Perception of the local environment is a precondition for mobile robots to navigate safely in dynamic environments. Most robots, i.e., humanoids and smaller wheeled robots rely on planar regions. For humanoids, a simple 2D occupancy map as environment representation on which a path is planned is hereby not sufficient since they can step over and onto objects and therefore need height information. Considering dynamic obstacles introduces another level of complexity, since they can lead to necessary replanning or collisions at later stages. In this paper, we present a framework that first extracts planar regions in height maps and detects dynamic obstacles. Our system then uses this information to create a set of prediction maps, in which paths can be efficiently planned in real time at low CPU cost. We show in simulation and real-world experiments that our framework keeps run times well under 10ms for one computation cycle and allows for foresighted real-time 3D footstep planning.

In many situations, users walk on typical paths between specific destinations at which the service of a mobile robot is needed. Depending on the environment and the paths, step-by-step following of the human might not be the optimal solution since better paths for the robot exist. We propose to perform a prediction about the human's future movements and use this information in a reinforcement learning framework to generate foresighted navigation actions for the robot. Since frequent occlusions of the human will occur due to obstacles and the robot's constrained field of view, the estimate about the humans's position and the prediction of the next destination are affected by uncertainty. Our approach deals with such situations by explicitly considering occlusions in the reward function such that the robot automatically considers to execute actions to get the human in its field of view. We show in simulated and real-world experiments that our technique leads to significantly shorter paths compared to an approach in which the robot always tries to closely follow the user and, additionally, can handle occlusions.

To this date, many footstep planning systems rely on external sensors for mapping and traversability analysis or on computationally expensive algorithms that do not allow for real-time calculations. In this paper, we present an approach that analyzes the environment in the vicinity of the robot with an onboard RGBD camera while computing local footstep plans in real time. We achieve this by combining the advantages of grid-based height maps, fast planar region segmentation, and a systematic local footstep search to a local goal point. Using a single CPU core, a full mapping and planning cycle only takes 18 ms on average and thus presents an important step to autonomous humanoid robots in dynamic environments that only rely on their onboard hardware.

In this paper, we introduce an approach to efficient robot navigation through cluttered indoor environments. We propose to estimate local obstacle densities based on already detected objects and use them to predict traversal costs corresponding to potential obstacles in regions not yet observable by the robot's sensors. By taking into account the predicted costs for path planning, the robot is then able to navigate in a more foresighted manner and reduces the risk of getting stuck in cluttered regions. We thoroughly evaluated our approach in simulated and real-world experiments. As the experimental results demonstrate, our method enables the robot to efficiently navigate through environments containing cluttered regions and achieves significantly shorter completion times compared to a standard approach not using any prediction.