A Constant-Factor Approximation Algorithm for Online Coverage Path Planning with Energy Constraint

In this paper, we study the problem of coverage planning by a mobile robot with a limited energy budget. The objective of the robot is to cover every point in the environment while minimizing the traveled path length. The environment is initially unknown to the robot. Therefore, it needs to avoid the obstacles in the environment on-the-fly during the exploration. As the robot has a specific energy budget, it might not be able to cover the complete environment in one traversal. Instead, it will need to visit a static charging station periodically in order to recharge its energy. To solve the stated problem, we propose a budgeted depth-first search (DFS)-based exploration strategy that helps the robot to cover any unknown planar environment while bounding the maximum path length to a constant-factor of the shortest-possible path length. Our $O(1)$-approximation guarantee advances the state-of-the-art of log-approximation for this problem. Simulation results show that our proposed algorithm outperforms the current state-of-the-art algorithm both in terms of the traveled path length and run time in all the tested environments with concave and convex obstacles

Energy-Aware Ergodic Search: Continuous Exploration for Multi-Agent Systems with Battery Constraints

Continuous exploration without interruption is important in scenarios such as search and rescue and precision agriculture, where consistent presence is needed to detect events over large areas. Ergodic search already derives continuous trajectories in these scenarios so that a robot spends more time in areas with high information density. However, existing literature on ergodic search does not consider the robot's energy constraints, limiting how long a robot can explore. In fact, if the robots are battery-powered, it is physically not possible to continuously explore on a single battery charge. Our paper tackles this challenge, integrating ergodic search methods with energy-aware coverage. We trade off battery usage and coverage quality, maintaining uninterrupted exploration by at least one agent. Our approach derives an abstract battery model for future state-of-charge estimation and extends canonical ergodic search to ergodic search under battery constraints. Empirical data from simulations and real-world experiments demonstrate the effectiveness of our energy-aware ergodic search, which ensures continuous exploration and guarantees spatial coverage.Comment: 7 pages, 7 figures, ICRA'2

Coordinated Sensor-Based Area Coverage and Cooperative Localization of a Heterogeneous Fleet of Autonomous Surface Vessels (ASVs)

Sensor coverage with fleets of robots is a complex task requiring solutions to localization, communication, navigation and basic sensor coverage. Sensor coverage of large areas is a problem that occurs in a variety of different environments from terrestrial to aerial to aquatic. In this thesis we consider the aquatic version of the problem. Given a known aquatic environment and collection of aquatic surface vehicles with known kinematic and dynamic constraints, how can a fleet of vehicles be deployed to provide sensor coverage of the surface of the body of water? Rather than considering this problem in general, in this work we consider the problem given a specific fleet consisting of one very well equipped robot aided by a number of smaller, less well equipped devices that must operate in close proximity to the main robot. A boustrophedon decomposition algorithm is developed that incorporates the motion, sensing and communication constraints imposed by the autonomous fleet. Solving the coverage problem leads to a localization/communication problem. A critical problem for a group of autonomous vehicles is ensuring that the collection operates within a common reference frame. Here we consider the problem of localizing a heterogenous collection of aquatic surface vessels within a global reference frame. We assume that one vessel -- the mother robot -- has access to global position data of high accuracy, while the other vessels -- the child robots -- utilize limited onboard sensors and sophisticated sensors on board the mother robot to localize themselves. This thesis provides details of the design of the elements of the heterogeneous fleet including the sensors and sensing algorithms along with the communication strategy used to localize all elements of the fleet within a global reference frame. Details of the robot platforms to be used in implementing a solution are also described. Simulation of the approach is used to demonstrate the effectiveness of the algorithm, and the algorithm and its components are evaluated using a fleet of ASVs

PPCPP: A Predator-Prey-Based Approach to Adaptive Coverage Path Planning

© 2004-2012 IEEE. Most of the existing coverage path planning (CPP) algorithms do not have the capability of enabling a robot to handle unexpected changes in the coverage area of interest. Examples of unexpected changes include the sudden introduction of stationary or dynamic obstacles in the environment and change in the reachable area for coverage (e.g., due to imperfect base localization by an industrial robot). Thus, a novel adaptive CPP approach is developed that is efficient to respond to changes in real-time while aiming to achieve complete coverage with minimal cost. As part of the approach, a total reward function that incorporates three rewards is designed where the first reward is inspired by the predator-prey relation, the second reward is related to continuing motion in a straight direction, and the third reward is related to covering the boundary. The total reward function acts as a heuristic to guide the robot at each step. For a given map of an environment, model parameters are first tuned offline to minimize the path length while assuming no obstacles. It is shown that applying these learned parameters during real-time adaptive planning in the presence of obstacles will still result in a coverage path with a length close to the optimized path length. Many case studies with various scenarios are presented to validate the approach and to perform numerous comparisons

Recent Advances in Multi Robot Systems

To design a team of robots which is able to perform given tasks is a great concern of many members of robotics community. There are many problems left to be solved in order to have the fully functional robot team. Robotics community is trying hard to solve such problems (navigation, task allocation, communication, adaptation, control, ...). This book represents the contributions of the top researchers in this field and will serve as a valuable tool for professionals in this interdisciplinary field. It is focused on the challenging issues of team architectures, vehicle learning and adaptation, heterogeneous group control and cooperation, task selection, dynamic autonomy, mixed initiative, and human and robot team interaction. The book consists of 16 chapters introducing both basic research and advanced developments. Topics covered include kinematics, dynamic analysis, accuracy, optimization design, modelling, simulation and control of multi robot systems