text: 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.
  @cite_5: The manufacturing industry today is still focused on the maximization of production. A possible development able to support the global achievement of this goal is the implementation of a new support system for trajectory-planning, specific for industrial robots. This paper describes the trajectory-planning algorithm, able to generate trajectories manageable by human operators, consisting of linear and circular movement primitives. First, the world model and a topology preserving roadmap are stored in a probabilistic occupancy octree by applying a cell extension based algorithm. Successively, the roadmap is constructed within the free reachable joint space maximizing the clearance to the obstacles. A search algorithm is applied on robot configuration positions within the roadmap to identify a path avoiding static obstacles. Finally, the resulting path is converted through an elastic net algorithm into a robot trajectory, which consists of canonical ordered linear and circular movement primitives. The algorithm is demonstrated in a real industrial manipulator context.
  @cite_9: 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.
  @cite_4: 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.
summary: Planning for systems with high-dimensional motion flexibility quickly reaches its limits for larger environments since the search space grows exponentially. Similar to multiresolution planning, several approaches utilize multiple representations with different planning dimensionalities to decrease planning complexity. @cite_5 generate an initial plan in a low-dimensional search space and replan in the high-dimensional search space by only considering those states that are part of the low-dimensional plan. @cite_4 plan a path in a low-dimensional search space and only switch to high-dimensional planning in those areas where low-dimensional planning cannot find a solution. Similarly, @cite_9 plan in 2D and switch to high-dimensional planning in the robot vicinity and at key points. As described for multiresolution planning, planning with multiple robot configuration dimensionalities might lead to wrong or bad plans, since a low-dimensional robot representation might assess challenging situations wrongly.