32,736 research outputs found

    Efficient Multi-Robot Coverage of a Known Environment

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    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

    Modeling and stochastic optimization of complete coverage under uncertainties in multi-robot base placements

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    © 2016 IEEE. Uncertainties in base placements of mobile, autonomous industrial robots can cause incomplete coverage in tasks such as grit-blasting and spray painting. Sensing and localization errors can cause such uncertainties in robot base placements. This paper addresses the problem of collaborative complete coverage under uncertainties through appropriate base placements of multiple mobile and autonomous industrial robots while aiming to optimize the performance of the robot team. A mathematical model for complete coverage under uncertainties is proposed and then solved using a stochastic multi-objective optimization algorithm. The approach aims to concurrently find an optimal number and sequence of base placements for each robot such that the robot team's objectives are optimized whilst uncertainties are accounted for. Several case studies based on a real-world application using a realworld object and a complex simulated object are provided to demonstrate the effectiveness of the approach for different conditions and scenarios, e.g. various levels of uncertainties, different numbers of robots, and robots with different capabilities

    A New Frontier Based Approach for Multi-Robot Coverage in Unknown Environments

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    With the emergence of technology in our daily lives, robots are being increasingly used for coverage tasks which were earlier considered too dangerous or monotonous to be performed by humans such as interplanetary exploration and search & rescue. Out of all the multi-robot coverage approaches, the frontier based approach is one of the most widely used. Most of the coverage approaches developed so far, face the issue of frontier duplication and require access to the maps of the environment prior to coverage. In this work, we have developed a new frontier based approach for multi-robot coverage in unknown environments. This new approach is scalable to multiple robots and does not require prior access to the maps. This approach also uses a new frontier allocation and robot coordination algorithm, which reduces the frontier duplication in the robots and improves the efficiency of robot coverage

    Multi robot cooperative area coverage, case study: spraying

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    Area coverage is a well-known problem in multi robotic systems, and it is a typical requirement in various real-world applications. A common and popular approach in the robotic community is to use explicit forms of communication for task allocation and coordination. These approaches are susceptible to the loss of communication signal, and costly with high computational complexity. There are very few approaches which are focused on implicit forms of communication. In these approaches, robots rely only on their local information for task allocation and coordination. In this paper, a cooperative strategy is proposed by which a team of robots perform spraying a large field. The focus of this paper is to achieve task allocation and coordination using only the robots' local information. Keywords: Multi Robotic System, Cooperative Behaviour, Coopera- tive Area Coverag

    A Dynamical System Approach for Resource-Constrained Mobile Robotics

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    The revolution of autonomous vehicles has led to the development of robots with abundant sensors, actuators with many degrees of freedom, high-performance computing capabilities, and high-speed communication devices. These robots use a large volume of information from sensors to solve diverse problems. However, this usually leads to a significant modeling burden as well as excessive cost and computational requirements. Furthermore, in some scenarios, sophisticated sensors may not work precisely, the real-time processing power of a robot may be inadequate, the communication among robots may be impeded by natural or adversarial conditions, or the actuation control in a robot may be insubstantial. In these cases, we have to rely on simple robots with limited sensing and actuation, minimal onboard processing, moderate communication, and insufficient memory capacity. This reality motivates us to model simple robots such as bouncing and underactuated robots making use of the dynamical system techniques. In this dissertation, we propose a four-pronged approach for solving tasks in resource-constrained scenarios: 1) Combinatorial filters for bouncing robot localization; 2) Bouncing robot navigation and coverage; 3) Stochastic multi-robot patrolling; and 4) Deployment and planning of underactuated aquatic robots. First, we present a global localization method for a bouncing robot equipped with only a clock and contact sensors. Space-efficient and finite automata-based combinatorial filters are synthesized to solve the localization task by determining the robot’s pose (position and orientation) in its environment. Second, we propose a solution for navigation and coverage tasks using single or multiple bouncing robots. The proposed solution finds a navigation plan for a single bouncing robot from the robot’s initial pose to its goal pose with limited sensing. Probabilistic paths from several policies of the robot are combined artfully so that the actual coverage distribution can become as close as possible to a target coverage distribution. A joint trajectory for multiple bouncing robots to visit all the locations of an environment is incrementally generated. Third, a scalable method is proposed to find stochastic strategies for multi-robot patrolling under an adversarial and communication-constrained environment. Then, we evaluate the vulnerability of our patrolling policies by finding the probability of capturing an adversary for a location in our proposed patrolling scenarios. Finally, a data-driven deployment and planning approach is presented for the underactuated aquatic robots called drifters that creates the generalized flow pattern of the water, develops a Markov-chain based motion model, and studies the long- term behavior of a marine environment from a flow point-of-view. In a broad summary, our dynamical system approach is a unique solution to typical robotic tasks and opens a new paradigm for the modeling of simple robotics system
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