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

Software Architecture and Development for Controlling a Hubo Humanoid Robot

Due to their human-like structure, humanoid robots are capable of doing some complex tasks. Since a humanoid robot has a large number of actuators and sensors, controlling it is a difficult task. For various tasks like balancing, driving a car, and interacting with humans, real-time response of the robot is essential. Efficiently controlling a humanoid robot requires a software that guarantees real-time interface and control mechanism so that real-time response of the robot is possible. Addition- ally, to reduce the development effort and time, the software should be open-source, multi-lingual and should have high-level constructs inbuilt in it. Currently Robot Operating System (ROS) and Microsoft Robotics Developer Studio (MRDS) are most commonly used software packages for controlling robots. Since ROS uses Transmission Control Protocol (TCP) for inter-process communication, the latency in communication is high. Therefore, if ROS is used, the robot cannot respond in real-time. On the other hand, MRDS is not an open-source but a proprietary soft- ware package. Therefore it cannot be optimized for a particular robot. Thus, there is an urgent need to develop a real-time, open-source, modular, and thin software for controlling humanoid robots. This thesis describes the design and architecture of two software packages developed to fill this gap. It is expected that in the near future a large number of humanoid robots will be used all around the world. The humanoid robots will be used to perform various tasks. The developed software packages have the potential to be the most commonly used software packages for controlling humanoid robots. These packages will assist humans in controlling and monitoring humanoid robots to perform search-and-rescue operations, explore the universe, assist in household chores, etc

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

Representation and control of coordinated-motion tasks for human-robot systems

It is challenging for robots to perform various tasks in a human environment. This is because many human-centered tasks require coordination in both hands and may often involve cooperation with another human. Although human-centered tasks require different types of coordinated movements, most of the existing methodologies have focused only on specific types of coordination. This thesis aims at the description and control of coordinated-motion tasks for human-robot systems; i.e., humanoid robots as well as multi-robot and human-robot systems. First, for bimanually coordinated-motion tasks in dual-manipulator systems, we propose the Extended-Cooperative-Task-Space (ECTS) representation, which extends the existing Cooperative-Task-Space (CTS) representation based on the kinematic models for human bimanual movements in Biomechanics. The proposed ECTS representation can represent the whole spectrum of dual-arm motion/force coordination using two sets of ECTS motion/force variables in a unified manner. The type of coordination can be easily chosen by two meaningful coefficients, and during coordinated-motion tasks, each set of variables directly describes two different aspects of coordinated motion and force behaviors. Thus, the operator can specify coordinated-motion/force tasks more intuitively in high-level descriptions, and the specified tasks can be easily reused in other situations with greater flexibility. Moreover, we present consistent procedures of using the ECTS representation for task specifications in the upper-body and lower-body subsystems of humanoid robots in order to perform manipulation and locomotion tasks, respectively. Besides, we propose and discuss performance indices derived based on the ECTS representation, which can be used to evaluate and optimize the performance of any type of dual-arm manipulation tasks. We show that using the ECTS representation for specifying both dual-arm manipulation and biped locomotion tasks can greatly simplify the motion planning process, allowing the operator to focus on high-level descriptions of those tasks. Both upper-body and lower-body task specifications are demonstrated by specifying whole-body task examples on a Hubo II+ robot carrying out dual-arm manipulation as well as biped locomotion tasks in a simulation environment. We also present the results from experiments on a dual-arm robot (Baxter) for teleoperating various types of coordinated-motion tasks using a single 6D mouse interface. The specified upper- and lower-body tasks can be considered as coordinated motions with constraints. In order to express various constraints imposed across the whole-body, we discuss the modeling of whole-body structure and the computations for robotic systems having multiple kinematic chains. Then we present a whole-body controller formulated as a quadratic programming, which can take different types of constraints into account in a prioritized manner. We validate the whole-body controller based on the simulation results on a Hubo II+ robot performing specified whole-body task examples with a number of motion and force constraints as well as actuation limits. Lastly, we discuss an extension of the ECTS representation, called Hierarchical Extended-Cooperative-Task Space (H-ECTS) framework, which uses tree-structured graphical representations for coordinated-motion tasks of multi-robot and human-robot systems. The H-ECTS framework is validated by experimental results on two Baxter robots cooperating with each other as well as with an additional human partner

Negotiating Large Obstacles with a Humanoid Robot via Multi-Contact Motion Planning

Incremental progress in humanoid robot locomotion over the years has achieved essential capabilities such as navigation over at or uneven terrain, stepping over small obstacles and imbing stairls. However, the locomotion research has mostly been limited to using only bipedal gait and only foot contacts with the environment, using the upper body for balancing without considering additional external contacts. As a result, challenging locomotion tasks like climbing over large obstacles relative to the size of the robot have remained unsolved. In this paper, we address this class of open problems with an approach based on multi-contact motion planning, guided by physical human demonstrations. Our goal is to make humanoid locomotion problem more tractable by taking advantage of objects in the surrounding environment instead of avoiding them. We propose a multi-contact motion planning algorithm for humanoid robot locomotion which exploits the multi-contacts at the upper and lower body limbs. We propose a contact stability measure, which simplies the contact search from demonstration and contact transition motion generation for the multi-contact motion planning algorithm. The algorithm uses the whole-body motions generated via Quadratic Programming (QP) based solver methods. The multi-contact motion planning algorithm is applied for a challenging task of climbing over a relatively larger obstacle compared to the robot. We validate our planning approach with simulations and experiments for climbing over a large wooden obstacle with COMAN, which is a complaint humanoid robot with 23 degrees of freedom (DOF). We also propose a generalization method, the \Policy-Contraction Learning Method" to extend the algorithm for generating new multi-contact plans for our multi-contact motion planner, that can adapt to changes in the environment. The method learns a general policy and the multi-contact behavior from the human demonstrations, for generating new multi-contact plans for the obstacle-negotiation

State Generation Method for Humanoid Motion Planning Based on Genetic Algorithm

A new approach to generate the original motion data for humanoid motion planning is presented in this paper. And a state generator is developed based on the genetic algorithm, which enables users to generate various motion states without using any reference motion data. By specifying various types of constraints such as configuration constraints and contact constraints, the state generator can generate stable states that satisfy the constraint conditions for humanoid robots. To deal with the multiple constraints and inverse kinematics, the state generation is finally simplified as a problem of optimizing and searching. In our method, we introduce a convenient mathematic representation for the constraints involved in the state generator, and solve the optimization problem with the genetic algorithm to acquire a desired state. To demonstrate the effectiveness and advantage of the method, a number of motion states are generated according to the requirements of the motion

Implementation and Integration of Fuzzy Algorithms for Descending Stair of KMEI Humanoid Robot

Locomotion of humanoid robot depends on the mechanical characteristic of the robot. Walking on descending stairs with integrated control systems for the humanoid robot is proposed. The analysis of trajectory for descending stairs is calculated by the constrains of step length stair using fuzzy algorithm. The established humanoid robot on dynamically balance on this matter of zero moment point has been pretended to be consisting of single support phase and double support phase. Walking transition from single support phase to double support phase is needed for a smooth transition cycle. To accomplish the problem, integrated motion and controller are divided into two conditions: motion working on offline planning and controller working online walking gait generation. To solve the defect during locomotion of the humanoid robot, it is directly controlled by the fuzzy logic controller. This paper verified the simulation and the experiment for descending stair of KMEI humanoid robot.&nbsp

Multi-contact planning and control for humanoid robots: Design and validation of a complete framework

In this paper, we consider the problem of generating appropriate motions for a torque- controlled humanoid robot that is assigned a multi-contact loco-manipulation task, i.e., a task that requires the robot to move within the environment by repeatedly establishing and breaking multiple, non-coplanar contacts. To this end, we present a complete multi-contact planning and control framework for multi-limbed robotic systems, such as humanoids. The planning layer works offline and consists of two sequential modules: first, a stance planner computes a sequence of feasible contact combinations; then, a whole-body planner finds the sequence of collision-free humanoid motions that realize them while respecting the physical limitations of the robot. For the challenging problem posed by the first stage, we propose a novel randomized approach that does not require the specification of pre-designed potential contacts or any kind of pre-computation. The control layer produces online torque commands that enable the humanoid to execute the planned motions while guaranteeing closed-loop balance. It relies on two modules, i.e., the stance switching and reactive balancing module; their combined action allows it to withstand possible execution inaccuracies, external disturbances, and modeling uncertainties. Numerical and experimental results obtained on COMAN+, a torque-controlled humanoid robot designed at Istituto Italiano di Tecnologia, validate our framework for loco-manipulation tasks of different complexity