Reference number : GC-2017-Q1-03187, amendment number : , agreement type : G, recipient legal name : Vardy, Andrew (Memorial University of Newfoundland), recipient country : CA, recipient province : nan, recipient city : Newfoundland and Labrador|Terre-Neuve-et-Labrador, recipient postal code : nan, program name english : Discovery Grants Program - Individual, program name french : Programme de subventions à la découverte - individuelles, program purpose english : It is often stated that robots are best suited for dirty, dull, and dangerous jobs. This research program concerns the development of techniques that would allow a swarm of robots to organize their environment, a task that can easily be described as dirty, dull, and potentially dangerous. We focus on the problems of object clustering and sorting. In object clustering, there is only one type of of object in the environment and the goal is to gather all objects into one cluster. In object sorting, there are multiple types of objects and the goal is to form homogeneous clusters, ideally one for each type. This work falls under the domain of swarm robotics which is an approach to the design of multi-robot systems where the robots are constrained to sense and act locally. In a swarm robotic system, no single robot is in charge which also means that the failure of any single robot does not lead to the failure of the system. Previous approaches to object clustering and sorting only handle uniformly shaped objects which are easily identifiable. We are interested in addressing real-world challenges such as arbitrarily shaped objects which are not readily identifiable. Another real-world challenge is dealing with congestion where the robots become overcrowded and unproductive. Dealing with human users and using their feedback to modify the behaviour of a swarm is another significant real-world challenge that we will address. 
 
Our approach is innovative and practical in that we focus on the capabilities of inexpensive robots which can perform these tasks as a collective. Robots with the desired capabilities can be constructed with current off-the-shelf technology for less than $500. The key capabilities for what we term low-cost navigation include visual homing, the ability to return to previously visited places using vision, and odometry, the ability to estimate movement over short distances. We will exploit strategies for navigation to accelerate task performance by having robots move deliberately to previously visited places, as opposed to the currently popular strategy of randomized motion. 
 
The introduction of low-cost navigation capability into swarm robotics will help to push the field towards practical application. We envision robots cleaning homes, gathering garden waste, sorting recyclables, and bringing order to chaotic work environments. These tasks may be viewed as mundane, but that is exactly why we are interested in automating them. They are also ubiquitous. Therefore, workable solutions have the potential for a major societal impact and the development of such solutions has the capacity to generate significant economic activity and jobs. The HQP trained in this program will have the unique opportunity to exploit the technology from this research program to either create start-up companies or develop careers in the growing robotics industry., program purpose french : It is often stated that robots are best suited for dirty, dull, and dangerous jobs. This research program concerns the development of techniques that would allow a swarm of robots to organize their environment, a task that can easily be described as dirty, dull, and potentially dangerous. We focus on the problems of object clustering and sorting. In object clustering, there is only one type of of object in the environment and the goal is to gather all objects into one cluster. In object sorting, there are multiple types of objects and the goal is to form homogeneous clusters, ideally one for each type. This work falls under the domain of swarm robotics which is an approach to the design of multi-robot systems where the robots are constrained to sense and act locally. In a swarm robotic system, no single robot is in charge which also means that the failure of any single robot does not lead to the failure of the system. Previous approaches to object clustering and sorting only handle uniformly shaped objects which are easily identifiable. We are interested in addressing real-world challenges such as arbitrarily shaped objects which are not readily identifiable. Another real-world challenge is dealing with congestion where the robots become overcrowded and unproductive. Dealing with human users and using their feedback to modify the behaviour of a swarm is another significant real-world challenge that we will address. 
 
Our approach is innovative and practical in that we focus on the capabilities of inexpensive robots which can perform these tasks as a collective. Robots with the desired capabilities can be constructed with current off-the-shelf technology for less than $500. The key capabilities for what we term low-cost navigation include visual homing, the ability to return to previously visited places using vision, and odometry, the ability to estimate movement over short distances. We will exploit strategies for navigation to accelerate task performance by having robots move deliberately to previously visited places, as opposed to the currently popular strategy of randomized motion. 
 
The introduction of low-cost navigation capability into swarm robotics will help to push the field towards practical application. We envision robots cleaning homes, gathering garden waste, sorting recyclables, and bringing order to chaotic work environments. These tasks may be viewed as mundane, but that is exactly why we are interested in automating them. They are also ubiquitous. Therefore, workable solutions have the potential for a major societal impact and the development of such solutions has the capacity to generate significant economic activity and jobs. The HQP trained in this program will have the unique opportunity to exploit the technology from this research program to either create start-up companies or develop careers in the growing robotics industry., agreement title english : Robots Organizing Environments: Collective Strategies based on Low-Cost Navigation, agreement title french : Robots Organizing Environments: Collective Strategies based on Low-Cost Navigation, agreement number : RGPIN, agreement value : 100000.0, agreement start date : 2017-05-10, agreement end date : nan, description english : nan, description french : nan, expected results english : nan, expected results french : nan, additional information english : Grant or Award spanning more than one fiscal year. (2017-2018 to 2022-2023), additional information french : Subvention ou bourse octroyée s'appliquant à plus d'un exercice financier. (2017-2018 à 2022-2023), owner organization : nserc-crsng, owner organization title : Natural Sciences and Engineering Research Council of Canada | Conseil de recherches en sciences naturelles et en génie du Canada