Autonomous exploration of unknown environments has been widely applied in inspection, surveillance, and search and rescue. In exploration task, the basic requirement for robots is to detect the unknown space as fast as possible. In this paper, we propose an autonomous collaborative system consists of an aerial robot and a ground vehicle to explore in unknown environments. We combine the frontier based method and the harmonic field to generate a path. Then, For the ground robot, a minimum jerk piecewise Bezier curve which can guarantee safety and dynamical feasibility is generated amid obstacles. For the aerial robot, a motion primitive method is adopted for local path planning. We implement the proposed framework on an autonomous collaborative aerial-ground system. Extensive field experiments as well as simulations are presented to validate the method and demonstrate its higher efficiency against each single vehicle.
For robotics exploration, @cite_2 first proposed the concept of frontier, which is defined as unknown grid-map cells adjacent to free ones and thus represents accessible new information. Harmonic function, the solution to @math , is used to plan path to frontiers @cite_5 . This method generates a scalar field in free-space based on its surrounding boundary conditions (occupied cells and frontier cells) and obtains the path using gradient-descent. For air-ground exploration, @cite_4 uses the UAV as an back-up instead of an independent explorer. It is only deployed when UGV encounters high, invisible areas. @cite_12 is also proposed based on the same spirit that one vehicle helps another, failing to exploit both vehicles' full potential. Compared to these works, the collaborative system proposed in this paper fully utilizes advantages of different vehicles and thus results in a more efficient exploration. We summarize our contribution as: 1. An efficient exploration framework that combines UAV and UGV's advantages. 2. A more efficient computation method of harmonic function for robotic exploration tasks. 3. Integration of the proposed collaborative exploration framework with the state estimation, sensor fusion and trajectory optimization. Extensive field experiments are presented to validate the efficiency and robustness of the proposed method.
@cite_5 @cite_4 @cite_12 @cite_2
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Exploring and mapping previously unknown environments while avoiding collisions with obstacles is a fundamental task for autonomous robots. In scenarios where this needs to be done rapidly, multi-rotors are a good choice for the task, as they can cover ground at potentially very high velocities. Flying at high velocities, however, implies the ability to rapidly plan trajectories and to react to new information quickly. In this paper, we propose an extension to classical frontier-based exploration that facilitates exploration at high speeds. The extension consists of a reactive mode in which the multi-rotor rapidly selects a goal frontier from its field of view. The goal frontier is selected in a way that minimizes the change in velocity necessary to reach it. While this approach can increase the total path length, it significantly reduces the exploration time, since the multi-rotor can fly at consistently higher speeds. We introduce a new approach for exploration based on the concept of frontiers, regions on the boundary between open space and unexplored space. By moving to new frontiers, a mobile robot can extend its map into new territory until the entire environment has been explored. We describe a method for detecting frontiers in evidence grids and navigating to these frontiers. We also introduce a technique for minimizing specular reflections in evidence grids using laser-limited sonar. We have tested this approach with a real mobile robot, exploring real-world office environments cluttered with a variety of obstacles. An advantage of our approach is its ability to explore both large open spaces and narrow cluttered spaces, with walls and obstacles in arbitrary orientation. We address the problem of planning a path for a ground robot through unknown terrain, using observations from a flying robot. In search and rescue missions, which are our target scenarios, the time from arrival at the disaster site to the delivery of aid is critically important. Previous works required exhaustive exploration before path planning, which is time-consuming but eventually leads to an optimal path for the ground robot. Instead, we propose active exploration of the environment, where the flying robot chooses regions to map in a way that optimizes the overall response time of the system, which is the combined time for the air and ground robots to execute their missions. In our approach, we estimate terrain classes throughout our terrain map, and we also add elevation information in areas where the active exploration algorithm has chosen to perform 3-D reconstruction. This terrain information is used to estimate feasible and efficient paths for the ground robot. By exploring the environment actively, we achieve superior response times compared to both exhaustive and greedy exploration strategies. We demonstrate the performance and capabilities of the proposed system in simulated and real-world outdoor experiments. To the best of our knowledge, this is the first work to address ground robot path planning using active aerial exploration. We propose an information-theoretic planning approach that enables mobile robots to autonomously construct dense 3D maps in a computationally efficient manner. Inspired by prior work, we accomplish this task by formulating an information-theoretic objective function based on CauchySchwarz quadratic mutual information (CSQMI) that guides robots to obtain measurements in uncertain regions of the map. We then contribute a two stage approach for active mapping. First, we generate a candidate set of trajectories using a combination of global planning and generation of local motion primitives. From this set, we choose a trajectory that maximizes the information-theoretic objective. Second, we employ a gradientbased trajectory optimization technique to locally refine the chosen trajectory such that the CSQMI objective is maximized while satisfying the robot’s motion constraints. We evaluated our approach through a series of simulations and experiments on a ground robot and an aerial robot mapping unknown 3D environments. Real-world experiments suggest our approach reduces the time to explore an environment by 70 compared to a closest frontier exploration strategy and 57 compared to an information-based strategy that uses global planning, while simulations demonstrate the approach extends to aerial robots with higher-dimensional state. Exploration of unknown environments is an important aspect to fielding teams of robots. Without the ability to determine on their own where to go in the environment, the full potential of robotic teams is limited to the abilities of human operators to deploy them for search and rescue, mapping, or other tasks that are predicated on gaining knowledge from the environment. This is of particular importance in real-world 3-Dimensional (3-D) environments where simple planar assumptions can lead to incomplete exploration, for example, real-world environments have areas underneath overhangs or inside caves. As an additional challenge, when the teams of robots have vastly different capabilities, the planning system must take those into account to efficiently utilize the available assets. In this paper, we present a combined air-ground system for conducting 3-D exploration in cluttered environments. We first describe the hardware and software components of the system. We then present our algorithm for planning 3-D goal locations for a heterogeneous team of robots to efficiently explore a previously unknown environment and demonstrate its applicability in real-world experiments. Cameras are a natural fit for micro aerial vehicles MAVs due to their low weight, low power consumption, and two-dimensional field of view. However, computationally-intensive algorithms are required to infer the 3D structure of the environment from 2D image data. This requirement is made more difficult with the MAV's limited payload which only allows for one CPU board. Hence, we have to design efficient algorithms for state estimation, mapping, planning, and exploration. We implement a set of algorithms on two different vision-based MAV systems such that these algorithms enable the MAVs to map and explore unknown environments. By using both self-built and off-the-shelf systems, we show that our algorithms can be used on different platforms. All algorithms necessary for autonomous mapping and exploration run on-board the MAV. Using a front-looking stereo camera as the main sensor, we maintain a tiled octree-based 3D occupancy map. The MAV uses this map for local navigation and frontier-based exploration. In addition, we use a wall-following algorithm as an alternative exploration algorithm in open areas where frontier-based exploration under-performs. During the exploration, data is transmitted to the ground station which runs large-scale visual SLAM. We estimate the MAV's state with inertial data from an IMU together with metric velocity measurements from a custom-built optical flow sensor and pose estimates from visual odometry. We verify our approaches with experimental results, which to the best of our knowledge, demonstrate our MAVs to be the first vision-based MAVs to autonomously explore both indoor and outdoor environments. 1. ABSTRACT Frontier-based exploration directs mobile robots to regions on the boundary between unexplored space and space that is known to be open. Previously, we have demonstrated that frontier-based exploration can be used to map indoor environments where walls and obstacles may be in arbitrary orientations. In this paper, we show how frontier-based exploration can be extended to multiple robots. In our approach, robots share perceptual information, but maintain separate global maps, and make independent decisions about where to explore. This approach enables robots to make use of information from other robots to explore more effectively, but it also allows the team to be robust to the loss of individual robots. We have implemented our multirobot exploration system on real robots, and we demonstrate that they can explore and map office environments as a team. This article investigates the prerequisites for a global exploration strategy in an unknown environment on a virtual disaster site. Assume that a robot equipped with a laser range scanner can build a detailed map of a previous unknown environment. The remaining question is how to use this information on this map for further exploration. On a map several interesting locations can be present where the exploration can be continued, referred as exploration frontiers. Typically, a greedy algorithm is used for the decision which frontier to explore next. Such a greedy algorithm only considers interesting locations locally, focused to reduce the movement costs. More sophisticated algorithms also take into account the information that can be gained along each frontier. This shifts the problem to estimate the amount of unexplored area behind the frontiers on the global map. Our algorithm exploits the long range of current laser scanners. Typically, during the previous exploration a small number of laser rays already passed the frontier, but this number is too low to have major impact on the generated map. Yet, the few rays through a frontier can be used to estimate the potential information gain from unexplored area beyond the frontier. We propose a multi-robot exploration algorithm that uses adaptive coordination to provide heterogeneous behavior. The key idea is to maximize the efficiency of exploring and mapping an unknown environment when a team is faced with unreliable communication and limited battery life (e.g., with aerial rotorcraft). The proposed algorithm utilizes four states: explore, meet, sacrifice, and relay. The explore state uses a frontier-based exploration algorithm, the meet state returns to the last known location of communication to share data, the sacrifice state sends the robot out to explore without consideration of remaining battery, and the relay state lands the robot until a meeting occurs. This approach allows robots to take on the role of a relay to improve communication between team members. In addition, the robots can “sacrifice” themselves by continuing to explore even when they do not have sufficient battery to return to the base station. We compare the performance of the proposed approach to state-of-the-art frontier-based exploration, and results show gains in explored area. The feasibility of components of the proposed approach is also demonstrated on a team of two custom-built quadcopters exploring an office environment. Exploration and mapping are fundamental prerequisites for autonomous robots operating in initially unknown environments. In this paper, we evaluate simple yet efficient fronti er-based exploration strategies. Furthermore, we discuss improvements to the classic frontier-based exploration st rategy by that further shorten the resulting exploration paths and present results from a comparative evaluation with a reference exploration strategy taken from the literature. An information-driven autonomous robotic explo- ration method on a continuous representation of unknown envi- ronments is proposed in this paper. The approach conveniently handles sparse sensor measurements to build a continuous model of the environment that exploits structural dependencies without the need to resort to a fixed resolution grid map. A gradient field of occupancy probability distribution is regressed from sensor data as a Gaussian process providing frontier boundaries for further exploration. The resulting continuous global frontier surface completely describes unexplored regions and, inherently, provides an automatic stop criterion for a desired sensitivity. The performance of the proposed approach is evaluated through simulation results in the well-known Freiburg and Cave maps. A concept for a multicopter unmanned aerial vehicle (UAV) automatically performing inspection flights at a wind turbine is proposed. Key aspects of the concept are (1) a priori 3D mapping of the plant and (2) spline-based flight path planning as well as (3) a collision avoidance and distance control system. A quadrotor UAV prototype and its dynamical model are presented. Validation of the different aspects is carried out in simulation and partially in indoor tests using Robot Operating System (ROS). Existence of a 3D map is an essential precondition for path planning and collision-free flight. A brief initial flight preceding the actual inspection with a 2D LiDAR sensor yields a point cloud of the plant which is used for 3D mapping. This map is efficiently generated and represented using octrees, a hierarchical data structure that can be used for 3D maps. Subsequently a smooth and collision-free flight path is generated using splines. For redundancy's sake navigation tasks not only rely on GPS but also on the LiDAR sensor mentioned before. The sensor allows for continuous estimation of the distance between multicopter and wind turbine. A distance control algorithm guarantees collision-free flight. Motion planning for unmanned ground vehicles (UGV) constitutes a domain of research where several disciplines meet, ranging from artificial intelligence and machine learning to robot perception and computer vision. In view of the plurality of related applications such as planetary exploration, search and rescue, agriculture, mining and off-road exploration, the aim of the present survey is to review the field of 3D terrain traversability analysis that is employed at a preceding stage as a means to effectively and efficiently guide the task of motion planning. We identify that in the epicenter of all related methodologies, 3D terrain information is used which is acquired from LIDAR, stereo range data, color or other sensory data and occasionally combined with static or dynamic vehicle models expressing the interaction of the vehicle with the terrain. By taxonomizing the various directions that have been explored in terrain perception and analysis, this review takes a step toward agglomerating the dispersed contributions from individual domains by elaborating on a number of key similarities as well as differences, in order to stimulate research in addressing the open challenges and inspire future developments. Search and exploration using multiple autonomous sensing platforms has been extensively studied in the fields of controls and artificial intelligence. The task of persistent surveillance is different from a coverage or exploration problem, in that the target area needs to be continuously searched, minimizing the time between visitations to the same region. This difference does not allow a straightforward application of most exploration techniques to the problem, although ideas from these methods can still be used. In this research we investigate techniques that are scalable, reliable, efficient, and robust to problem dynamics. These are tested in a multiple unmanned air vehicle (UAV) simulation environment, developed for this program. A semi-heuristic control policy for a single UAV is extended to the case of multiple UAVs using two methods. One is an extension of a reactive policy for a single UAV and the other involves allocation of sub-regions to individual UAVs for parallel exploration. An optimal assignment procedure (based on auction algorithms) has also been developed for this purpose. A comparison is made between the two approaches and a simplified optimal result. The reactive policy is found to exhibit an interesting emergent behavior as the number of UAVs becomes large. The control policy derived for a single UAV is modified to account for actual aircraft dynamics (a 3 degree-of-freedom nonlinear dynamics simulation is used for this purpose) and improvements in performance are observed. Finally, we draw conclusions about the utility and efficiency of these techniques. The theoretical ability of modular robots to reconfigure in response to complex tasks in a priori unknown environments has frequently been cited as an advantage, but has never been experimentally demonstrated. For the first time, we present a system that integrates perception, high-level mission planning, and modular robot hardware, allowing a modular robot to autonomously reconfigure in response to an a priori unknown environment in order to complete high-level tasks. Three hardware experiments validate the system, and demonstrate a modular robot autonomously exploring, reconfiguring, and manipulating objects to complete high-level tasks in unknown environments. We present system architecture, software and hardware in a general framework that enables modular robots to solve tasks in unknown environments using autonomous, reactive reconfiguration. The physical robot is composed of modules that support multiple robot configurations. An onboard 3D sensor provides information about the environment and informs exploration, reconfiguration decision making and feedback control. A centralized high-level mission planner uses information from the environment and the user-specified task description to autonomously compose low-level controllers to perform locomotion, reconfiguration, and other behaviors. A novel, centralized self-reconfiguration method is used to change robot configurations as needed. In robotics, grid maps are often used for solving tasks like collision checking, path planning, and localization. Many approaches to these problems use Euclidean distance maps (DMs), generalized Voronoi diagrams (GVDs), or configuration space (c-space) maps. A key challenge for their application in dynamic environments is the efficient update after potential changes due to moving obstacles or when mapping a previously unknown area. To this end, this paper presents novel algorithms that perform incremental updates that only visit cells affected by changes. Furthermore, we propose incremental update algorithms for DMs and GVDs in the configuration space of non-circular robots. These approaches can be used to implement highly efficient collision checking and holonomic path planning for these platforms. Our c-space representations benefit from parallelization on multi-core CPUs and can also be integrated with other state-of-the-art path planners such as rapidly-exploring random trees. In various experiments using real-world data we show that our update strategies for DMs and GVDs require substantially less cell visits and computation time compared to previous approaches. Furthermore, we demonstrate that our GVD algorithm deals better with non-convex structures, such as indoor areas. All our algorithms consider actual Euclidean distances rather than grid steps and are easy to implement. An open source implementation is available online. Probability theory has become the standard framework in the field of mobile robotics because of the inherent uncertainty associated with sensing and acting. In this paper, we show that the theory of belief functions with its ability to distinguish between different types of uncertainty is able to provide significant advantages over probabilistic approaches in the context of robotics. We do so by presenting solutions to the essential problems of simultaneous localization and mapping (SLAM) and planning based on belief functions. For SLAM, we show how the joint belief function over the map and the robot's poses can be factored and efficiently approximated using a Rao-Blackwellized particle filter, resulting in a generalization of the popular probabilistic FastSLAM algorithm. Our SLAM algorithm produces occupancy grid maps where belief functions explicitly represent additional information about missing and conflicting measurements compared to probabilistic grid maps. The basis for this SLAM algorithm are forward and inverse sensor models, and we present general evidential models for range sensors like sonar and laser scanners. Using the generated evidential grid maps, we show how optimal decisions can be made for path planning and active exploration. To demonstrate the effectiveness of our evidential approach, we apply it to two real-world datasets where a mobile robot has to explore unknown environments and solve different planning problems. Finally, we provide a quantitative evaluation and show that the evidential approach outperforms a probabilistic one both in terms of map quality and navigation performance. A belief-function-based approach to SLAM for mobile robots is presented.Different types of uncertainty are explicitly represented in evidential grid maps.Optimal navigation and exploration based on evidential grid maps is shown.Evidential forward and inverse models for range sensors are provided.The approach is evaluated using real-world datasets recorded by a mobile robot. In this paper, a novel solution for autonomous robotic exploration is proposed. We model the distribution of information in an unknown environment as an unsteady diffusion process, which can be an appropriate mathematical formulation and analogy for expanding, time-varying, and dynamic environments. This information distribution map is the solution of the diffusion process partial differential equation, and is regressed from sensor data as a Gaussian Process. Optimization of the process parameters leads to an optimal frontier map which describes regions of interest for further exploration. Since the presented approach considers a continuous model of the environment, it can be used to plan smooth exploration paths exploiting the structural dependencies of the environment whilst handling sparse sensor measurements. The performance of the approach is evaluated through simulation results in the well-known Freiburg and Cave maps. In this paper, a novel solution for autonomous robotic exploration is proposed. We model the distribution of information in an unknown environment as an unsteady diffusion process, which can be an appropriate mathematical formulation and analogy for expanding, time-varying, and dynamic envi- ronments. This information distribution map is the solution of the diffusion process partial differential equation, and is regressed from sensor data as a Gaussian Process. Optimization of the process parameters leads to an optimal frontier map which describes regions of interest for further exploration. Since the presented approach considers a continuous model of the environment, it can be used to plan smooth exploration paths exploiting the structural dependencies of the environment whilst handling sparse sensor measurements. The performance of the approach is evaluated through simulation results in the well- known Freiburg and Cave maps. Abstract We address the problem of controlling a mobile robot to explore a partially known environment. The robot’s objective is the maximization of the amount of information collected about the environment. We formulate the problem as a partially observable Markov decision process (POMDP) with an information-theoretic objective function, and solve it applying forward simulation algorithms with an open-loop approximation. We present a new sample-based approximation for mutual information useful in mobile robotics. The approximation can be seamlessly integrated with forward simulation planning algorithms. We investigate the usefulness of POMDP based planning for exploration, and to alleviate some of its weaknesses propose a combination with frontier based exploration. Experimental results in simulated and real environments show that, depending on the environment, applying POMDP based planning for exploration can improve performance over frontier exploration.