The LRU Rover for Autonomous Planetary Exploration and its Success in the SpaceBotCamp Challenge

The task of planetary exploration poses many challenges for a robot system, from weight and size constraints to sensors and actuators suitable for extraterrestrial environment conditions. As there is a significant communication delay to other planets, the efficient operation of a robot system requires a high level of autonomy. In this work, we present the Light Weight Rover Unit (LRU), a small and agile rover prototype that we designed for the challenges of planetary exploration. Its locomotion system with individually steered wheels allows for high maneuverability in rough terrain and the application of stereo cameras as its main sensor ensures the applicability to space missions. We implemented software components for self-localization in GPS-denied environments, environment mapping, object search and localization and for the autonomous pickup and assembly of objects with its arm. Additional high-level mission control components facilitate both autonomous behavior and remote monitoring of the system state over a delayed communication link. We successfully demonstrated the autonomous capabilities of our LRU at the SpaceBotCamp challenge, a national robotics contest with focus on autonomous planetary exploration. A robot had to autonomously explore a moon-like rough-terrain environment, locate and collect two objects and assemble them after transport to a third object - which the LRU did on its first try, in half of the time and fully autonomous

Iteratively Refined Feasibility Checks in Robotic Assembly Sequence Planning

Due to shorter product life-cycles and increasing customization, production lines must be able to quickly adapt to novel product variants. This requires the automatic generation of assembly sequence plans from product specifications, as manual engineering of plans is slow and labor-intensive. The main challenge in assembly planning is that the search for a valid plan must take the capabilities of the robotic system that will execute the plan into account. But checking the feasibility of executing a plan requires a simulation of the system, which slows down the search for a valid and executable plan. We therefore propose and implement two ideas to reduce search times. First, we iteratively refine the feasibility check of an assembly plan from levels taking only parts into account (which is fast) to high-fidelity levels including motion planning and full robotic simulations which is high-fidelity, but slow). Slower levels are only checked if faster levels succeed. The second is that errors in these levels are propagated upwards as symbolic rules which prune the search tree. We demonstrate how our contributions reduce the need for high-fidelity simulations on a two-armed robotic system that assembles product variants out of aluminum profiles

Toward a Fully Autonomous UAV: Research Platform for Indoor and Outdoor Urban Search and Rescue

Urban Search and Rescue missions raise special requirements on robotic systems. Small aerial systems provide essential support to human task forces in situation assessment and surveillance. As external infrastructure for navigation and communication is usually not available, robotic systems must be able to operate autonomously. Limited payload of small aerial systems poses a great challenge to the system design. The optimal tradeoff between flight performance, sensors and computing resources has to be found. Communication to external computers cannot be guaranteed, therefore all processing and decision making has to be done on-board. In this paper, we present a UAS system design fulfilling these requirements. The components of our system are structured into groups to encapsulate their functionality and interfaces.We use both laser and stereo vision odometry to enable seamless indoor and outdoor navigation. The odometry is fused with an Inertial Measurement Unit in an Extended Kalman Filter. Navigation is supported by a module that recognizes known objects in the environment. A distributed computation approach is adopted to address computational requirements of the used algorithms. The capabilities of the system are validated in flight experiments, using a quadrotor

Towards Autonomous Planetary Exploration: The Lightweight Rover Unit (LRU), its Success in the SpaceBotCamp Challenge, and Beyond

Planetary exploration poses many challenges for a robot system: From weight and size constraints to extraterrestrial environment conditions, which constrain the suitable sensors and actuators. As the distance to other planets introduces a significant communication delay, the efficient operation of a robot system requires a high level of autonomy. In this work, we present our Lightweight Rover Unit (LRU), a small and agile rover prototype that we designed for the challenges of planetary exploration. Its locomotion system with individually steered wheels allows for high maneuverability in rough terrain and stereo cameras as its main sensors ensure the applicability to space missions. We implemented software components for self-localization in GPS-denied environments, autonomous exploration and mapping as well as computer vision, planning and control modules for the autonomous localization, pickup and assembly of objects with its manipulator. Additional high-level mission control components facilitate both autonomous behavior and remote monitoring of the system state over a delayed communication link. We successfully demonstrated the autonomous capabilities of our LRU at the SpaceBotCamp challenge, a national robotics contest with focus on autonomous planetary exploration. A robot had to autonomously explore an unknown Moon-like rough terrain, locate and collect two objects and assemble them after transport to a third object - which the LRU did on its first try, in half of the time and fully autonomously. The next milestone for our ongoing LRU development is an upcoming planetary exploration analogue mission to perform scientific experiments at a Moon analogue site located on a volcano