Critical Timescales for Burrowing in Undersea Substrates via Localized Fluidization, Demonstrated by RoboClam: A Robot Inspired by Atlantic Razor Clams

The Atlantic razor clam (Ensis directus) burrows into underwater soil by using motions of its shell to locally fluidize the surrounding substrate. The energy associated with movement through fluidized soil — characterized by a depth-independent density and viscosity — scales linearly with depth. In contrast, moving through static soil requires energy that scales with depth squared. For E. directus, this translates to a 10X reduction in the energy required to reach observed burrow depths. For engineers, localized fluidization offers a mechanically simple and purely kinematic method to dramatically reduce burrowing energy. This concept is demonstrated with RoboClam, an E. directus-inspired robot. Using a genetic algorithm to generate digging kinematics, RoboClam has achieved localized fluidization and burrowing performance comparable to that of the animal, with a linear energy-depth relationship. In this paper, we present the critical timescales and associated kinematics necessary for achieving localized fluidization, which are calculated from soil parameters and validated via RoboClam and E. directus testing.Battelle Memorial InstituteBluefin RoboticsChevron Corporatio

Approximate hybrid model predictive control for multi-contact push recovery in complex environments

Feedback control of robotic systems interacting with the environment through contacts is a central topic in legged robotics. One of the main challenges posed by this problem is the choice of a model sufficiently complex to capture the discontinuous nature of the dynamics but simple enough to allow online computations. Linear models have proved to be the most effective and reliable choice for smooth systems; we believe that piecewise affine (PWA) models represent their natural extension when contact phenomena occur. Discrete-time PWA systems have been deeply analyzed in the field of hybrid Model Predictive Control (MPC), but the straightforward application of MPC techniques to complex systems, such as a humanoid robot, leads to mixed-integer optimization problems which are not solvable at real-time rates. Explicit MPC methods can construct the entire control policy offline, but the resulting policy becomes too complex to compute for systems at the scale of a humanoid robot. In this paper we propose a novel algorithm which splits the computational burden between an offline sampling phase and a limited number of online convex optimizations, enabling the application of hybrid predictive controllers to higher-dimensional systems. In doing so we are willing to partially sacrifice feedback optimality, but we set stability of the system as an inviolable requirement. Simulation results of a simple planar humanoid that balances by making contact with its environment are presented to validate the proposed controller

Toward a Probabilistic Approach to Acquiring Information from Human Partners Using Language

Our goal is to build robots that can robustly interact with humans using natural language. This problem is extremely challenging because human language is filled with ambiguity, and furthermore, the robot's model of the environment might be much more limited than the human partner. When humans encounter ambiguity in dialog with each other, a key strategy to resolve it is to ask clarifying questions about whatthey do not understand. This paper describes an approach for enabling robots to take the same approach: asking the human partner clarifying questions about ambiguous commands in order to infer better actions. The robot fuses information from the command, the question, and the answer by creating a joint probabilistic graphical model in the Generalized Grounding Graph framework. We demonstrate that by performing inference using information from the command, question and answer, the robot is able to infer object groundings and follow commands with higher accuracythan by using the command alone

The Design and Testing of RoboClam: A Machine Used to Investigate and Optimize Razor Clam-Inspired Burrowing Mechanisms for Engineering Applications

Razor clams (Ensis directus) are one of nature’s most adept burrowing organisms, able to dig to 70cm at nearly 1cm/s using only 0.21J/cm. Ensis reduces burrowing drag by using motions of its shell to fluidize a thin layer of substrate around its body. Although these shell motions have an energetic cost, moving through fluidized rather than packed soil results in exponentially lower overall energy consumption. This paper describes the design and testing of RoboClam, a device that mimics Ensis digging methods to understand the limits of razor clam-inspired burrowing, how they scale for different environments and conditions, and how they can be transferred into engineering applications. Using a genetic optimization solver, we found that RoboClam’s most efficient digging motion mimicked Ensis shell kinematics and yielded a power law relationship between digging energy and depth of n = 1.17, very close to the ideal value of n = 1. Pushing through static soil has a theoretical energy-depth power law of n = 2, which means that Ensis-inspired burrowing motions can provide exponentially higher energy efficiency and nearly depth-independent drag resistance.Battelle Memorial InstituteBluefin RoboticsChevron Corporatio

Razor clam to RoboClam: burrowing drag reduction mechanisms and their robotic adaptation

Estimates based on the strength, size, and shape of the Atlantic razor clam (Ensis directus) indicate that the animal's burrow depth should be physically limited to a few centimeters; yet razor clams can dig as deep as 70 cm. By measuring soil deformations around burrowing E. directus, we have found the animal reduces drag by contracting its valves to initially fail, and then fluidize, the surrounding substrate. The characteristic contraction time to achieve fluidization can be calculated directly from soil properties. The geometry of the fluidized zone is dictated by two commonly-measured geotechnical parameters: coefficient of lateral earth pressure and friction angle. Calculations using full ranges for both parameters indicate that the fluidized zone is a local effect, occurring between 1–5 body radii away from the animal. The energy associated with motion through fluidized substrate—characterized by a depth-independent density and viscosity—scales linearly with depth. In contrast, moving through static soil requires energy that scales with depth squared. For E. directus, this translates to a 10X reduction in the energy required to reach observed burrow depths. For engineers, localized fluidization offers a mechanically simple and purely kinematic method to dramatically reduce energy costs associated with digging. This concept is demonstrated with RoboClam, an E. directus-inspired robot. Using a genetic algorithm to find optimal digging kinematics, RoboClam has achieved localized fluidization burrowing performance comparable to that of the animal, with a linear energy-depth relationship, in both idealized granular glass beads and E. directus' native cohesive mudflat habitat.Battelle Memorial InstituteBluefin RoboticsChevron Corporatio

Multi-Substrate Burrowing Performance and Constitutive Modeling of RoboClam: A Biomimetic Robot Based on Razor Clams

The Atlantic razor clam (Ensis directus) reduces burrowing drag by using motions of its shell to fluidize a thin layer of substrate around its body. We have developed RoboClam, a robot that digs using the same mechanisms as Ensis, to explore how localized fluidization burrowing can be extended to engineering applications. In this work we present burrowing performance results of RoboClam in two distinctly different substrates: ideally granular 1mm soda lime glass beads and cohesive ocean mudflat soil. Using a genetic algorithm to optimize RoboClam’s kinematics, the machine was able to burrow in both substrates with a power law relationship between digging energy and depth of n = 1.17. Pushing through static soil has a theoretical energy-depth power law of n = 2, which means that Ensis-inspired burrowing motions can provide exponentially higher energy efficiency. We propose a theoretical constitutive model that describes how a fluidized region should form around a contracting body in virtually any type of saturated soil. The model predicts fluidization to be a relatively local effect, extending only two to three characteristic lengths away from the body, depending on friction angle and coefficient of lateral earth pressure, two commonly measured soil parameters.Battelle Memorial InstituteBluefin RoboticsChevron Corporatio

Continuous Humanoid Locomotion over Uneven Terrain using Stereo Fusion

Abstract — For humanoid robots to fulfill their mobility po-tential they must demonstrate reliable and efficient locomotion over rugged and irregular terrain. In this paper we present the perception and planning algorithms which have allowed a humanoid robot to use only passive stereo imagery (as opposed to actuating a laser range sensor) to safely plan footsteps to continuously walk over rough and uneven surfaces without stopping. The perception system continuously integrates stereo imagery to build a consistent 3D model of the terrain which is then used by our footstep planner which reasons about obstacle avoidance, kinematic reachability and foot rotation through mixed-integer quadratic optimization to plan the required step positions. We illustrate that our stereo imagery fusion approach can measure the walking terrain with sufficient accuracy that it matches the quality of terrain estimates from LIDAR. To our knowledge this is the first such demonstration of the use of computer vision to carry out general purpose terrain estimation on a locomoting robot — and additionally to do so in continuous motion. A particular integration challenge was ensuring that these two computationally intensive systems oper-ate with minimal latency (below 1 second) to allow re-planning while walking. The results of extensive experimentation and quantitative analysis are also presented. Our results indicate that a laser range sensor is not necessary to achieve locomotion in these challenging situations. I

Optimization-based locomotion planning, estimation, and control design for the atlas humanoid robot

This paper describes a collection of optimization algorithms for achieving dynamic planning, control, and state estimation for a bipedal robot designed to operate reliably in complex environments. To make challenging locomotion tasks tractable, we describe several novel applications of convex, mixed-integer, and sparse nonlinear optimization to problems ranging from footstep placement to whole-body planning and control. We also present a state estimator formulation that, when combined with our walking controller, permits highly precise execution of extended walking plans over non-flat terrain. We describe our complete system integration and experiments carried out on Atlas, a full-size hydraulic humanoid robot built by Boston Dynamics, Inc.United States. Air Force Office of Scientific Research (FA8750-12-1-0321)United States. Office of Naval Research (N00014-12-1-0071)United States. Office of Naval Research (N00014-10-1-0951)National Science Foundation (U.S.) (IIS-0746194)National Science Foundation (U.S.) (IIS-1161909

An Architecture for Online Affordance-based Perception and Whole-body Planning

The DARPA Robotics Challenge Trials held in December 2013 provided a landmark demonstration of dexterous mobile robots executing a variety of tasks aided by a remote human operator using only data from the robot's sensor suite transmitted over a constrained, field-realistic communications link. We describe the design considerations, architecture, implementation and performance of the software that Team MIT developed to command and control an Atlas humanoid robot. Our design emphasized human interaction with an efficient motion planner, where operators expressed desired robot actions in terms of affordances fit using perception and manipulated in a custom user interface. We highlight several important lessons we learned while developing our system on a highly compressed schedule