This thesis focuses on a generic distributed coordination framework for heterogeneous robots in two type of scenarios: smart factories and search and rescue situations. First, it will be described of the software architecture developed for the Walk-Man robot and used during the Darpa Robotics Challenge. One part of the software used is a novel footstep planner capable of anytime planning and providing statically stable footsteps even in case of rough and irregular terrain. The footstep planner is capable of receiving a desired position or a direction of movements, and allows to command a legged robot with the same controls of a wheeled robot. Such controls are provided by a novel distributed coordination planner that uses a time-space discretization of the environment, and computes deadlock and collision free trajectories for all the robots in communication range. Finally, the distributed planner is extended by using a random sampler for computing the shortest path in time--space without the limits of the discretization approach, i.e. memory and computation costs. The random sampler planner is also tested with ground and flying robots in the same experiments, by using both 2D and 3D state spaces.
Distributed Planning for Legged and Mobile Robots From single footsteps to distributed coordination with a time expanded approach
2016
Abstract
This thesis focuses on a generic distributed coordination framework for heterogeneous robots in two type of scenarios: smart factories and search and rescue situations. First, it will be described of the software architecture developed for the Walk-Man robot and used during the Darpa Robotics Challenge. One part of the software used is a novel footstep planner capable of anytime planning and providing statically stable footsteps even in case of rough and irregular terrain. The footstep planner is capable of receiving a desired position or a direction of movements, and allows to command a legged robot with the same controls of a wheeled robot. Such controls are provided by a novel distributed coordination planner that uses a time-space discretization of the environment, and computes deadlock and collision free trajectories for all the robots in communication range. Finally, the distributed planner is extended by using a random sampler for computing the shortest path in time--space without the limits of the discretization approach, i.e. memory and computation costs. The random sampler planner is also tested with ground and flying robots in the same experiments, by using both 2D and 3D state spaces.File | Dimensione | Formato | |
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https://hdl.handle.net/20.500.14242/130533
URN:NBN:IT:UNIPI-130533