Mobile robots operating on unstructured terrain must predict which areas of the environment they are able to pass in order to plan feasible paths and to react to unforeseen terrain patterns. We address traversability estimation as a heightmap classification problem: we build a convolutional neural network that, given an image representing the heightmap of a terrain patch, predicts whether the robot will be able to traverse such patch from left to right. The classifier is trained for a specific wheeled robot model (but may implement any other locomotion type such as tracked, legged, snake-like), using simulation data on a variety of procedurally generated training terrains; once trained, the classifier can quickly be applied to patches extracted from unseen large heightmaps, in multiple orientations, thus building oriented traversability maps. We quantitatively validate the approach on real-elevation datasets and implement a path planning approach that employs our traversability estimation.
Geometry-based approaches use local @cite_15 or global sensory data to derive an elevation map of the terrain, which is a convenient spatial representation for ground robots @cite_34 . Then, one option is to simulate a model of the robot on different areas of such elevation map: this allows one to explicitly test traversability, or, in a simpler setup, to evaluate the pose that the robot would assume when lying on each point of the elevation map @cite_31 . A more common approach evaluates each point of the elevation map by extracting simple local features (such as slope, roughness, step height), and then estimate traversability either through handcrafted rules, or using a learned classifier @cite_12 @cite_7 @cite_24 .
@cite_31 @cite_7 @cite_24 @cite_15 @cite_34 @cite_12
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Autonomous long-range navigation in partially known planetary-like terrains is still an open challenge for robotics. Navigating hundreds of meters without any human intervention requires the robot to be able to build various representations of its environment, to plan and execute trajectories according to the kind of terrain traversed, to control its motions and to localize itself as it moves. All these activities have to be scheduled, triggered, controlled and interrupted according to the rover context. In this paper, we briefly review some functionalities that have been developed in our laboratory, and implemented on board the Marsokhod model robot, Lama. We then present how the various concurrent instances of the perception, localization and motion generation functionalities are integrated. Experimental results illustrate the functionalities throughout the paper. In this paper we present a traversability assessment method for motion planning in autonomous walking robots. The aim is to plan the motion of the robot in a real scenario on a rough terrain, where the level of details in the obtained terrain maps is not sufficient for motion planning. A guided RRT (Rapidly-exploring Random Trees) algorithm is used to plan the motion of the robot on rough terrain. We are looking for a method that can learn the terrain traversability cost function to the benefit of the guiding function of the planning algorithm. A probabilistic regression technique is used to solve the traversability assessment problem. Computing the predictions of the traversability values we use the RRT planner to explore the space of possible solutions. We demonstrate efficiency of the prediction method and we show results of experiments on the real walking robot. This paper presents a framework for planning safe and efficient paths for a legged robot in rough and unstructured terrain. The proposed approach allows to exploit the distinctive obstacle negotiation capabilities of legged robots, while keeping the complexity low enough to enable planning over considerable distances in short time. We compute typical terrain characteristics such as slope, roughness, and steps to build a traversability map. This map is used to assess the costs of individual robot footprints as a function of the robot-specific obstacle negotiating capabilities for steps, gaps and stairs. Our sampling-based planner employs the RRT* algorithm to optimize path length and safety. The planning framework has a hierarchical architecture to frequently replan the path during execution as new terrain is perceived with onboard sensors. Furthermore, a cascaded planning structure makes use of different levels of simplification to allow for fast search in simple environments, while retaining the ability to find complex solutions, such as paths through narrow passages. The proposed navigation planning framework is integrated on the quadrupedal robot StarlETH and extensively tested in simulation as well as on the real platform. 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. In this paper we address the problem of closing the loop from perception to action selection for unmanned ground vehicles, with a focus on navigating slopes. A new non-parametric learning technique is presented to generate a mobility representation where the maximum feasible speed is used as a criterion to classify the world. The inputs to the algorithm are terrain gradients derived from an elevation map and past observations of wheel slip. It is argued that such a representation can aid in path planning with improved selection of vehicle heading and velocity in off-road slopes. In addition, an information theoretic test is proposed to validate a chosen proprioceptive representation (such as slip) for mobility map generation. Results of mobility map generation and its benefits to path planning are shown.