四肢ロボットの安定かつ桟の認識が可能な垂直はしご昇降の運動生成

早大学位記番号:新8018早稲田大

Vertical ladder climbing by the HRP-2 humanoid robot

International audienceWe report the results obtained from our trials in making the HRP-2 humanoid robot climb vertical industrial-norm ladders. We integrated our multi-contact planner and multi-objective QP control as basic components. First, a set of contacts to climb the ladder is planned off-line and provided as an input for a finite state machine that sequences tasks to be realized by our multi-objective model-based QP in closed-loop control. The trials we made revealed that hardware changes are to be made on the HRP-2, and the software has to be made more robust. Yet, we confirmed that HRP-2 has power capability to climb real industrial ladders, such as those found in nuclear power plants and large scale manufacturings (e.g. airliners, shipyards and buildings)

Online, interactive user guidance for high-dimensional, constrained motion planning

We consider the problem of planning a collision-free path for a high-dimensional robot. Specifically, we suggest a planning framework where a motion-planning algorithm can obtain guidance from a user. In contrast to existing approaches that try to speed up planning by incorporating experiences or demonstrations ahead of planning, we suggest to seek user guidance only when the planner identifies that it ceases to make significant progress towards the goal. Guidance is provided in the form of an intermediate configuration $\hat{q}$, which is used to bias the planner to go through $\hat{q}$. We demonstrate our approach for the case where the planning algorithm is Multi-Heuristic A* (MHA*) and the robot is a 34-DOF humanoid. We show that our approach allows to compute highly-constrained paths with little domain knowledge. Without our approach, solving such problems requires carefully-crafting domain-dependent heuristics

Online, interactive user guidance for high-dimensional, constrained motion planning

We consider the problem of planning a collision-free path for a high-dimensional robot. Specifically, we suggest a planning framework where a motion-planning algorithm can obtain guidance from a user. In contrast to existing approaches that try to speed up planning by incorporating experiences or demonstrations ahead of planning, we suggest to seek user guidance only when the planner identifies that it ceases to make significant progress towards the goal. Guidance is provided in the form of an intermediate configuration $\hat{q}$, which is used to bias the planner to go through $\hat{q}$. We demonstrate our approach for the case where the planning algorithm is Multi-Heuristic A* (MHA*) and the robot is a 34-DOF humanoid. We show that our approach allows to compute highly-constrained paths with little domain knowledge. Without our approach, solving such problems requires carefully-crafting domain-dependent heuristics

Design and Implementation of a Humanoid Climbing Robot

芝浦工業大学201

A Stability-Estimator to Unify Humanoid Locomotion: Walking, Stair-Climbing and Ladder-Climbing

The field of Humanoid robotics research has often struggled to find a unique niche that is not better served by other forms of robot. Unlike more traditional industrials robots with a specific purpose, a humanoid robot is not necessarily optimized for any particular task, due to the complexity and balance issues of being bipedal. However, the versatility of a humanoid robot may be ideal for applications such as search and rescue. Disaster sites with chemical, biological, or radiation contamination mean that human rescue workers may face untenable risk. Using a humanoid robot in these dangerous circumstances could make emergency response faster and save human lives. Despite the many successes of existing mobile robots in search and rescue, stair and ladder climbing remains a challenging task due to their form. To execute ladder climbing motions effectively, a humanoid robot requires a reliable estimate of stability. Traditional methods such as Zero Moment Point are not applicable to vertical climbing, and do not account for force limits imposed on end-effectors. This dissertation implements a simple contact wrench space method using a linear combination of contact wrenches. Experiments in simulation showed ZMP equivalence on flat ground. Furthermore, the estimator was able to predict stability with four point contact on a vertical ladder. Finally, an extension of the presented method is proposed based on these findings to address the limitations of the linear combination.Ph.D., Mechanical Engineering and Mechanics -- Drexel University, 201

Offline and Online Planning and Control Strategies for the Multi-Contact and Biped Locomotion of Humanoid Robots

In the past decades, the Research on humanoid robots made progress forward accomplishing exceptionally dynamic and agile motions. Starting from the DARPA Robotic Challenge in 2015, humanoid platforms have been successfully employed to perform more and more challenging tasks with the eventual aim of assisting or replacing humans in hazardous and stressful working situations. However, the deployment of these complex machines in realistic domestic and working environments still represents a high-level challenge for robotics. Such environments are characterized by unstructured and cluttered settings with continuously varying conditions due to the dynamic presence of humans and other mobile entities, which cannot only compromise the operation of the robotic system but can also pose severe risks both to the people and the robot itself due to unexpected interactions and impacts. The ability to react to these unexpected interactions is therefore a paramount requirement for enabling the robot to adapt its behavior to the task needs and the characteristics of the environment. Further, the capability to move in a complex and varying environment is an essential skill for a humanoid robot for the execution of any task. Indeed, human instructions may often require the robot to move and reach a desired location, e.g., for bringing an object or for inspecting a specific place of an infrastructure. In this context, a flexible and autonomous walking behavior is an essential skill, study of which represents one of the main topics of this Thesis, considering disturbances and unfeasibilities coming both from the environment and dynamic obstacles that populate realistic scenarios.  Locomotion planning strategies are still an open theme in the humanoids and legged robots research and can be classified in sample-based and optimization-based planning algorithms. The first, explore the configuration space, finding a feasible path between the start and goal robot’s configuration with different logic depending on the algorithm. They suffer of a high computational cost that often makes difficult, if not impossible, their online implementations but, compared to their counterparts, they do not need any environment or robot simplification to find a solution and they are probabilistic complete, meaning that a feasible solution can be certainly found if at least one exists. The goal of this thesis is to merge the two algorithms in a coupled offline-online planning framework to generate an offline global trajectory with a sample-based approach to cope with any kind of cluttered and complex environment, and online locally refine it during the execution, using a faster optimization-based algorithm that more suits an online implementation. The offline planner performances are improved by planning in the robot contact space instead of the whole-body robot configuration space, requiring an algorithm that maps the two state spaces.   The framework proposes a methodology to generate whole-body trajectories for the motion of humanoid and legged robots in realistic and dynamically changing environments.  This thesis focuses on the design and test of each component of this planning framework, whose validation is carried out on the real robotic platforms CENTAURO and COMAN+ in various loco-manipulation tasks scenarios. &nbsp