Design and Validation of a Multi-Arm Relocatable Manipulator for Space Applications

This work presents the computational design and validation of the Multi-Arm Relocatable Manipulator (MARM), a three-limb robot for space applications, with particular reference to the MIRROR (i.e., the Multi-arm Installation Robot for Readying ORUs and Reflectors) use-case scenario as proposed by the European Space Agency. A holistic computational design and validation pipeline is proposed, with the aim of comparing different limb designs, as well as ensuring that valid limb candidates enable MARM to perform the complex loco-manipulation tasks required. Motivated by the task complexity in terms of kinematic reachability, (self)-collision avoidance, contact wrench limits, and motor torque limits affecting Earth experiments, this work leverages on multiple state-of-art planning and control approaches to aid the robot design and validation. These include sampling-based planning on manifolds, non-linear trajectory optimization, and quadratic programs for inverse dynamics computations with constraints. Finally, we present the attained MARM design and conduct preliminary tests for hardware validation through a set of lab experiments.

Prototyping fast and agile motions for legged robots with Horizon

For legged robots to perform agile, highly dynamic and contact-rich motions, whole-body trajectories computation of under-actuated complex systems subject to non-linear dynamics is required. In this work, we present hands-on applications of Horizon, a novel open-source framework for trajectory optimization tailored to robotic systems, that provides a collection of tools to simplify dynamic motion generation. Horizon was tested on a broad range of behaviours involving several robotic platforms: we introduce its building blocks and describe the complete procedure to generate three complex motions using its intuitive and straightforward API.

Loco-Manipulation Planning for Legged Robots: Offline and Online Strategies

The deployment of robots within realistic environments requires the capability to plan and refine the loco-manipulation trajectories on the fly to avoid unexpected interactions with a dynamic environment. This extended abstract provides a pipeline to offline plan a configuration space global trajectory based on a randomized strategy, and to online locally refine it depending on any change of the dynamic environment and the robot state. The offline planner directly plans in the contact space, and additionally seeks for whole-body feasible configurations compliant with the sampled contact states. The planned trajectory, made by a discrete set of contacts and configurations, can be seen as a graph and it can be online refined during the execution of the global trajectory. The online refinement is carried out by a graph optimization planner exploiting visual information. It locally acts on the global initial plan to account for possible changes in the environment. While the offline planner is a concluded work, tested on the humanoid COMAN+, the online local planner is still a work-in-progress which has been tested on a reduced model of the CENTAURO robot to avoid dynamic and static obstacles interfering with a wheeled motion task. Both the COMAN+ and the CENTAURO robots have been designed at the Italian Institute of Technology (IIT).