Chaohui Gong

Tail use improves performance on soft substrates in models of early vertebrate land locomotors

Animal and robot experiments explore the use of a tail in aiding terrestrial locomotion. In the evolutionary transition from an aquatic to a terrestrial environment, early tetrapods faced the challenges of terrestrial locomotion on flowable substrates, such as sand and mud of variable stiffness and incline. The morphology and range of motion of appendages can be revealed in fossils; however, biological and robophysical studies of modern taxa have shown that movement on such substrates can be sensitive to small changes in appendage use. Using a biological model (the mudskipper), a physical robot model, granular drag measurements, and theoretical tools from geometric mechanics, we demonstrate how tail use can improve robustness to variable limb use and substrate conditions. We hypothesize that properly coordinated tail movements could have provided a substantial benefit for the earliest vertebrates to move on land.

Kinematic gait synthesis for snake robots

Snake robots are highly articulated mechanisms that can perform a variety of motions that conventional robots cannot. Despite many demonstrated successes of snake robots, these mechanisms have not been able to achieve the agility displayed by their biological counterparts. We suggest that studying how biological snakes coordinate whole-body motion to achieve agile behaviors can help improve the performance of snake robots. The foundation of this work is based on the hypothesis that, for snake locomotion that is approximately kinematic, replaying parameterized shape trajectory data collected from biological snakes can generate equivalent motions in snake robots. To test this hypothesis, we collected shape trajectory data from sidewinder rattlesnakes executing a variety of different behaviors. We then analyze the shape trajectory data in a concise and meaningful way by using a new algorithm, called conditioned basis array factorization, which projects high-dimensional data arrays onto a low-dimensional representation. The low-dimensional representation of the recorded snake motion is able to reproduce the essential features of the recorded biological snake motion on a snake robot, leading to improved agility and maneuverability, confirming our hypothesis. This parameterized representation allows us to search the low-dimensional parameter space to generate behaviors that further improve the performance of snake robots.

Extended gait equation for sidewinding

Sidewinding is an efficient translational gait used by biological snakes to locomote over flat ground. Prior work has identified the fact that it is possible to steer the moving direction of sidewinding. The previously proposed virtual tread model reveals the working principal of sidewinding from a geometric point of view. Unfortunately, the implementation of the virtual tread model relied on a computationally expensive numerical fitting algorithm that impeded online applications. Motivated by this limitation, in this work we propose a novel approach to develop analytical expressions for snake robot gaits based on the study of the corresponding geometric model. This approach is rooted in the identification of dominant frequency components afforded by the two-dimensional Fast Fourier Transformation (FFT). Applying this method to the virtual tread model for conical sidewinding, we derive an analytical expression between the parameters that describe the gaits motion and the turning radius of the system moving in the world. This analytical expression, which we call the extended gait equation, is verified by experimental results.

Visual sensing for developing autonomous behavior in snake robots

Snake robots are uniquely qualified to investigate a large variety of settings including archaeological sites, natural disaster zones, and nuclear power plants. For these applications, modular snake robots have been tele-operated to perform specific tasks using images returned to it from an onboard camera in the robots head. In order to give the operator an even richer view of the environment and to enable the robot to perform autonomous tasks we developed a structured light sensor that can make three-dimensional maps of the environment. This paper presents a sensor that is uniquely qualified to meet the severe constraints in size, power and computational footprint of snake robots. Using range data, in the form of 3D pointclouds, we show that it is possible to pair high-level planning with mid-level control to accomplish complex tasks without operator intervention.

Snakes on a plan: Toward combining planning and control

Highly articulated robot locomotion systems, such as snake robots, present special motion planning challenges. They possess many degrees of freedom, and therefore are modeled by a high dimensional configuration space which must be searched to plan a path. Kinematic and dynamic constraints further complicate the selection of effective controls. Finally, snake robots often have multiple modes of interaction with the terrain as contacts are made and broken, leading to complex and imperfect motion models. We believe that the space of useful controls that provides desirable motions, however, is much smaller. Useful net motions for such systems are often generated via gaits, or cyclic motions in the shape space. Gaits transform a high-dimensional continuum search into a relatively tractable discrete search. In this paper, we put forward a framework which allows a planner to generate paths in a low dimensional work space and select among gaits, pre-planned motions in the robots shape space. The contribution of this paper rests on the “virtual chassis” which is a choice of body frame for the snake robot that allows the planner to efficiently select among and plan with gaits to direct the robot along the work space path. We demonstrate this planner running on a simulated snake robot navigating through a variety of clutter scenarios. The virtual chassis also has the benefit of allowing us to generalize notions of controllability to gait motions.

Modeling rolling gaits of a snake robot

Successful deployment of a snake robot in search and rescue tasks requires the capability of generating controls which can adapt to unknown environments in real-time. However, available motion generation techniques can be computationally expensive and lack the ability to adapt to the surroundings. This work considers modeling the rolling motion of a snake robot by applying the Bellows model with computation reduction techniques. One benefit of this is that controllers are defined with physically meaningful parameters, which in turn allows for higher level control of the robot. Another benefit is that it allows controllers to be defined by “composing shapes”, which enables developing controllers that can adapt to the surroundings. Using shape composition, we implemented a novel gait, named rolling hump, which forms a contour-fitting hump to negotiate obstacles. The efficacy of a snake robot climbing over obstacles by using the rolling hump is experimentally evaluated. An autonomous control strategy is presented and realized in simulation.

Snakes on an inclined plane: Learning an adaptive sidewinding motion for changing slopes

Sidewinding is an efficient gait adopted by biological and robotic snakes for locomoting on various terrains. The mechanics of this motion on flat ground and steady state terrains have been thoroughly investigated, while its capability to adapt to changing environments is not as well studied. We demonstrate the capability of a snake robot to automatically adjust gait parameters to optimally move up and down slopes of varying angle. This capability is achieved by three components. First, an efficient offline learning algorithm finds a policy mapping the estimated slope angle to the optimal gait parameters. Next, a robust online state estimation technique infers the local terrain characteristics. Finally, the precomputed policy is consulted online to select the optimal gait parameters for this slope. The efficacy of this approach is verified by robot experiments.

Simplifying Gait Design via Shape Basis Optimization

Gaits are crucial to the performance of locomotors. However, it is often difficult to design effective gaits for complex locomotors. Geometric mechanics offers powerful gait design tools, but the utilities of these tools have been limited to systems with two joints. Using shape basis functions, it is possible to approximate the kinematics of complex locomotors using only two shape variables. As a result, the tools of geometric mechanics can be used to study complex locomotion in an intuitive way. The choice of shape basis functions plays an important role in determining gait kinematics, and therefore the performance of a locomotor. To find appropriate basis functions, we introduce the shape basis optimization algorithm, an algorithm that iteratively improve basis functions to find effective kinematic programs. Applying this algorithm to a snake robot resulted a novel gait, which improves its speed of swimming in granular materials.

Multi-agent deterministic graph mapping via robot rendezvous

In this paper, we present a novel algorithm for deterministically mapping an undirected graph-like world with multiple synchronized agents. The application of this algorithm is the collective mapping of an indoor environment with multiple mobile robots while leveraging an embedded topological decomposition of the environment. Our algorithm relies on a group of agents that all depart from the same initial vertex in the graph and spread out to explore the graph. A centralized tree of graph hypotheses is maintained to consider loop-closure, which is deterministically verified when agents observe each other at a common vertex. To achieve efficient mapping, we introduce an active exploration method in which agents dynamically request rendezvous tasks from other available agents to validate graph hypotheses.

Limbless locomotors that turn in place

Our research group has started a collaboration that analyzes data collected from biological snakes to provide insight on how to better program snake robots. Most data collected on biological snakes views the snakes from above and thus can only detect motion in the horizontal plane. However, both our robots and biological snakes are capable of generating motions both in the horizontal and vertical planes. Vertical waves naturally play a major role in limbless locomotion in that they simultaneously provide thrust motion and make-and-break contact between the mechanism and environment. Analysis on the data, collected from sidewinder rattle snakes, revealed that disparate modes of locomotion emerged from different contact patterns. We conclude that the same horizontal undulation can cause dramatically different motions for both the biological and robotic snakes depending upon the choice of contacts. With this knowledge, we introduce contact scheduling, a technique that plans positions of contacts along the body to design gaits for snake robots. Contact scheduling results in a novel turning gait, which can reorient a snake robot more than 90 degrees in one gait cycle.