Ground robots which are able to navigate a variety of terrains are needed in many domains. One of the key aspects is the capability to adapt to the ground structure, which can be realized through movable body parts coming along with additional degrees of freedom (DoF). However, planning respective locomotion is challenging since suitable representations result in large state spaces. Employing an additional abstract representation—which is coarser, lower-dimensional, and semantically enriched—can support the planning. While a desired robot representation and action set of such an abstract representation can be easily defined, the cost function requires large tuning efforts. We propose a method to represent the cost function as a CNN. Training of the network is done on generated artificial data, while it generalizes well to the abstraction of real world scenes. We further apply our method to the problem of search-based planning of hybrid driving-stepping locomotion. The abstract representation is used as a powerful informed heuristic which accelerates planning by multiple orders of magnitude.
A solution to handle large environment sizes is multi-resolution planning @cite_2 . To handle high-dimensional state spaces, a local adaptation of the robot representation is an option. In previous work @cite_5 , we have proposed a search-based approach to plan hybrid driving-stepping locomotion. Similarly, @cite_4 have planned multi-modal paths for a humanoid with a search-based planner. Both approaches handle the occurring high-dimensional state spaces by separating the planning problem with respect to the locomotion mode and apply high-dimensional planning only if required. Nevertheless, both works suffer the problem of handling large scenarios in feasible time since the high-dimensional represented areas are still too large.
@cite_5 @cite_4 @cite_2
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Navigating in search and rescue environments is challenging, since a variety of terrains has to be considered. Hybrid driving-stepping locomotion, as provided by our robot Momaro, is a promising approach. Similar to other locomotion methods, it incorporates many degrees of freedom—offering high flexibility but making planning computationally expensive for larger environments. We propose a navigation planning method, which unifies different levels of representation in a single planner. In the vicinity of the robot, it provides plans with a fine resolution and a high robot state dimensionality. With increasing distance from the robot, plans become coarser and the robot state dimensionality decreases. We compensate this loss of information by enriching coarser representations with additional semantics. Experiments show that the proposed planner provides plans for large, challenging scenarios in feasible time. Path planning quickly becomes computationally hard as the dimensionality of the state-space increases. In this paper, we present a planning algorithm intended to speed up path planning for high-dimensional state-spaces such as robotic arms. The idea behind this work is that while planning in a high-dimensional state-space is often necessary to ensure the feasibilityof the resulting path, large portions of the path have a lower-dimensional structure. Based on this observation, our algorithm iteratively constructs a state-space of an adaptive dimensionality--a state-space that is high-dimensional only where the higher dimensionality is absolutely necessary for finding a feasible path. This often reduces drastically the size of the state-space, and as a result, the planning time and memory requirements. Analytically, we show that our method is complete and is guaranteed to find a solution if one exists, within a specified suboptimality bound. Experimentally, we apply the approach to 3D vehicle navigation (x, y, heading), and to a 7 DOF robotic arm on the Willow Garage’s PR2 robot. The results from our experiments suggest that ourmethod can be substantially faster than some of the state-of-the-art planning algorithms optimized for those tasks. Planning with kinodynamic constraints is often required for mobile robots operating in cluttered, complex environments. A common approach is to use a two-dimensional (2-D) global planner for long range planning, and a short range higher dimensional planner or controller capable of satisfying all of the constraints on motion. However, this approach is incomplete and can result in oscillations and the inability to find a path to the goal. In this paper we present an approach to solving this problem by combining the global and local path planning problem into a single search using a combined 2-D and higher dimensional state-space. In this paper we describe a novel path planning approach for mobile robots operating in indoor environments. In such scenarios, robots must be able to maneuver in crowded spaces, partially filled with static and dynamic obstacles (such as people). Our approach produces smooth, complex maneuvers over large distances through the use of an anytime graph search algorithm applied to a novel multi-resolution state lattice, where the resolution is adapted based on both environmental characteristics and task characteristics. In addition, we present a novel approach for generating fast globally optimal trajectories in constrained spaces (i.e. rooms connected via doors and hallways). This approach exploits offline precomputation to provide extremely efficient online performance and is applicable to a wide range of both indoor and outdoor navigation scenarios. By combining an anytime, multi-resolution lattice-based search algorithm with our precomputation technique, globally optimal trajectories in up to four dimensions (2D position, heading and velocity) are obtained in real-time.