Mobile robots with reconfigurable chassis are able to traverse unstructured outdoor environments with boulders or rubble, and overcome challenging structures in urban environments, like stairs or steps. Autonomously traversing rough terrain and such obstacles while ensuring the safety of the robot is a challenging task in mobile robotics. In this paper we introduce a two-phase motion planning al- gorithm for actively reconfigurable tracked robots. We first use the completeness of a graph search on a regular grid to quickly find an initial path in a low dimensional space, considering only the platform’s operating limits instead of the complete state. We then take this initial path to focus the RRT* search in the continuous high-dimensional state space including the actuators of the robot. We do not rely on a detailed structure/terrain classification or use any predefined motion sequences. Hence, our planner can be applied to urban structures, like stairs, as well as rough unstructured environments. Simulation results prove our method to be effective in solving planning queries in such environments

In disaster scenarios, mobile robots can be employed in hazardous environments where it is too dangerous for human rescuers. Robotic systems can assist rescue personnel as they can be used to explore those inaccessible areas and to assess the situation. Tracked platforms with actuators have been proven to be well suited for such deployments because they are agile enough to overcome quite challenging terrain. A very demanding task for operators is the navigation of the robotic system in complex disaster environments. Hence, an important capability of future systems for search and rescue missions is autonomous navigation in disaster scenarios. In this paper we introduce a two-phase motion planning algorithm for tracked robots with actively controlled actuators to find a fast and stable path to a user specified goal. In the first phase, we generate an initial path considering the platform's operating limits and the terrain roughness. In the second phase, we limit the search space to the area around the initial path and refine the preliminary solution accounting for the complete robot state including actuators and the robot's stability and traction. A main distinction of our method is that it does not rely on a previous classification of the terrain, thus, can be applied to a variety of environments. We present experiments evaluating our algorithm in simulation and in two real-world scenarios to demonstrate the validity and feasibility of our approach

Looking at the mobility of robots and their chassis most are limited to fairly flat environments. In urban environments common structures such as steps or stairs pose invincible obstacles for such systems. When it comes to unstructured outdoor environments a vast variety of obstacles is imaginable which are not traversable by common robots, for instance boulders, debris, rocks or trunks of fallen or chopped trees. However, there are mobile robots with adjustable chassis providing a higher degree of mobility and enabling them to overcome such obstacles. This paper presents first results on our motion planning algorithm which aims at utilizing the enhanced capabilities of those robots. It takes into account the chassis configuration and the system stability to propose the best path. We use a high level planner to quickly generate a preliminary path by considering the platform's operating limits. We then distinguish between path segments on flat and rough terrain. For each hard segment we restrict the search space to a tube around the initial path. A subsequent planner is used to refine the preliminary path by considering the actuator positions, the robot's stability and a ground contact factor. Our planning algorithm is general in the sense that we do not categorize obstacles and do not use predefined motion sequences for those obstacle classes. Finally, we present a discussion including an analysis of the time complexity and a simulation application in ROS and Gazebo as proof of feasability

In this paper we present a method for navigating a multi-robot system through an environment while additionally maintaining a predefined set of constraints. Possible constraints are the requirement to keep up the direct line-of-sight between robots or to ensure that robots stay within a certain distance. Our approach is based on graph structures that model movements and constraints separately, in order to cover different robots and a large class of possible constraints. Additionally, the partition of movement and constraint graph allows us to use known graph algorithms like Steiner trees to solve the problem of finding a target configuration for the robots. We construct so called separated distance graphs from the Steiner tree and the underlying roadmap graph, which allow assembling valid navigation plans fast

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