We consider the problem of exploring an unknown environment with a pair of mobile robots. The goal is to make the robots meet (or rendezvous) in minimum time such that there is a maximum speed gain of the exploration task. The key challenge in achieving this goal is to rendezvous with the least possible dependency on communication. This single constraint involves several sub-problems: finding unique potential rendezvous locations in the environment, ranking these locations based on their uniqueness and synchronizing with the other robot to meet at one of the locations at a scheduled time. In addition, these tasks are to be performed simultaneously while exploring and mapping the environment. We propose an approach for efficiently combining the exploration and rendezvous tasks by considering the cost of reaching a rendezvous location and the reward of its uniqueness. This cost and reward model is combined with a set of deterministic and probabilistic rendezvous strategies for the robots to meet during exploration.
We address the problem of rendezvous between two agents in urban street networks. Specifically, we consider the case where the agents have variable speeds and they need to schedule a rendezvous under uncertainty in their travel times. Examples of such a scenario range from everyday life where two people would like to coordinate a meeting while going from office to home; to a futuristic case where automated taxis would like to meet each other for load balancing passengers. The scheduling for such scenarios can easily become challenging with uncertainties such as delayed departures, road blocks due to construction or traffic congestion. Any solution for such a task is required to minimize the waiting time and the planning overhead. We propose an algorithm that optimizes the total travel time and the waiting time for two agents to complete their respective paths from start to rendezvous and from rendezvous to goal locations subject to delays along their paths. We validate our approach with a street network database which has a cost associated with every query made to the database server. Thus our algorithm intelligently optimizes for rendezvous trajectories that effectively mitigate the scourge of traffic delays, while simultaneously limiting the number of queries through careful analysis of the informative value of each potential query.
This project addresses the problem of searching multiple non-adversarial targets using a mobile searcher in an obstacle-free environment. In practice, we are particularly interested in marine applications where the targets drift on the ocean surface. These targets can be surface sensors used for marine environmental monitoring, drifting debris, or lost divers in open water. Searching for a floating target requires prior knowledge about the search region and an estimate of the target’s motion. This task becomes challenging when searching for multiple targets where persistent searching for one of the targets can result in the loss of other targets. Hence, the searcher needs to trade-off between guaranteed and fast searches. We propose three classes of search strategies for addressing the multi-target search problem. These include, data-independent, probabilistic and hybrid search. The data-independent search strategy follow a pre-defined search pattern and schedule. The probabilistic search strategy is guided by the estimated probability distribution of the search target. The hybrid strategy combines data-independent search patterns with a probabilistic search schedule. We evaluate these search strategies in simulation and compare their performance characteristics in the context of searching multiple drifting targets using an Autonomous Surface Vehicle (ASV) and an Autonomous Underwater Vehicle.
A cross-platform smart phone application was developed to provide optimal meeting points for humans and/or robot teams. Some of the various use cases for finding optimal meeting points are: social rendezvous, ride-sharing, package delivery and logistic management. Recently, it has also found a useful application in multi-modal fleet management system where the passengers need to be transported from first mile to last mile of their journey using different modes of transportion (including, public and private).
We designed floating sensors called drifters which can take measurements of water surface properties, log data internally and communicate with other nearby surface agents. Each drifter is equipped with a miniPC (Android MK-802), a GPS receiver (Adafruit) and a 5V/2A battery pack. The miniPC is capable of storing on-board data (e.g. wind direction) and communicating with the searcher (Autonomous Surface Vehicle) via its in-built WiFi antenna. The drifters are designed to be neutrally buoyant such that they float upright for data collection, receive communication and GPS signals with minimal interference.
A real-time tracking tool for surface agents was required for our marine field trials, given the large scale of the experiments involving drifting sensor targets, underwater and surface vehicles. A web-interface was developed in collaboration with the members of Mobile Robotics Lab at McGill University to visualize the real-time and simulated track paths of the agents during the field experiments and simulated trials, respectively. The web-interface can cache map tiles which allows us to use the interface even without an internet connection. It can be easily integrated with any robotic platform using the ROS-bridge interface.