The problem of coordination without a priori information about the environment is important in robotics. Applications vary from formation control to search and rescue. This paper considers the problem of search by a group of solitary robots: self-interested robots without a priori knowledge about each other, and with restricted communication capacity. When the capacity of robots to communicate is limited, they may obliviously search in overlapping regions (i.e. be subject to interference). Interference hinders robot progress, and strategies have been proposed in the literature to mitigate interference [1], [2]. Interaction of solitary robots has attracted much interest in robotics, but the problem of mitigating interference when time for search is limited remains an important area of research. We propose a coordination strategy based on the method of cellular decomposition [3] where we employ the concept of soft obstacles: a robot considers cells assigned to other robots as obstacles. The performance of the proposed strategy is demonstrated by means of simulation experiments. Simulations indicate the utility of the strategy in situations where a known upper bound on the search time precludes search of the entire environment.
Another solution in the literature which mitigates interruptibility has robots plan to meet other robots before the rendezvous time arrives @cite_2 . Two challenging aspects need to be handled in this case: forecasting of the current positions of the robots involved; and forecasting of the paths taken by the robots from their current positions. To address these challenges, the method proposed by Hourani is based on serendipity @cite_2 . Usually, when robots interrupt exploration to attend a rendezvous, new information might not be gained. To address this negative impact, some robots, which are referred to as serendip robots, plan to interact with other members before the rendezvous time arrives by forecasting their paths. Thus interruptibility is mitigated.
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Dependence on a team of robots to carry out the exploration missions has many benefits such as speeding up the missions; provided that the coordination among robots is maintained. Many circumstances can limit the communication, which is crucial for the coordination among robots (e.g. impenetrable barriers, high temperature etc.). A periodic rendezvous strategy is considered in this paper as a work around in order to overlap communication ranges of the robots. Attending these periodic rendezvous sessions requires that the robots interrupt their current exploration progress periodically and traverse back to the rendezvous points (i.e. Interruptibility). During their trips to these points, they do not gain new knowledge since they cross already explored parts of the area. Therefore, using rendezvous strategies improves the team behavior but has a negative impact on the time efficiency. The contribution of this paper is to mitigate this negative impact of Interruptibility on explorations while maintaining the coordination among robots. The mitigation algorithm is evaluated on several graphs and its performance is compared with other rendezvous approaches where the results are promising. Two mobile agents (robots) have to meet in an a priori unknown bounded terrain modeled as a polygon, possibly with polygonal obstacles. Robots are modeled as points, and each of them is equipped with a compass. Compasses of robots may be incoherent. Robots construct their routes, but the actual walk of each robot is decided by the adversary that may, e.g., speed up or slow down the robot. We consider several scenarios, depending on three factors: (1) obstacles in the terrain are present, or not, (2) compasses of both robots agree, or not, (3) robots have or do not have a map of the terrain with their positions marked. The cost of a rendezvous algorithm is the worst-case sum of lengths of the robots' trajectories until they meet. For each scenario, we design a deterministic rendezvous algorithm and analyze its cost. We also prove lower bounds on the cost of any deterministic rendezvous algorithm in each case. For all scenarios these bounds are tight. For many robotics applications (such as robotic search and rescue), information about the environment must be gathered by a team of robots and returned to a single, specific location. Coordination of robots and sharing of information is vital, and when environments have severe communication limitations, approaches must be robust to communication drop-out and failure. The difficulties are compounded in dynamic environments, where paths previously believed to be free can suddenly become blocked. In this paper, we introduce a novel way of calculating rendezvous points for robots to meet and share information. Using role-based exploration, some robots continuously explore the environment while others ferry information back and forth to a central command centre. Optimal rendezvous point selection leads to more efficient exploration, and allows robots to replan when one of them has unexpected obstacles in its path. Indoor autonomous mobile service robots can overcome their hardware and potential algorithmic limitations by asking humans for help. In this work, we focus on mobile robots that need human assistance at specific spatially-situated locations (e.g., to push buttons in an elevator or to make coffee in the kitchen). We address the problem of what the robot should do when there are no humans present at such help locations. As the robots are mobile, we argue that they should plan to proactively seek help and travel to offices or occupied locations to bring people to the help locations. Such planning involves many trade-offs, including the wait time at the help location before seeking help, and the time and potential interruption to find and displace someone in an office. In order to choose appropriate parameters to represent such decisions, we first conduct a survey to understand potential helpers' travel preferences in terms of distance, interruptibility, and frequency of providing help. We then use these results to contribute a decision-theoretic algorithm to evaluate the possible choices in offices and plan where to proactively seek help. We demonstrate that our algorithm aims to minimize the number of office interruptions as well as task completion time.