SnakeSIM: a ROS-based control and simulation framework for perception-driven obstacle-aided locomotion of snake robots

Biological snakes are capable of exploiting roughness in the terrain for locomotion. This feature allows them to adapt to different types of environments. Snake robots that can mimic this behaviour could be fitted with sensors and used for transporting tools to hazardous or confined areas that other robots and humans are unable to access. Snake robot locomotion in a cluttered environment where the snake robot utilises a sensory–perceptual system to perceive the surrounding operational environment for means of propulsion can be defined as perception-driven obstacle-aided locomotion (POAL). The initial testing of new control methods for POAL in a physical environment using a real snake robot imposes challenging requirements on both the robot and the test environment in terms of robustness and predictability. This paper introduces SnakeSIM, a virtual rapid-prototyping framework that allows researchers for the design and simulation of POAL more safely, rapidly and efficiently. SnakeSIM is based on the robot operating system (ROS) and it allows for simulating the snake robot model in a virtual environment cluttered with obstacles. The simulated robot can be equipped with different sensors. Tactile perception can be achieved using contact sensors to retrieve forces, torques, contact positions and contact normals. A depth camera can be attached to the snake robot head for visual perception purposes. Furthermore, SnakeSIM allows for exploiting the large variety of robotics sensors that are supported by ROS. The framework can be transparently integrated with a real robot. To demonstrate the potential of SnakeSIM, a possible control approach for POAL is considered as a case study.     

Quadruped robot trotting over irregular terrain assisted by stereo-vision

Legged robots have the potential to navigate in challenging terrain, and thus to exceed the mobility of wheeled vehicles. However, their control is more difficult as legged robots need to deal with foothold computation, leg trajectories and posture control in order to achieve successful navigation. In this paper, we present a new framework for the hydraulic quadruped robot HyQ, which performs goal-oriented navigation on unknown rough terrain using inertial measurement data and stereo-vision. This work uses our previously presented reactive controller framework with balancing control and extends it with visual feedback to enable closed-loop gait adjustment. On one hand, the camera images are used to keep the robot walking towards a visual target by correcting its heading angle if the robot deviates from it. On the other hand, the stereo camera is used to estimate the size of the obstacles on the ground plane and thus the terrain roughness. The locomotion controller then adjusts the step height and the velocity according to the size of the obstacles. This results in a robust and autonomous goal-oriented navigation over difficult terrain while subject to disturbances from the ground irregularities or external forces. Indoor and outdoor experiments with our quadruped robot show the effectiveness of this framework.     

3-D Snake Robot Motion: Nonsmooth Modeling, Simulations, and Experiments

A nonsmooth (hybrid) 3-D mathematical model of a snake robot (without wheels) is developed and experimentally validated in this paper. The model is based on the framework of nonsmooth dynamics and convex analysis that allows us to easily and systematically incorporate unilateral contact forces (i.e., between the snake robot and the ground surface) and friction forces based on Coulomb's law of dry friction. Conventional numerical solvers cannot be employed directly due to set-valued force laws and possible instantaneous velocity changes. Therefore, we show how to implement the model for numerical treatment with a numerical integrator called the time-stepping method. This method helps to avoid explicit changes between equations during simulation even though the system is hybrid. Simulation results for the serpentine motion pattern lateral undulation and sidewinding are presented. In addition, experiments are performed with the snake robot ldquoAikordquo for locomotion by lateral undulation and sidewinding, both with isotropic friction. For these cases, back-to-back comparisons between numerical results and experimental results are given.     

A review on modelling, implementation, and control of snake robots

This paper provides an overview of previous literature on snake robot locomotion. In particular, the paper considers previous research efforts related to modelling of snake robots, physical development of these mechanisms, and finally control design efforts for snake locomotion. The review shows that the majority of literature on snake robots so far has focused on locomotion over flat surfaces, but that there is a growing trend towards locomotion in environments that are more challenging, i.e. environments that are more in line with realistic applications of these mechanisms.     

六足机器人不规则地形下的运动规划算法研究

以六足类昆虫典型代表——蚂蚁爬越障碍柱和障碍柱堆的两种运动过程及其特点为分析对象,总结了蚂蚁在应对不规则地形时的运动策略,建立了更为合理的六足机器人的运动学数学模型;并仿照蚂蚁在应对不规则地形时的运动策略,提出在进行仿生六足机器人运动规划时,可将地形划分为浅度不平地形和深度不平地形两种情况,进而针对两种地形情况提出了六足机器人在不规则地形条件下的运动规划算法。最后通过样机试验验证所提算法的有效性。     

On the Improvement of Walking Performance in Natural Environments by a Compliant Adaptive Gait

It is a widespread idea that animal-legged locomotion is better than wheeled locomotion on natural rough terrain. However, the use of legs as a locomotion system for vehicles and robots still has a long way to go before it can compete with wheels and trucks, even on natural ground. This paper aims to solve two main disadvantages plaguing walking robots: their inability to react to external disturbances (which is also a drawback of wheeled robots); and their extreme slowness. Both problems are reduced here by combining: 1) a gait-parameter-adaptation method that maximizes a dynamic energy stability margin and 2) an active-compliance controller with a new term that compensates for stability variations, thus helping the robot react stably in the face of disturbances. As a result, the combined gait-adaptation approach helps the robot achieve faster, more stable compliant motions than conventional controllers. Experiments performed with the SILO4 quadruped robot show a relevant improvement in the walking gait     

Development of a new spinning gait for a planar snake robot using central pattern generators

In this paper, we first present dynamic equation of n-link snake robot using Lagrange’s method in a simplified matrix form and verify them experimentally. Next, we introduce a new locomotion mode called spinning gait. Central pattern generators (CPGs) are used for online gait generation. To realize spinning gait, genetic algorithm is used to find optimal CPG network parameters. We illustrate both theoretically, using derived robot dynamics and experimentally that the CPG-based online gait generation method allows continuous and rather smooth transitions between gaits. Lastly, we present an application where the snake robot is guided from an initial to final position while avoiding obstacles by changing CPG parameters.     

Analysis of creeping locomotion of a snake-like robot

Snakes perform many kinds of movement adapted to the environment. Utilizing the snake (its forms and motion) as a model to develop a snake-like robot, that performs the snake's function, is important for generating a new type of locomotion and expanding the possible uses of robots. In this study, we developed a simulator to simulate the creeping locomotion of the snake-like robot, in which the robot dynamics is modeled and the interaction with the environment is considered through Coulomb friction. This simulator makes it possible to analyze creeping locomotion with normaldirection slip, adding to the glide along the tangential direction. Through the developed simulator, we investigate the snake-like robot creeping locomotion which is generated only by swinging each of the joints from side to side and discuss the optimal creeping locomotion of the snake-like robot that is adapted to the environment.     

Terrain-evaluation-based motion planning for legged locomotion on irregular terrain

The path planning of legged locomotion is complex in that path generation is based on constraints not only from body motion, but also from leg motion. A general approach to path planning will fail in generating a feasible path for walking machines when facing the huge searching space of legged locomotion. In this paper, an effective method of path planning is introduced by virtue of terrain evaluation. It maps obstacles into the robot configuration space by evaluating the obstacles' influence on the legged locomotion. The evaluation produces an index of terrain, called terrain complexity, for path planning. Using potential-guided searching, the terrain with mapped obstacles is searched to generate a feasible path.     

Design and Modeling of a Snake Robot Based on Worm-Like Locomotion

In this paper we restrict our attention to worm-like, vertical traveling wave locomotion and present detailed kinematics and dynamics of a planar multi-link snake robot. Lagrange's method is used to obtain the robot dynamics. Webots software is used for simulation and to experimentally investigate the effects of link shape on motor torques. Using the dynamics model and Webots simulation, a nine-link snake robot is designed and constructed. Physical experiments are carried out to validate the mathematical model. Webots software is also used to perform simulation and further validate theoretical results. Finally, stability of the snake robot is experimentally investigated.     

Rough terrain mapping and classification for foothold selection in a walking robot

Although legged locomotion over a moderately rugged terrain can be accomplished by employing simple reactions to the ground contact information, a more effective approach, which allows predictively avoiding obstacles, requires a model of the environment and a control algorithm that takes this model into account when planning footsteps and leg movements. This article addresses the issues of terrain perception and modeling and foothold selection in a walking robot. An integrated system is presented that allows a legged robot to traverse previously unseen, uneven terrain using only onboard perception, provided that a reasonable general path is known. An efficient method for real‐time building of a local elevation map from sparse two‐dimensional (2D) range measurements of a miniature 2D laser scanner is described. The terrain mapping module supports a foothold selection algorithm, which employs unsupervised learning to create an adaptive decision surface. The robot can learn from realistic simulations; therefore no a priori expert‐given rules or parameters are used. The usefulness of our approach is demonstrated in experiments with the six‐legged robot Messor. We discuss the lessons learned in field tests and the modifications to our system that turned out to be essential for successful operation under real‐world conditions. © 2011 Wiley Periodicals, Inc.     

Formation control of underactuated bio-inspired snake robots

This paper considers formation control of snake robots. In particular, based on a simplified locomotion model, and using the method of virtual holonomic constraints, we control the body shape of the robot to a desired gait pattern defined by some pre-specified constraint functions. These functions are dynamic in that they depend on the state variables of two compensators which are used to control the orientation and planar position of the robot, making this a dynamic maneuvering control strategy. Furthermore, using a formation control strategy we make the multi-agent system converge to and keep a desired geometric formation, and enforce the formation follow a desired straight line path with a given speed profile. Specifically, we use the proposed maneuvering controller to solve the formation control problem for a group of snake robots by synchronizing the commanded velocities of the robots. Simulation results are presented which illustrate the successful performance of the theoretical approach.     

Adaptive Robot Biped Locomotion with Dynamic Motion Primitives and Coupled Phase Oscillators

In order to properly function in real-world environments, the gait of a humanoid robot must be able to adapt to new situations as well as to deal with unexpected perturbations. A promising research direction is the modular generation of movements that results from the combination of a set of basic primitives. In this paper, we present a robot control framework that provides adaptive biped locomotion by combining the modulation of dynamic movement primitives (DMPs) with rhythm and phase coordination. The first objective is to explore the use of rhythmic movement primitives for generating biped locomotion from human demonstrations. The second objective is to evaluate how the proposed framework can be used to generalize and adapt the human demonstrations by adjusting a few open control parameters of the learned model. This paper contributes with a particular view into the problem of adaptive locomotion by addressing three aspects that, in the specific context of biped robots, have not received much attention. First, the demonstrations examples are extracted from human gaits in which the human stance foot will be constrained to remain in flat contact with the ground, forcing the “bent-knee” at all times in contrast with the typical straight-legged style. Second, this paper addresses the important concept of generalization from a single demonstration. Third, a clear departure is assumed from the classical control that forces the robot’s motion to follow a predefined fixed timing into a more event-based controller. The applicability of the proposed control architecture is demonstrated by numerical simulations, focusing on the adaptation of the robot’s gait pattern to irregularities on the ground surface, stepping over obstacles and, at the same time, on the tolerance to external disturbances.     

An adaptive locomotion controller for a hexapod robot: CPG,kinematics and force feedback

Insects can perform versatile locomotion behaviors such as multiple gaits, adapting to different terrains, fast escaping, etc. However, most of the existing bio-inspired legged robots do not possess such walking ability, especially when they walk on irregular terrains. To tackle this challenge, a central pattern generator （CPG）-based locomotion control methodology is proposed, integrated with a contact force feedback function. In this approach, multiple gaits are produced by the CFG module. After passing through a post-processing circuit and a delay-line, the control signal is fed into six trajectory generators to generate predefined feet trajectories for the six legs. Then, force feedback is employed to adjust these trajectories so as to adapt the robot to rough terrains. Finally the regulated trajectories are sent to inverse kinematics modules such that the position control instructions are generated to control the actuators. In both simulations and real robot experiments, we consistently show that the robot can perform sophisticated walking patterns. What is more, the robot can use the force feedback mechanism to deal with the irregularity in rough terrain. With this mechanism, the stability and adaptability of the robot are enhanced. In conclusion, the CPG-base control is an effective approach for legged robots and the force feedback approach is able to improve walking ability of the robots, especially when they walk on irregular terrains.     

Empirical learning in mobile robot navigation

The purpose of this study is to improve the locomotion performance for autonomous mobile robots in outdoor environments. In this paper improvement of an environment model is called empirical locomotion performance leaming. A system avoids wasting time of observations and actions by analyzing data from the last run. We propose a method of empirical learning. The method is expressed by rewriting the rules on the trajectory data. Brief route information for navigating a robot is represented with motion directions at intersections and metric distances between intersections. The behavior of our robot is based on a locomotion strategy 'sign pattern-based stereotyped motion'. The behaviors are implemented on our mobile robot HARUNOBU-4 and tested at our university campus. Experimental results show a robustness of our proposed behaviors under dynamic environments with existing obstacles. Furthermore, they showed that our proposed rewriting rules improved the locomotion performance. In particular, searching time was shortened by 87% (from 453 to 61 s) and the travel distance was shortened by 10% (from 173.8 to 157.5 m).     

Control of snake robots with switching constraints: trajectory tracking with moving obstacle

We propose control of a snake robot that can switch lifting parts dynamically according to kinematics. Snakes lift parts of their body and dynamically switch lifting parts during locomotion: e.g. sinus-lifting and sidewinding motions. These characteristic types of snake locomotion are used for rapid and efficient movement across a sandy surface. However, optimal motion of a robot would not necessarily be the same as that of a real snake as the features of a robot’s body are different from those of a real snake. We derived a mathematical model and designed a controller for the three-dimensional motion of a snake robot on a two-dimensional plane. Our aim was to accomplish effective locomotion by selecting parts of the body to be lifted and parts to remain in contact with the ground. We derived the kinematic model with switching constraints by introducing a discrete mode number. Next, we proposed a control strategy for trajectory tracking with switching constraints to decrease cost function, and to satisfy the conditions of static stability. In this paper, we introduced a cost function related to avoidance of the singularity and the moving obstacle. Simulations and experiments demonstrated the effectiveness of the proposed controller and switching constraints.     

Development of a novel crawler mechanism with polymorphic locomotion

The design of a novel crawler mechanism with polymorphic locomotion is presented in this paper. The proposed mechanism, which is equipped with a planetary gear reducer, provides two kinds of outputs in different form only using one actuator. By determining the reduction ratio of two outputs in a suitable proportion, the crawler mechanism is capable of switching between two locomotion modes autonomously according to terrain. Using this property, robots equipped with the crawler mechanism can perform more efficient and adaptable locomotion or posture in irregular environments. Experimental tests showed that the developed crawler-driven module equipped with the proposed crawler mechanism cannot only move on moderately rugged terrain, but also perform a particular locomotion mode to negotiate high obstacles or adapt to different terrains without any sensors for distinguishing obstacles or any extra actuators or mechanisms for assistance.     

Force-guidance of a compliant omnidirectional non-holonomic platform

Physical guidance is a natural interaction capability that would be beneficial for mobile robots. However, placing force sensors at specific locations on the robot limits where physical interaction can occur. This paper presents an approach that uses torque data from four compliant steerable wheels of an omnidirectional non-holonomic mobile platform, to respond to physical commands given by a human. The use of backdrivable and torque-controlled elastic actuators for active steering of this platform intrinsically provides the capability of perceiving applied forces directly from its locomotion mechanism. In this paper, we integrate this capability into a control architecture that allows users to force-guide the platform with shared-control ability, i.e., having the platform being guided by the user while avoiding obstacles and collisions. Results using a real platform demonstrate that user’s intent can be estimated from the compliant steerable wheels, and used to guide the platform while taking nearby obstacles into consideration.     

High-level motion planning for CPG-driven modular robots

Modular robots may become candidates for search and rescue operations or even for future space missions, as they can change their structure to adapt to terrain conditions and to better fulfill a given task. A core problem in such missions is the ability to visit distant places in rough terrain. Traditionally, the motion of modular robots is modeled using locomotion generators that can provide various gaits, e.g. crawling or walking. However, pure locomotion generation cannot ensure that desired places in a complex environment with obstacles will in fact be reached. These cases require several locomotion generators providing motion primitives that are switched using a planning process that takes the obstacles into account. In this paper, we present a novel motion planning method for modular robots equipped with elementary motion primitives. The utilization of primitives significantly reduces the complexity of the motion planning which enables plans to be created for robots of arbitrary shapes. The primitives used here do not need to cope with environmental changes, which can therefore be realized using simple locomotion generators that are scalable, i.e., the primitives can provide motion for robots with many modules. As the motion primitives are realized using locomotion generators, no reconfiguration is required and the proposed approach can thus be used even for modular robots without self-reconfiguration capabilities. The performance of the proposed algorithm has been experimentally verified in various environments, in physical simulations and also in hardware experiments.     

Experimentally Verified Optimal Serpentine Gait and Hyperredundancy of a Rigid-Link Snake Robot

In this study, we examine, for a six-link snake robot, how an optimal gait might change as a function of the snake- surface interaction model and how the overall locomotion performance changes under nonoptimal conditions such as joint failure. Simulations are evaluated for three different types of friction models, and it is shown that the gait parameters for serpentine motion are very dependant on the frictional model if minimum power expenditure is desired for a given velocity. Experimental investigations then motivate a surface interaction model not commonly used in snake locomotion studies. Using this new model, simulation results are compared to experiments for nominal and nonnominal locomotion cases including actuator faults. It is shown that this model quite accurately predicts locomotion velocities and link profiles, but that the accuracy of these predictions degrades severely at speeds where actuator dynamics become significant.