Organizations: the College of Electrical Engineering, Zhejiang University, Hangzhou 310027, China, and also with the State Key Laboratory of Industrial Control Technology, Zhejiang University, Hangzhou 310027, China · the National Key Laboratory of Human-Machine Hybrid Augmented Intelligence, Xi’an Jiaotong University, Xi’an 710049, China, and also with the State Key Laboratory of Industrial Control Technology, Zhejiang University, Hangzhou 310027, China
To address the challenge of efficient coverage by multi-robot systems in non-convex regions with multiple obstacles, this paper proposes a coverage control method based on the Generalized Voronoi Graph (GVG), which has two phases: Load-Balancing Algorithm phase and Collaborative Coverage phase. In Load-Balancing Algorithm phase, the non-convex region is partitioned into multiple sub-regions based on GVG. Besides, a weighted load-balancing algorithm is developed, which considers the quality differences among sub-regions. By iteratively optimizing the robot allocation ratio, the number of robots in each sub-region is matched with the sub-region quality to achieve load balance. In Collaborative Coverage phase, each robot is controlled by a new controller to effectively coverage the region. The convergence of the method is proved and its performance is evaluated through simulations.
Figures & tables
Fig. 1: Overview of the proposed method, which includes Load-Balancing Algorithm phase (top) and Collaborative Coverage phase (bottom). Load-Balancing Algorithm determines the final number of robots in each region. In Collaborative Coverage phase, each robot are controlled to efficiently cover its region based on the designed controller.
Fig. 2: Vij and Vji are two GVG nodes defined by Eij . The circle is the node circle. The blue line represents the connection between the node and the intersection point of the circle and an obstacle, and it also serves as the boundary of the region.
Fig. 3: The non-convex area D with four black holes. The red line represents the GVG curve, and each GVG cell is denoted by a distinct color.
Fig. 4: The final configuration of robots in each region.
Fig. 5: A comparison between the actual number of robots and the ideal number of robots in each region.
Fig. 6: The initial configuration is shown in (a) and the configuration after the execution of the Load-Balancing Algorithm is shown in (b). The trajectories of robots is shown in (c) and (d) shows the final configuration and the circles represent the density.
Coverage control is essential for the optimal deployment of agents to monitor or cover areas with sensory demands. While traditional coverage involves single-task robots, increasing autonomy now enables multitask operations. This paper introduces a novel multitask coverage problem and addresses it for both the cases of known and unknown sensory demands. For known demands, we design a federated multitask coverage algorithm and establish its convergence properties. For unknown demands, we employ a multitask Gaussian Process (GP) framework to learn sensory demand functions and integrate it with the multitask coverage algorithm to develop an adaptive algorithm. We introduce a novel notion of multitask coverage regret that compares the performance of the adaptive algorithm against an oracle with prior knowledge of the demand functions. We establish that our algorithm achieves sublinear cumulative regret, and numerically illustrate its performance.
Lai Wei, Andrew McDonald, Vaibhav Srivastava
Life Sciences Institute, University of Michigan, Ann Arbor, MI 48109 USA · University of Cambridge, Cambridge, UK. · Department of Electrical and Computer Engineering. Michigan State University, East Lansing, MI 48823 USA.
This letter presents an energy-efficient multi-robot coverage path planning (MRCPP) framework for large, nonconvex Regions of Interest (ROI) containing obstacles and no-fly zones (NFZ). Existing minimum-energy coverage planning algorithms utilize meta-heuristic boustrophedon workspace decomposition. Therefore, even with minimum energy objectives and energy consumption constraints, they cannot achieve optimal energy efficiency. Moreover, most existing frameworks support only a single type of robotic platform. MRCPP overcomes these limitations by: generating globally-informed swath generation, creating parallel sweeping paths with minimal turns, calculating safety buffers to ensure safe turning clearance, using an efficient mTSP solver to balance workloads and minimize mission time, and connecting disjoint segments via a modified visibility graph that tracks heading angles while maintaining transitions within safe regions. The efficacy of the proposed MRCPP framework is demonstrated through real-world experiments involving autonomous aerial vehicles (AAVs) and autonomous surface vehicles (ASVs). Evaluations demonstrate that the proposed MRCPP consistently outperforms state-of-the-art planners, reducing average total energy consumption by 3% to 40% for a team of 3 robots and computation time by an order of magnitude, while maintaining balanced workload distribution and strong scalability across increasing fleet sizes. The MRCPP framework is released as an open-source package and videos of real-world and simulated experiments are available at https://mrc-pp.github.io.
Sourav Raxit, Jose Fuentes, Paulo Padrao +4
Department of Computer Science, University of New Orleans, New Orleans, LA 70148, USA · School of Computing and Information Sciences, Florida International University, Miami, FL 33199, USA · Department of Mathematics & Computer Science, Providence College, Providence, RI 02918
Density-Driven Optimal Control (D2OC) provides a principled approach to distributing multi-robot teams over non-uniform spatial distributions. Applying D2OC to nonholonomic robots, however, creates a gap between safety constraints imposed on a reference motion and the physical inputs that determine the actual robot motion. We address this issue by enforcing the safety constraint directly on the robot's physical inputs while preserving the density-driven coverage objective. The proposed framework combines D2OC with a control barrier function safety filter through a feedback-linearizing look-ahead point, allowing safety and actuator limits to be considered together during control. We further derive a safety margin that accounts for the look-ahead geometry, robot footprint, and motion during each control interval. Simulation results show that the proposed method maintains the required physical separation while achieving coverage performance comparable to a conventional reference-tracking approach, which can satisfy safety on the reference motion yet violate the corresponding physical clearance. Experiments on multiple nonholonomic robots in the Robotarium further demonstrate safe execution while driving the robots toward the desired spatial distribution. These results show that enforcing safety directly on the physical inputs can eliminate the mismatch between safety certification and physical robot motion in density-driven multi-robot coverage.
Julian Martinez, Kooktae Lee
Department of Mechanical and Aerospace Engineering, Texas Tech University, Lubbock, TX 79409, USA