Footstep and Motion Planning in Semi-unstructured Environments Using Randomized Possibility Graphs

Traversing environments with arbitrary obstacles poses significant challenges for bipedal robots. In some cases, whole body motions may be necessary to maneuver around an obstacle, but most existing footstep planners can only select from a discrete set of predetermined footstep actions; they are unable to utilize the continuum of whole body motion that is truly available to the robot platform. Existing motion planners that can utilize whole body motion tend to struggle with the complexity of large-scale problems. We introduce a planning method, called the "Randomized Possibility Graph", which uses high-level approximations of constraint manifolds to rapidly explore the "possibility" of actions, thereby allowing lower-level motion planners to be utilized more efficiently. We demonstrate simulations of the method working in a variety of semi-unstructured environments. In this context, "semi-unstructured" means the walkable terrain is flat and even, but there are arbitrary 3D obstacles throughout the environment which may need to be stepped over or maneuvered around using whole body motions.Comment: Accepted by IEEE International Conference on Robotics and Automation 201

Traversing Environments Using Possibility Graphs for Humanoid Robots

Locomotion for legged robots poses considerable challenges when confronted by obstacles and adverse environments. Footstep planners are typically only designed for one mode of locomotion, but traversing unfavorable environments may require several forms of locomotion to be sequenced together, such as walking, crawling, and jumping. Multi-modal motion planners can be used to address some of these problems, but existing implementations tend to be time-consuming and are limited to quasi-static actions. This paper presents a motion planning method to traverse complex environments using multiple categories of actions. We introduce the concept of the "Possibility Graph", which uses high-level approximations of constraint manifolds to rapidly explore the "possibility" of actions, thereby allowing lower-level single-action motion planners to be utilized more efficiently. We show that the Possibility Graph can quickly find paths through several different challenging environments which require various combinations of actions in order to traverse

Probabilistic Completeness of Randomized Possibility Graphs Applied to Bipedal Walking in Semi-unstructured Environments

We present a theoretical analysis of a recent whole body motion planning method, the Randomized Possibility Graph, which uses a high-level decomposition of the feasibility constraint manifold in order to rapidly find routes that may lead to a solution. These routes are then examined by lower-level planners to determine feasibility. In this paper, we show that this approach is probabilistically complete for bipedal robots performing quasi-static walking in "semi-unstructured" environments. Furthermore, we show that the decomposition into higher and lower level planners allows for a considerably higher rate of convergence in the probability of finding a solution when one exists. We illustrate this improved convergence with a series of simulated scenarios

Traversing Environments Using Possibility Graphs for Humanoid Robots

Locomotion for legged robots poses considerable challenges when confronted by obstacles and adverse environments. Footstep planners are typically only designed for one mode of locomotion, but traversing unfavorable environments may require several forms of locomotion to be sequenced together, such as walking, crawling, and jumping. Multi-modal motion planners can be used to address some of these problems, but existing implementations tend to be time-consuming and are limited to quasi-static actions. This paper presents a motion planning method to traverse complex environments using multiple categories of actions. We introduce the concept of the "Possibility Graph", which uses high-level approximations of constraint manifolds to rapidly explore the "possibility" of actions, thereby allowing lower-level single-action motion planners to be utilized more efficiently. We show that the Possibility Graph can quickly find paths through several different challenging environments which require various combinations of actions in order to traverse

Mice Deficient in GEM GTPase Show Abnormal Glucose Homeostasis Due to Defects in Beta-Cell Calcium Handling

Glucose-stimulated insulin secretion from beta-cells is a tightly regulated process that requires calcium flux to trigger exocytosis of insulin-containing vesicles. Regulation of calcium handling in beta-cells remains incompletely understood. Gem, a member of the RGK (Rad/Gem/Kir) family regulates calcium channel handling in other cell types, and Gem over-expression inhibits insulin release in insulin-secreting Min6 cells. The aim of this study was to explore the role of Gem in insulin secretion. We hypothesised that Gem may regulate insulin secretion and thus affect glucose tolerance in vivo

Work those arms: Toward dynamic and stable humanoid walking that optimizes full-body motion

Humanoid robots are designed with dozens of actuated joints to suit a variety of tasks, but walking controllers rarely make the best use of all of this freedom. We present a framework for maximizing the use of the full humanoid body for the purpose of stable dynamic locomotion, which requires no restriction to a planning template (e.g. LIPM). Using a hybrid zero dynamics (HZD) framework, this approach optimizes a set of outputs which provides requirements for the motion for all actuated links, including arms. These output equations are then rapidly solved by a whole-body inverse-kinematic (IK) solver, providing a set of joint trajectories to the robot. We apply this procedure to a simulation of the humanoid robot, DRC-HUBO, which has over 27 actuators. As a consequence, the resulting gaits swing their arms, not by a user defining swinging motions a priori or superimposing them on gaits post hoc, but as an emergent behavior from optimizing the dynamic gait. We also present preliminary dynamic walking experiments with DRC-HUBO in hardware, thereby building a case that hybrid zero dynamics as augmented by inverse kinematics (HZD+IK) is becoming a viable approach for controlling the full complexity of humanoid locomotion

Work those Arms: Toward Dynamic and Stable Humanoid Walking that Optimizes Full-Body Motion

© 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.DOI: 10.1109/ICRA.2016.7487293Humanoid robots are designed with dozens of actuated joints to suit a variety of tasks, but walking controllers rarely make the best use of all of this freedom. We present a framework for maximizing the use of the full humanoid body for the purpose of stable dynamic locomotion, which requires no restriction to a planning template (e.g. LIPM). Using a hybrid zero dynamics (HZD) framework, this approach optimizes a set of outputs which provides requirements for the motion for all actuated links, including arms. These output equations are then rapidly solved by a whole-body inverse-kinematic (IK) solver, providing a set of joint trajectories to the robot. We apply this procedure to a simulation of the humanoid robot, DRC-HUBO, which has over 27 actuators. As a consequence, the resulting gaits swing their arms, not by a user defining swinging motions a priori or superimposing them on gaits post hoc, but as an emergent behavior from optimizing the dynamic gait. We also present preliminary dynamic walking experiments with DRC-HUBO in hardware, thereby building a case that hybrid zero dynamics as augmented by inverse kinematics (HZD+IK) is becoming a viable approach for controlling the full complexity of humanoid locomotion

Humanoid manipulation planning using backward-forward search

This paper explores combining task and manipulation planning for humanoid robots. Existing methods tend to either take prohibitively long to compute for humanoids or artificially limit the physical capabilities of the humanoid platform by restricting the robot's actions to predetermined trajectories. We present a hybrid planning system which is able to scale well for complex tasks without relying on predetermined robot actions. Our system utilizes the hybrid backward-forward planning algorithm for high-level task planning combined with humanoid primitives for standing and walking motion planning. These primitives are designed to be efficiently computable during planning, despite the large amount of complexity present in humanoid robots, while still informing the task planner of the geometric constraints present in the problem. Our experiments apply our method to simulated pick-and-place problems with additional gate constraints impacting navigation using the DRC-HUBO1 robot. Our system is able to solve puzzle-like problems on a humanoid within a matter of minutes

Humanoid manipulation planning using backward-forward search

This paper explores combining task and manipulation planning for humanoid robots. Existing methods tend to either take prohibitively long to compute for humanoids or artificially limit the physical capabilities of the humanoid platform by restricting the robot's actions to predetermined trajectories. We present a hybrid planning system which is able to scale well for complex tasks without relying on predetermined robot actions. Our system utilizes the hybrid backward-forward planning algorithm for high-level task planning combined with humanoid primitives for standing and walking motion planning. These primitives are designed to be efficiently computable during planning, despite the large amount of complexity present in humanoid robots, while still informing the task planner of the geometric constraints present in the problem. Our experiments apply our method to simulated pick-and-place problems with additional gate constraints impacting navigation using the DRC-HUBO1 robot. Our system is able to solve puzzle-like problems on a humanoid within a matter of minutes