Individual wiki page - collision avoidance: Difference between revisions

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= Planning =


== Week 5 ==
Making individual planning
Task environment description
Finalising user requirements through proxemics + privacy issues (partially completed already)
Investigating if dynamic window approach is still viable, if so try to understand how it works and how it can possibly be extended for this application
== Week 6 ==
Looking at viability of Social Force Model and SLAM, are they a better than DWA?
Identifying ways to combine these approaches
(Finding a way to distinguish between humans and static objects)
== Week 7 ==
Describing the ‘best’ collision avoidance procedure
(Performing simulations with this procedure?)
Discussion of benefits and disadvantages for this approach
Topics for further research
Working on final presentation
=Topic=
User-centred design of a collision avoidance procedure for robots in supermarket environments
=Introduction & Problem statement=
Robot navigation and collision avoidance in crowded and dynamic environments is a challenging problem, not only from a technical point of view, but also when looking at how robots should behave in the proximity of (large numbers of) people.
This research will focus on finding a solution for robot collision avoidance in a supermarket environment.  A supermarket environment has aspects that make it unique from other crowded environments. To make this more concise, a description of this environment is given with advantages and difficulties for designing a robot collision avoidance. Furthermore, it will also become clear that users (staff & customers) will have certain requirements that relate to human robot interactions (HRI).  Keeping both the environment and customer requirements in mind, state-of-the art collision avoidance procedures will be assessed on application in a supermarket environment and possible additions to enhance them for this application will be investigated. A simulation with a candidate object avoidance procedure will be done to test its working potential. Finally, advantages and disadvantages for this candidate procedure are given and topics for further research will be presented.
=Task environment description of a supermarket=
We will look at advantages and difficulties for robot collision avoidance in supermarkets.
==Advantages for collision avoidance==
1. It is assumed that there are several (security) cameras already mounted on the ceiling and that the robot already possesses an omnidirectional camera.  By giving the robot access to ceiling mounted cameras, these can be used for collision avoidance as extra sensory input on top of the camera already present on the robot itself. This gives the robot a top down view of the area he is in, filling in blank spots in the robot’s local sensing. This poses several questions; for one, security cameras usually make use of fish-eye cameras giving a distorted view of the environment, meaning that these images might need to be processed or not usable at all.  Then also, how many extra ceiling cameras would be necessary and how much would that cost? Takaaki Sato et al. [BRON]  have proved that fish eye cameras can be used to make a (2D) bird’s eye view of an environment to eliminate blind spots in a robot’s local sensing.
2. Supermarket aisles have a static layout, with each aisle having distinct retail products ordered in a known layout. This can be used for robot localisation but has no real impact on collision avoidance.
==Difficulties for collision avoidance==
1. Customers and staff members will be walking around supermarkets, either in groups or alone, maybe carrying a shopping cart. All these people need to be avoided in a way that is perceived as safe by them.
2. There are also peak times in number of customers walking around (e.g. on Saturdays). Collision avoidance procedures on their own might then lead to the robot having no way to avoid masses of people. Collision avoidance might need to be adapted here, so that a visible or hearable cue is added that alerts surrounding customers in a comfortable way. It should be investigated what kind of cues are desirable in these situations.
3. (Parked) shopping carts are present, which are objects that can move but not necessarily. For a parked shopping cart case, there should be some prediction about probability that it will move and in what direction. This probability should be depended on whether a human is close to that cart. These probabilities might be implemented in a cost function for shopping carts specifically. This can however lead to computationally heavy predictions and calculations, so this will need to be investigated
4. Miscellaneous items such as boxes, pallets or retail products fallen from shelves might be present as obstacles. Since these objects are static, no prediction of movement is necessary and avoiding it is rather straightforward with already existing collision avoidance procedures.
=Identifying user requirements=
==Proxemics for HRI during avoidances==
==Possible privacy issues when sensing humans==
==Describing cost functions==
=Distinguishing between humans and objects=
==Further distinguishing between possibly moving objects and static objects==
==Describing motion prediction models==

Latest revision as of 13:59, 14 October 2018

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