69 research outputs found
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
System of Terrain Analysis, Energy Estimation and Path Planning for Planetary Exploration by Robot Teams
NASA’s long term plans involve a return to manned moon missions, and eventually sending humans to mars. The focus of this project is the use of autonomous mobile robotics to enhance these endeavors. This research details the creation of a system of terrain classification, energy of traversal estimation and low cost path planning for teams of inexpensive and potentially expendable robots.
The first stage of this project was the creation of a model which estimates the energy requirements of the traversal of varying terrain types for a six wheel rocker-bogie rover. The wheel/soil interaction model uses Shibly’s modified Bekker equations and incorporates a new simplified rocker-bogie model for estimating wheel loads. In all but a single trial the relative energy requirements for each soil type were correctly predicted by the model.
A path planner for complete coverage intended to minimize energy consumption was designed and tested. It accepts as input terrain maps detailing the energy consumption required to move to each adjacent location. Exploration is performed via a cost function which determines the robot’s next move. This system was successfully tested for multiple robots by means of a shared exploration map. At peak efficiency, the energy consumed by our path planner was only 56% that used by the best case back and forth coverage pattern.
After performing a sensitivity analysis of Shibly’s equations to determine which soil parameters most affected energy consumption, a neural network terrain classifier was designed and tested. The terrain classifier defines all traversable terrain as one of three soil types and then assigns an assumed set of soil parameters. The classifier performed well over all, but had some difficulty distinguishing large rocks from sand.
This work presents a system which successfully classifies terrain imagery into one of three soil types, assesses the energy requirements of terrain traversal for these soil types and plans efficient paths of complete coverage for the imaged area. While there are further efforts that can be made in all areas, the work achieves its stated goals
Coverage Path Planning with Real‐time Replanning and Surface Reconstruction for Inspection of Three‐dimensional Underwater Structures using Autonomous Underwater Vehicles
Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/113717/1/rob21554.pd
End-to-end Reinforcement Learning for Online Coverage Path Planning in Unknown Environments
Coverage path planning is the problem of finding the shortest path that
covers the entire free space of a given confined area, with applications
ranging from robotic lawn mowing and vacuum cleaning, to demining and
search-and-rescue tasks. While offline methods can find provably complete, and
in some cases optimal, paths for known environments, their value is limited in
online scenarios where the environment is not known beforehand, especially in
the presence of non-static obstacles. We propose an end-to-end reinforcement
learning-based approach in continuous state and action space, for the online
coverage path planning problem that can handle unknown environments. We
construct the observation space from both global maps and local sensory inputs,
allowing the agent to plan a long-term path, and simultaneously act on
short-term obstacle detections. To account for large-scale environments, we
propose to use a multi-scale map input representation. Furthermore, we propose
a novel total variation reward term for eliminating thin strips of uncovered
space in the learned path. To validate the effectiveness of our approach, we
perform extensive experiments in simulation with a distance sensor, surpassing
the performance of a recent reinforcement learning-based approach
Autonomous search of real-life environments combining dynamical system-based path planning and unsupervised learning
In recent years, advancements have been made towards the goal of using
chaotic coverage path planners for autonomous search and traversal of spaces
with limited environmental cues. However, the state of this field is still in
its infancy as there has been little experimental work done. Current
experimental work has not developed robust methods to satisfactorily address
the immediate set of problems a chaotic coverage path planner needs to overcome
in order to scan realistic environments within reasonable coverage times. These
immediate problems are as follows: (1) an obstacle avoidance technique which
generally maintains the kinematic efficiency of the robot's motion, (2) a means
to spread chaotic trajectories across the environment (especially crucial for
large and/or complex-shaped environments) that need to be covered, and (3) a
real-time coverage calculation technique that is accurate and independent of
cell size. This paper aims to progress the field by proposing algorithms that
address all of these problems by providing techniques for obstacle avoidance,
chaotic trajectory dispersal, and accurate coverage calculation. The algorithms
produce generally smooth chaotic trajectories and provide high scanning
coverage of environments. These algorithms were created within the ROS
framework and make up a newly developed chaotic path planning application. The
performance of this application was comparable to that of a conventional
optimal path planner. The performance tests were carried out in environments of
various sizes, shapes, and obstacle densities, both in real-life and Gazebo
simulations
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
Coverage path planning methods focusing on energy efficient and cooperative strategies for unmanned aerial vehicles
The coverage path planning (CPP) algorithms aim to cover the total area of interest with minimum overlapping. The goal of the CPP algorithms is to minimize the total covering path and execution time. Significant research has been done in robotics, particularly for multi-unmanned unmanned aerial vehicles (UAVs) cooperation and energy efficiency in CPP problems. This paper presents a review of the early-stage CPP methods in the robotics field. Furthermore, we discuss multi-UAV CPP strategies and focus on energy-saving CPP algorithms. Likewise, we aim to present a comparison between energy efficient CPP algorithms and directions for future research
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