text: 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.
  @cite_5: Abstract Harmonic functions provide optimal potential maps for robot navigation in a previously explored static environment. Here we investigate the performance of an algorithm for exploration based on partial updates of a harmonic potential in an occupancy grid. We consider that while the robot moves it carries along an activation window whose size is of the order of the sensor’s range. The activation window recruits grid points to participate in the potential calculation. By using simulations and experiments with the Nomad 200 robot we investigate the algorithm performance in respect to parameters such as the frequency of updates and the numerical method used to calculate the harmonic potential.
  @cite_4: 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.
  @cite_12: 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.
  @cite_2: 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.
summary: 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.