Shintaro Noda

Development of life-sized high-power humanoid robot JAXON for real-world use

This paper presents the development of life-sized high-power humanoid robot JAXON. Humanoid robots for disaster relief assistance need the same degree of physical performance as humans. We have developed STARO as the high-power humanoid robot with a high degree of physical performance. However this is not enough for practical use of the humanoid robot in a disaster site. We consider the following as additional conditions to operate humanoid robots for disaster relief assistance outside of the lab in outdoor environments. 1) Robots have humanlike body proportion to work in infrastructure matched to human body structure. 2) Robots have energy sources such as batteries and act without tethers. 3) Robots walk with two legs or four limbs and continue to work without fatal damage in unexpected rollover. JAXON satisfied these conditions. We demonstrates the performance of JAXON through the experiment of getting out of a vehicle, stepping over walls, and operating on batteries. Further more, we assesses the performance of the strong armor and the shock absorbing structure through a backward over-turning accident.

Generating whole-body motion keep away from joint torque, contact force, contact moment limitations enabling steep climbing with a real humanoid robot

For humanoid robots to perform whole-body motions, a motion planner should generate feasible motions satisfying various constraints including joint torque limitation, friction, balancing, collision, and so on. Furthermore, for life-size humanoid robots to perform higher-load motions, such as climbing ladders, safely, it is important to generate motions which requirements are not too close to the limitations. In this paper, we propose a humanoid motion planner based on Body Retention Load Vector (BRLV), which is a novel index for representing severity of physical constraints: limitation of joint Torque, contact Force, and contact Moment (TFM limitations). By minimizing the norm of BRLV, we obtain humanoid motions that are farthest from TFM limitations. Finally, we evaluate the proposed motion planner in simulation and confirm the effectiveness of the planner through experiments in which a life-size humanoid robot climbs a ladder and a car.

Manipulation strategy decision and execution based on strategy proving operation for carrying large and heavy objects

In case that a robot carries large and heavy objects with unknown physical parameters such as mass automatically, the autonomous decision and execution of the manipulation strategy are necessary. The method to decide the proper strategy from the various candidates depending on the object is a difficult problem and not researched widely. We consider the operation as the mapping from the physical parameter space to the object motion space. Based on the concept of mapping, we define the strategy proving operation (SPO) for determination of strategy feasibility. We introduce two examples of SPO and construct the system for deciding strategy from lifting, pushing, and pivoting. Executing the strategy in the situation that physical parameters are not known is also necessary. We construct the generator and controller for the full-body manipulation, which can be employed regardless of strategy. The controller enables the robot to exert adequate force while keeping balance. We clarify the applicable scope of the proposed method and show that a life-sized humanoid decides the strategy and carries various large and heavy objects autonomously through the experiment.

Development of humanoid robot system for disaster response through team NEDO-JSK's approach to DARPA Robotics Challenge Finals

This paper presents Team NEDO-JSKs approach to the development of novel humanoid platform for disaster response through participation to DARPA Robotics Challenge Finals. This development is a part of the project organized by New Energy and Industrial Technology Development Organization. Technology for this robot is based on the recent research of high-speed and high-torque motor driver with water-cooling system, RTM-ROS inter-operation for intelligent robotics, and generation of full-body fast dancing motion, due to the generic 10 years research of HRP-2 as a platform humanoid robot. Development target is the robot support in a variety of unsafe human tasks teleoperated by humans in case of a disaster response, equipped with body structure capability for use of human devices and tools in human environment, performance for dynamic full-body actions covering human-sized speed and power, and basic function for intelligent and integrated robot platform system for performing various tasks independently. we also describes NEDO-JSK teams approach to design methodology for robot hardware and architecture of software system and user interface for DRC Finals as a test case of disaster response.

Multi-layered real-time controllers for humanoid's manipulation and locomotion tasks with emergency stop

This paper describes a practical method to construct real-time controllers to achieve locomotion and manipulation tasks with a humanoid robot. We propose a method to insert emergency stop functionality to each layer to avoid robots falling down and joint overloads even if recognition and planning error exist. We explain implementation of multi-layered real-time controllers on HRP2 robot and application to several manipulation and locomotion tasks. Finally, we evaluate emergency stop functionality in several manipulation tasks.

Online estimation of object-environment constraints for planning of humanoid motion on a movable object

This paper shows a method for achieving multi-contact motion for a humanoid robot on a movable object, such as climbing of a stepladder. Recent research has developed methods for achieving multi-contact motion that considers various constraints, such as joint limits, torques, balance constraints, reachability, and collision avoidance. In addition to these constraints, Motion On a Movable Object (MOMO) has the following features: it has to consider an objects balance during the changing of contact points; and it has to handle scenarios where the mass properties of an object are unknown. In this paper, in order to achieve a humanoid robot having MOMO, we propose balance constraints that consider the constraints imposed by an object as well as an online estimation of objects constraints. First, we use object-environment constraints as the robots constraints, and then we show a method for estimating them based on information provided by the robots sensors. Next, we show a method for applying the balance constraints to a humanoid motion planner and for executing planned motion with real-time sensor feedback controller. Finally, we evaluate our proposed method through experiments in which a life-sized humanoid robot climbs stepladders that have unknown mass properties.

Achievement of recognition guided teleoperation driving system for humanoid robots with vehicle path estimation

In the wake of the DARPA Robotics Challenge, the task for robots to drive vehicles has been expected to be a method for robots to transport themselves to the disaster site where it is hazardous for humans to approach. In the driving task, it is important for the robot to estimate the path of the vehicle and select an appropriate path for navigation through unknown obstacles, even under limited communication with an operator. It is also necessary for robots to suggest the estimated path of vehicle to an operator to deal with unforeseen circumstances. Therefore, we propose a recognition guided teleoperated driving system for robots to drive vehicles in disaster sites with estimated vehicle path based on steering angle and vehicle model. First, we show model based steering and pedaling strategy to achieve the target steering angle for desired path. Next, we propose a vehicle path estimation and a local planner that can suggest a traveling path according to the surroundings. We integrated them into a teleoperation system for bandwidth limited environments as recognition guidance. Finally, we show the effectiveness of our driving system by conducting field driving experiments with three different robots: JAXON, STARO and HRP-2.

Whole-body aerial manipulation by transformable multirotor with two-dimensional multilinks

In this paper, we introduce the achievement of the aerial manipulation by using the whole body of a transformable aerial robot, instead of attaching an additional manipulator. The aerial robot in our work is composed by two-dimensional multilinks which enable a stable aerial transformation and can be employed as an entire gripper. We propose a planning method to find the optimized grasping form for the multilinks while they are on the air, which is based on the original planar enveloping algorithm, along with the optimization of the internal force and joint torque for the force-closure. We then propose the aerial approach and grasp motion strategy, which is devoted to the determination of the form and position of the aerial robot to approach and grasp effectively the object from the air. Finally we present the experimental results of the aerial manipulation which involves grasping, carrying and dropping different types of object. These results validate the performance of aerial grasping based on our proposed whole-body grasp planning and motion control method.

Whole body joint load reduction control for high-load tasks of humanoid robot through adapting joint torque limitation based on online joint temperature estimation

In the field of assistive and disaster response robotics, robots must perform long term or momentary high-load tasks, such as holding heavy objects or climbing up and descending from a high step in environments with a lot of disturbances. In such cases, the robot joints could potentially break due to an unintended load during high-load tasks in environments where detection of contact forces is difficult. In this paper, we propose a method for reducing the loads on the joints by limiting the joint torque dynamically based on temperature estimation of these joints. Since joint failure is caused by overheating of a motor, it is important to guarantee that a joint motor temperature remains within the safe limits. Our joint load reduction control is essentially a torque limitation method which bases on adapting the maximum torque given the temperature predictions. Such predictions are extracted from the motor thermal model. To do so, we establish a relationship between the joint temperature and the joint torque. The robot uses such relationship to predict the temperature rise as well as the maximum allowable torque. Next, this maximum torque is feeded to a torque controller in order to achieve load reduction. We experimentally tested our method and confirmed that specific high-load tasks can be achieved even in environments where unintended loads occur.

Online maintaining behavior of high-load and unstable postures based on whole-body load balancing strategy with thermal prediction.

Whole-body motions involving contacts in some complex terrains may become sometimes high-load and unstable ones. These kinds of motions have a novel problem not considered in the previous studies: integration of load. For example, in the situation of a electric motor subjected to some high loads, motor temperature will increase as integrating loads, and finally, fatal accident such as melting of motor coils would happen. In this study, we introduce a novel behavior control strategy for load balancing that the robot moves whole body during execution time in order to balance whole-body load while keeping contacts with environment. It is a well-known strategy for behavior control and motion generation to control the center of gravity position of robots for balancing. Besides, in the recent studies, optimization methods are also used for the purpose of satisfying constraints for posture maintenance and achieving more complex contact states. The important point of these optimization strategies is how to design the objective function. We designed the objective function as a barrier function of physical constraints and a function which is more larger with more higher motor temperature. By controlling the center of gravity according to this objective function, it is possible to achieve both load balancing and keeping complex contact states such as balancing on a ladder. At last, we confirm the effectiveness of our strategy for the long-sustained maintenance of high-load and unstable postures through three experiments of posture maintenance: crouching posture on a flat ground, balancing posture on a ladder and crawling posture on a flat ground.