Efficient Multi-Robot Coverage of a Known Environment

This paper addresses the complete area coverage problem of a known environment by multiple-robots. Complete area coverage is the problem of moving an end-effector over all available space while avoiding existing obstacles. In such tasks, using multiple robots can increase the efficiency of the area coverage in terms of minimizing the operational time and increase the robustness in the face of robot attrition. Unfortunately, the problem of finding an optimal solution for such an area coverage problem with multiple robots is known to be NP-complete. In this paper we present two approximation heuristics for solving the multi-robot coverage problem. The first solution presented is a direct extension of an efficient single robot area coverage algorithm, based on an exact cellular decomposition. The second algorithm is a greedy approach that divides the area into equal regions and applies an efficient single-robot coverage algorithm to each region. We present experimental results for two algorithms. Results indicate that our approaches provide good coverage distribution between robots and minimize the workload per robot, meanwhile ensuring complete coverage of the area.Comment: In proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 201

Providing Predictable Performance during Network Contingencies

In IP backbone networks, packets may get dropped due to: i) lack of viable next hops when a link/router fails, ii) forwarding loops during network convergence, and iii) buffer overflows in case of congestion. Similarly, packets may be lost in wireless networks due to variations in signal strength between a pair of mobile nodes. This dissertation explores the possibility of providing a predictable performance during such network contingencies in wired backbone networks and robotic wireless networks. First, we study the feasibility of developing a combination of local reroute and global update mechanisms that can achieve loop-free convergence, while performing disruption-free forwarding around a failed link/router, without carrying any additional information in the IP datagrams and with out needing any coordination between routers. We show that order of updates rarely matters for loop-free convergence when failure inference based fast reroute (FIFR) scheme with interface-specific forwarding is employed for dealing with link or router failures. In the rare cases where order matters, it can be coupled with progressive link metric increments to ensure loop-freedom with unordered updates of forwarding tables. We also demonstrate that, apart from providing protection against failures, FIFR can also be utilized to mitigate packet drops due to network congestion caused by micro traffic bursts. Second, we address the problem of constructing a communication map, which encodes information on whether two robots at given locations can communicate using a wireless network. Unlike previous offline approaches that do not utilize data measured by robots, we propose an online method, utilizing Gaussian Processes, to efficiently build a communication map with multiple robots, by exploiting prior communication models that can be derived from the physical map of the environment. Our evaluation, using a team of TurtleBot 2 platforms, confirms that the proposed method requires robots to take fewer signal strength measurements and travel less distance, and yet obtain similar accuracy as methods that consider all the locations in the environment

Planning Algorithms for Multi-Robot Active Perception

A fundamental task of robotic systems is to use on-board sensors and perception algorithms to understand high-level semantic properties of an environment. These semantic properties may include a map of the environment, the presence of objects, or the parameters of a dynamic field. Observations are highly viewpoint dependent and, thus, the performance of perception algorithms can be improved by planning the motion of the robots to obtain high-value observations. This motivates the problem of active perception, where the goal is to plan the motion of robots to improve perception performance. This fundamental problem is central to many robotics applications, including environmental monitoring, planetary exploration, and precision agriculture. The core contribution of this thesis is a suite of planning algorithms for multi-robot active perception. These algorithms are designed to improve system-level performance on many fronts: online and anytime planning, addressing uncertainty, optimising over a long time horizon, decentralised coordination, robustness to unreliable communication, predicting plans of other agents, and exploiting characteristics of perception models. We first propose the decentralised Monte Carlo tree search algorithm as a generally-applicable, decentralised algorithm for multi-robot planning. We then present a self-organising map algorithm designed to find paths that maximally observe points of interest. Finally, we consider the problem of mission monitoring, where a team of robots monitor the progress of a robotic mission. A spatiotemporal optimal stopping algorithm is proposed and a generalisation for decentralised monitoring. Experimental results are presented for a range of scenarios, such as marine operations and object recognition. Our analytical and empirical results demonstrate theoretically-interesting and practically-relevant properties that support the use of the approaches in practice