Whole-body motion planning and control of a quadruped robot for challenging terrain

Quadruped robots working in jungles, mountains or factories should be able to move through challenging scenarios. In this paper, we present a control framework for quadruped robots walking over rough terrain. The planner plans the trajectory of the robot's center of gravity by using the normalized energy stability criterion, which ensures that the robot is in the most stable state. A contact detection algorithm based on the probabilistic contact model is presented, which implements event-based state switching of the quadruped robot legs. And an on-line detection of contact force based on generalized momentum is also showed, which improves the accuracy of proprioceptive force estimation. A controller combining whole body control and virtual model control is proposed to achieve precise trajectory tracking and active compliance with environment interaction. Without any knowledge of the environment, the experiments of the quadruped robot SDUQuad-144 climbs over significant obstacles such as 38 cm high steps and 22.5 cm high stairs are designed to verify the feasibility of the proposed method.     

Weaver: Hexapod robot for autonomous navigation on unstructured terrain

Legged robots are an efficient alternative for navigation in challenging terrain. In this paper we describe Weaver, a six‐legged robot that is designed to perform autonomous navigation in unstructured terrain. It uses stereo vision and proprioceptive sensing based terrain perception for adaptive control while using visual‐inertial odometry for autonomous waypoint‐based navigation. Terrain perception generates a minimal representation of the traversed environment in terms of roughness and step height. This reduces the complexity of the terrain model significantly, enabling the robot to feed back information about the environment into its controller. Furthermore, we combine exteroceptive and proprioceptive sensing to enhance the terrain perception capabilities, especially in situations in which the stereo camera is not able to generate an accurate representation of the environment. The adaptation approach described also exploits the unique properties of legged robots by adapting the virtual stiffness, stride frequency, and stride height. Weaver's unique leg design with five joints per leg improves locomotion on high gradient slopes, and this novel configuration is further analyzed. Using these approaches, we present an experimental evaluation of this fully self‐contained hexapod performing autonomous navigation on a multiterrain testbed and in outdoor terrain.     

Avoiding Non-Manhattan Obstacles Based on Projection of Spatial Corners in Indoor Environment

Monocular vision-based navigation is a considerable ability for a home mobile robot. However, due to diverse disturbances, helping robots avoid obstacles, especially non-Manhattan obstacles, remains a big challenge. In indoor environments, there are many spatial right-corners that are projected into two dimensional projections with special geometric configurations. These projections, which consist of three lines, might enable us to estimate their position and orientation in 3D scenes. In this paper, we present a method for home robots to avoid non-Manhattan obstacles in indoor environments from a monocular camera. The approach first detects non-Manhattan obstacles. Through analyzing geometric features and constraints, it is possible to estimate posture differences between orientation of the robot and non-Manhattan obstacles. Finally according to the convergence of posture differences, the robot can adjust its orientation to keep pace with the pose of detected non-Manhattan obstacles, making it possible avoid these obstacles by itself. Based on geometric inferences, the proposed approach requires no prior training or any knowledge of the camera’s internal parameters, making it practical for robots navigation. Furthermore, the method is robust to errors in calibration and image noise. We compared the errors from corners of estimated non-Manhattan obstacles against the ground truth. Furthermore, we evaluate the validity of convergence of differences between the robot orientation and the posture of non-Manhattan obstacles. The experimental results showed that our method is capable of avoiding non-Manhattan obstacles, meeting the requirements for indoor robot navigation.      

Navigation of Mobile Robots Using a Fuzzy Logic Controller

This paper describes a mobile robot navigation control system based on fuzzy logic. Fuzzy rules embedded in the controller of a mobile robot enable it to avoid obstacles in a cluttered environment that includes other mobile robots. So that the robots do not collide against one another, each robot also incorporates a set of collision prevention rules implemented as a Petri Net model within its controller. The navigation control system has been tested in simulation and on actual mobile robots. The paper presents the results of the tests to demonstrate that the system enables multiple robots to roam freely searching for and successfully finding targets in an unknown environment containing obstacles without hitting the obstacles or one another.     

Generation of a continuous free gait for quadruped robot over rough terrains

Generating a robust gait is one of the most important factors to improve the adaptability of quadruped robots on rough terrains. This paper presents a new continuous free gait generation method for quadruped robots capable of walking on the rough terrain characterized by the uneven ground and forbidden areas. When walking with the proposed gait, the robot can effectively maintain its stability by using the Center of Gravity (COG) trajectory planning method. After analyzing the point cloud of rough terrain, the forbidden areas of the terrain can be obtained. Based on this analysis, an optimal foothold search strategy is presented to help quadruped robot to determine the optimum foothold for the swing foot automatically. In addition, the foot sequence determining method is proposed to improve the performance of robot. With the free gait proposed in this paper, quadruped robot can walk through the rough terrains automatically and successfully. The correctness and effectiveness of the proposed method is verified via simulations.     

Hardware neural network models of CPG and PWM for controlling servomotor system in quadruped robot

This paper discusses the pulse-type hardware neural networks (P-HNNs) that contain a central pattern generator (CPG) and a pulsewidth modulation (PWM) servomotor controller and the application to quadruped robots. The purpose of our study is mimicking the biological neural networks and reproducing the similar motion of the living organisms in the robot. The CPG of the living organism generates the walking rhythms. We mimicked this CPG by modeling the cell body and the synapse of the living organism. The developed CPG composed of the P-HNN output four pulse signal sequences and the four outputs are introduced to each leg of the quadruped robot. On the other hand, the angle of the servomotor is controlled by the PWM. The PWM is obtained by modeling the axon of the living organism. The CPG and the PWM servo control system perform the walking motion of the quadruped robot. Moreover, the gate pattern change of quadruped animals is reproduced by these P-HNNs.     

智能决策改进的四足机器人ZMP爬行步态算法

提出了一种基于反馈控制和贪婪决策的四足机器人爬行步态规划算法。该算法利用机载惯性传感器IMU（Inertial Measurement Unit）来实时计算零力矩点和姿态角,以稳态裕度为指标在支撑平面内实时规划期望零力矩点（Zero Moment Point,ZMP）轨迹,结合非线性反馈控制器实现对机体ZMP点的连续平滑调节,保证机器人在按给定速度矢量进行连续爬行的同时具有抵抗一定外力扰动的能力。步态规划采用动态步态周期,基于机器人结构约束和贪婪决策实现跨腿的自动触发,提高了步态自适应性。最终通过样机行走实验验证了所提算法应用于微型四足机器人中的可行性,机器人实现了在平坦地面上稳定地全向行走和旋转,所提算法同时兼顾了自适应性和稳定裕度。     

State estimation and traversability map construction method of a quadruped robot on soft uneven terrain

Quadruped robots show excellent application prospects in complex environment detection and rescue. At present, scholars mainly focus on quadruped walking in rigid environments. However, quadruped robots often need to pass through uneven and soft unconstructed terrains, prone to slip and impact. The mismatch between the planned foothold position and the real one resulting from environmental uncertainties makes the robot unstable. In this paper, the state estimation and traversability map construction methods are proposed for quadruped robots to achieve stable walking in an unstructured environment, especially on soft terrains. First, the Error-state Kalman Filter (ErKF) is extended by optimizing the leg odometry information to get an accurate robot state, especially in soft, uneven terrain. The ErKF method fuses the sensor data from the inertial measurement unit, laser, camera, and leg odometry. The leg odometry is optimized by considering the foot slippage, which easily occurs in soft uneven terrains. Then, the unstructured environment is parameterized and modeled by the terrain inclination, roughness, height, and stiffness. A traversability map, which is essential for robot path and foothold planning in autonomous movement, is constructed with the above parameters. Finally, the proposed method is verified by simulation and experiments. The results show that the quadruped robot can walk stably on different soft and uneven terrains.     

The Design and Control of a Prototype Quadruped Microrover

We describe the design, construction and control of a quadruped robot which walks on uneven terrain. A control system which produces a statically stable gait has been implemented; results showing a straight and turning gait are presented. The control of quadruped robots poses interesting challenges due to a small stability margin (when compared to hexapods for example). For this reason most implemented systems for outdoor walking on uneven terrain have been hexapods. The system described here has the added virtue of using very few inexpensive sensors and actuators. One of the aims of this work is to build a reduced complexity (low power, low mass and direct drive) walking robot for statically stable walking. The other aim is to compare the performance of this robot with a wheeled robot roughly the same size and weight. In this paper we report on progress towards the first of these two goals using a traverse across an obstacle field as an example.     

Direct visual tracking control of remote cellular robots

This paper presents the design of a stable non-linear control system for the remote visual tracking of cellular robots. The robots are controlled through visual feedback based on the processing of the image captured by a fixed video camera observing the workspace. The control algorithm is based only on measurements on the image plane of the visual camera–direct visual control–thus avoiding the problems related to camera calibration. In addition, the camera plane may have any (unknown) orientation with respect to the robot workspace. The controller uses an on-line estimation of the image Jacobians. Considering the Jacobians’ estimation errors, the control system is capable of tracking a reference point moving on the image plane–defining the reference trajectory–with an ultimately bounded error. An obstacle avoidance strategy is also developed in the same context, based on the visual impedance concept. Experimental results show the performance of the overall control system.     

Snake Robot Obstacle-Aided Locomotion: Modeling, Simulations, and Experiments

Snakes utilize irregularities in the terrain, such as rocks and vegetation, for faster and more efficient locomotion. This motivates the development of snake robots that actively use the terrain for locomotion, i.e., obstacle-aided locomotion. In order to accurately model and understand this phenomenon, this paper presents a novel nonsmooth (hybrid) mathematical model for wheel-less snake robots, which allows the snake robot to push against external obstacles apart from a flat ground. The framework of nonsmooth dynamics and convex analysis allows us to systematically and accurately incorporate both unilateral contact forces (from the obstacles) and isotropic friction forces based on Coulomb's law using set-valued force laws. The mathematical model is verified through experiments. In particular, a back-to-back comparison between numerical simulations and experimental results is presented. It is, furthermore, shown that the snake robot is able to move forward faster and more robustly by exploiting obstacles.     

Real-time obstacle detection for legged robots using the Kinect sensor

Autonomous navigation of legged robots in complex environments poses a great deal of challenges compared with ground vehicles because of their different terrain traverse capabilities. An obstacle for vehicles may be traversable for legged robots. This paper proposes a real-time obstacle detection algorithm for legged robots using the Microsoft Kinect sensor. First, the elevation map of a reference grid is calculated. Then an obstacle definition for legged robots is proposed, which makes it possible for a legged robot to discriminate traversable areas from non-traversable areas. To reduce computational cost, sometimes, efficient judging rules are developed to identify obstacles. A spiral search strategy is proposed to find the most ground-like point as the starting point for graph-based traversal. Breadth-First-Traversal of the graph is used to label all traversable areas connecting to the starting point. Experimental results demonstrate that our algorithm is reliable and efficient. The proposed algorithm can be employed in real-time obstacle detection for legged robots in complex environments.     

Map Building Fusing Acoustic and Visual Information using Autonomous Underwater Vehicles

We present a system for automatically building three‐dimensional (3‐D) maps of underwater terrain fusing visual data from a single camera with range data from multibeam sonar. The six‐degree‐of‐freedom location of the camera relative to the navigation frame is derived as part of the mapping process, as are the attitude offsets of the multibeam head and the onboard velocity sensor. The system uses pose graph optimization and the square root information smoothing and mapping framework to simultaneously solve for the robot's trajectory, the map, and the camera location in the robot's frame. Matched visual features are treated within the pose graph as images of 3‐D landmarks, while multibeam bathymetry submap matches are used to impose relative pose constraints linking robot poses from distinct tracklines of the dive trajectory. The navigation and mapping system presented works under a variety of deployment scenarios on robots with diverse sensor suites. The results of using the system to map the structure and the appearance of a section of coral reef are presented using data acquired by the Seabed autonomous underwater vehicle.     

Reactive navigation in dynamic environment using a multisensorpredictor

A reactive navigation system for an autonomous mobile robot in unstructured dynamic environments is presented. The motion of moving obstacles is estimated for robot motion planning and obstacle avoidance. A multisensor-based obstacle predictor is utilized to obtain obstacle-motion information. Sensory data from a CCD camera and multiple ultrasonic range finders are combined to predict obstacle positions at the next sampling instant. A neural network, which is trained off-line, provides the desired prediction on-line in real time. The predicted obstacle configuration is employed by the proposed virtual force based navigation method to prevent collision with moving obstacles. Simulation results are presented to verify the effectiveness of the proposed navigation system in an environment with multiple mobile robots or moving objects. This system was implemented and tested on an experimental mobile robot at our laboratory. Navigation results in real environment are presented and analyzed.     

New Visual Invariants for Terrain Navigation Without 3D Reconstruction

For autonomous vehicles to achieve terrain navigation, obstacles must be discriminated from terrain before any path planning and obstacle avoidance activity is undertaken. In this paper, a novel approach to obstacle detection has been developed. The method finds obstacles in the 2D image space, as opposed to 3D reconstructed space, using optical flow. Our method assumes that both nonobstacle terrain regions, as well as regions with obstacles, will be visible in the imagery. Therefore, our goal is to discriminate between terrain regions with obstacles and terrain regions without obstacles. Our method uses new visual linear invariants based on optical flow. Employing the linear invariance property, obstacles can be directly detected by using reference flow lines obtained from measured optical flow. The main features of this approach are: (1) 2D visual information (i.e., optical flow) is directly used to detect obstacles; no range, 3D motion, or 3D scene geometry is recovered; (2) knowledge about the camera-to-ground coordinate transformation is not required; (3) knowledge about vehicle (or camera) motion is not required; (4) the method is valid for the vehicle (or camera) undergoing general six-degree-of-freedom motion; (5) the error sources involved are reduced to a minimum, because the only information required is one component of optical flow. Numerous experiments using both synthetic and real image data are presented. Our methods are demonstrated in both ground and air vehicle scenarios.     

Hierarchical multi-robot navigation and formation in unknown environments via deep reinforcement learning and distributed optimization

Compared with a single robot, Multi-robot Systems (MRSs) can undertake more challenging tasks in complex scenarios benefiting from the increased transportation capacity and fault tolerance. This paper presents a hierarchical framework for multi-robot navigation and formation in unknown environments with static and dynamic obstacles, where the robots compute and maintain the optimized formation while making progress to the target together. In the proposed framework, each single robot is capable of navigating to the global target in unknown environments based on its local perception, and only limited communication among robots is required to obtain the optimal formation. Accordingly, three modules are included in this framework. Firstly, we design a learning network based on Deep Deterministic Policy Gradient (DDPG) to address the global navigation task for single robot, which derives end-to-end policies that map the robot’s local perception into its velocity commands. To handle complex obstacle distributions (e.g. narrow/zigzag passage and local minimum) and stabilize the training process, strategies of Curriculum Learning (CL) and Reward Shaping (RS) are combined. Secondly, for an expected formation, its real-time configuration is optimized by a distributed optimization. This configuration considers surrounding obstacles and current formation status, and provides each robot with its formation target. Finally, a velocity adjustment method considering the robot kinematics is designed which adjusts the navigation velocity of each robot according to its formation target, making all the robots navigate to their targets while maintaining the expected formation. This framework allows for formation online reconfiguration and is scalable with the number of robots. Extensive simulations and 3-D evaluations verify that our method can navigate the MRS in unknown environments while maintaining the optimal formation.     

Synchronized adaptive control for coordinating manipulators with time‐varying actuator constraint and uncertain dynamics

This paper addresses the control issue for cooperative visual servoing manipulators on strongly connected graph with communication delays, in which case that the uncertain robot dynamics and kinematics, uncalibrated camera model, and actuator constraint are simultaneously considered. An adaptive cooperative image‐based approach is established to overcome the control difficulty arising from nonlinear coupling between visual model and robot agents. To estimate the coupled camera‐robot parameters, a novel adaptive strategy is developed and its superiority mainly lies in the containment of both individual image‐space errors and the synchronous errors among networked robots; thus, the cooperative performance is significantly strengthened. Moreover, the proposed cooperative controller with a Nussbaum‐type gain is implemented to both globally stabilize the closed‐loop systems and realize the synchronization control objective under the existence of unknown and time‐varying actuator constraint. Finally, simulations are carried out to validate the developed approach.     

Neural networks IC controlled multi-legged walking MEMS robot with independent leg mechanism

In this paper, we will compare the walking behavior of quadruped and hexapod walking MEMS robots. These robots are fabricated by connecting same modules, which are composed of a couple of independent leg mechanisms. Independent leg mechanisms can actuate the single leg by a single artificial muscle wire. The neural networks IC that mimics real living organisms controls the mechanical systems. The length and weight of the quadruped MEMS robot were 7.2 mm and 95.8 mg, respectively. The quadruped robot showed the walking speed of 24.6 mm/min. The robot tended to lose its balance and the weight balance is quite important for the moving quadruped. On the other hand, the length and weight of the hexapod MEMS robot were 9.0 mm and 162 mg, respectively. The hexapod robot showed stable walking. The speed was 27.0 mm/min.     

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.     

Wide-baseline stereo vision for terrain mapping

Terrain mapping is important for mobile robots to perform localization and navigation. Stereo vision has been used extensively for this purpose in outdoor mapping tasks. However, conventional stereo does not scale well to distant terrain. This paper examines the use of wide-baseline stereo vision in the context of a mobile robot for terrain mapping, and we are particularly interested in the application of this technique to terrain mapping for Mars exploration. In wide-baseline stereo, the images are not captured simultaneously by two cameras, but by a single camera at different positions. The larger baseline allows more accurate depth estimation of distant terrain, but the robot motion between camera positions introduces two new problems. One issue is that the robot estimates the relative positions of the camera at the two locations imprecisely, unlike the precise calibration that is performed in conventional stereo. Furthermore, the wide-baseline results in a larger change in viewpoint than in conventional stereo. Thus, the images are less similar and this makes the stereo matching process more difficult. Our methodology addresses these issues using robust motion estimation and feature matching. We give results using real images of terrain on Earth and Mars and discuss the successes and failures of the technique.