4,064 research outputs found
An advanced telerobotic system for shuttle payload changeout room processing applications
To potentially alleviate the inherent difficulties in the ground processing of the Space Shuttle and its associated payloads, a teleoperated, semi-autonomous robotic processing system for the Payload Changeout Room (PCR) is now in the conceptual stages. The complete PCR robotic system as currently conceived is described and critical design issues and the required technologies are discussed
Enhancing 3D Autonomous Navigation Through Obstacle Fields: Homogeneous Localisation and Mapping, with Obstacle-Aware Trajectory Optimisation
Small flying robots have numerous potential applications, from quadrotors for search and rescue, infrastructure inspection and package delivery to free-flying satellites for assistance activities inside a space station. To enable these applications, a key challenge is autonomous navigation in 3D, near obstacles on a power, mass and computation constrained platform. This challenge requires a robot to perform localisation, mapping, dynamics-aware trajectory planning and control. The current state-of-the-art uses separate algorithms for each component. Here, the aim is for a more homogeneous approach in the search for improved efficiencies and capabilities. First, an algorithm is described to perform Simultaneous Localisation And Mapping (SLAM) with physical, 3D map representation that can also be used to represent obstacles for trajectory planning: Non-Uniform Rational B-Spline (NURBS) surfaces. Termed NURBSLAM, this algorithm is shown to combine the typically separate tasks of localisation and obstacle mapping. Second, a trajectory optimisation algorithm is presented that produces dynamically-optimal trajectories with direct consideration of obstacles, providing a middle ground between path planners and trajectory smoothers. Called the Admissible Subspace TRajectory Optimiser (ASTRO), the algorithm can produce trajectories that are easier to track than the state-of-the-art for flight near obstacles, as shown in flight tests with quadrotors. For quadrotors to track trajectories, a critical component is the differential flatness transformation that links position and attitude controllers. Existing singularities in this transformation are analysed, solutions are proposed and are then demonstrated in flight tests. Finally, a combined system of NURBSLAM and ASTRO are brought together and tested against the state-of-the-art in a novel simulation environment to prove the concept that a single 3D representation can be used for localisation, mapping, and planning
SMUG Planner: A Safe Multi-Goal Planner for Mobile Robots in Challenging Environments
Robotic exploration or monitoring missions require mobile robots to
autonomously and safely navigate between multiple target locations in
potentially challenging environments. Currently, this type of multi-goal
mission often relies on humans designing a set of actions for the robot to
follow in the form of a path or waypoints. In this work, we consider the
multi-goal problem of visiting a set of pre-defined targets, each of which
could be visited from multiple potential locations. To increase autonomy in
these missions, we propose a safe multi-goal (SMUG) planner that generates an
optimal motion path to visit those targets. To increase safety and efficiency,
we propose a hierarchical state validity checking scheme, which leverages
robot-specific traversability learned in simulation. We use LazyPRM* with an
informed sampler to accelerate collision-free path generation. Our iterative
dynamic programming algorithm enables the planner to generate a path visiting
more than ten targets within seconds. Moreover, the proposed hierarchical state
validity checking scheme reduces the planning time by 30% compared to pure
volumetric collision checking and increases safety by avoiding high-risk
regions. We deploy the SMUG planner on the quadruped robot ANYmal and show its
capability to guide the robot in multi-goal missions fully autonomously on
rough terrain
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