are also examples of the same. The robot processes such highly informative tags and compares it with a pre-fed look-up table for the same and finds the best match of the location, deterministically on most of the occasion.
Use of visual feed could be incorporated in both of the above techniques but using machine vision explicitly to explore the environment while tracking own motion, building an own version of the map and then trying to fit this generated map to a subset of the global map can also be used. In our approach to solving the coverage problem, localization is the first step that determines robot’s motion. It is followed by methods to reach and decompose the arbitrary coverage area into units and reduce the complexity of the task.
The coverage area for the task could be any arbitrary section, either continuous or
sporadically distributed over the complete map. This makes robot understand the
complexity of the task and reduce it to simpler individually addressable sections.
The complex scenarios for coverage areas could be:
All such special problems of coverage firstly need the robot to have a clear
understanding of the mapping information and then start the decomposition into viable,
individually approachable zones.
The different mapping techniques include:
Based on the mapping used, the robot needs to identify the best decomposition of the coverage area such that each smaller version is individually addressable and it encompasses the complete coverage area without missing zones. Following techniques are proposed to reduce the coverage area into simpler accessible zones.
Rectangular sub-sections with grids of variable granularity for remaining zones of non-standard dimensions - A subclass of Boustrophedon Decomposition
4. Random zones for random motion