Unknown Rough Terrain Research Articles

Optimizing and designing a leg shape to increase robustness of a running robot on rough terrain

The superior ability of dynamic legged locomotion in traversing rough terrain relative to wheeled or tracked mechanisms comes with the cost of fragile stability. Simple control methods that use only a few basic detection sensors and apply a single controller help robots keep their balance when traversing unforeseen rough terrain. Exploiting multiple controllers simultaneously, such as the free leg length and stiffness in our hopping monopod, can further improve robustness but is often mechanically hard to implement. This work demonstrates that a curved leg shape can improve the robustness of a robot to perturbations in both terrain levels and initial horizontal velocity without complicating the control scheme. Our work develops spring loaded inverted pendulum (SLIP) based models that manifest the coupling of the leg’s parameters and capture the rolling motion. We use these models to find an optimal combination of parameters that maximizes a measure for long-term stability−reaching a desired relative height above terrain. We demonstrate that when traversing unknown rough terrain, such optimal coupling can increase robustness to perturbations in the initial horizontal velocity by 93% relative to the optimal conventional SLIP model. We further demonstrate our results in experiments.

Variable stiffness locomotion with guaranteed stability for quadruped robots traversing uneven terrains.

Quadruped robots are widely applied in real-world environments where they have to face the challenges of walking on unknown rough terrains. This paper presents a control pipeline that generates robust and compliant legged locomotion for torque-controlled quadruped robots on uneven terrains. The Cartesian motion planner is designed to be reactive to unexpected early and late contacts using the estimated contact forces. Moreover, we present a novel scheme of optimal stiffness modulation that aims to coordinate desired compliance and tracking performance. It optimizes joint stiffness and contact forces coordinately in a quadratic programming (QP) formulation, where the constraints of non-slipping contacts and torque limits are imposed as well. In addition, the issue of stability under variable stiffness control is solved by imposing a tank-based passivity constraint explicitly. We finally validate the proposed control pipeline on our quadruped robot CENTAURO in experiments on uneven terrains and, through comparative tests, demonstrate the improvements of the variable stiffness locomotion.

Traversability analysis with vision and terrain probing for safe legged robot navigation.

Inspired by human behavior when traveling over unknown terrain, this study proposes the use of probing strategies and integrates them into a traversability analysis framework to address safe navigation on unknown rough terrain. Our framework integrates collapsibility information into our existing traversability analysis, as vision and geometric information alone could be misled by unpredictable non-rigid terrains such as soft soil, bush area, or water puddles. With the new traversability analysis framework, our robot has a more comprehensive assessment of unpredictable terrain, which is critical for its safety in outdoor environments. The pipeline first identifies the terrain’s geometric and semantic properties using an RGB-D camera and desired probing locations on questionable terrains. These regions are probed using a force sensor to determine the risk of terrain collapsing when the robot steps over it. This risk is formulated as a collapsibility metric, which estimates an unpredictable region’s ground collapsibility. Thereafter, the collapsibility metric, together with geometric and semantic spatial data, is combined and analyzed to produce global and local traversability grid maps. These traversability grid maps tell the robot whether it is safe to step over different regions of the map. The grid maps are then utilized to generate optimal paths for the robot to safely navigate to its goal. Our approach has been successfully verified on a quadrupedal robot in both simulation and real-world experiments.

Deep Reinforcement Learning for Safe Local Planning of a Ground Vehicle in Unknown Rough Terrain

Safe unmanned ground vehicle navigation in unknown rough terrain is crucial for various tasks such as exploration, search and rescue and agriculture. Offline global planning is often not possible when operating in harsh, unknown environments, and therefore, online local planning must be used. Most online rough terrain local planners require heavy computational resources, used for optimal trajectory searching and estimating vehicle orientation in positions within the range of the sensors. In this work, we present a deep reinforcement learning approach for local planning in unknown rough terrain with zero-range to local-range sensing, achieving superior results compared to potential fields or local motion planning search spaces methods. Our approach includes reward shaping which provides a dense reward signal. We incorporate self-attention modules into our deep reinforcement learning architecture in order to increase the explainability of the learnt policy. The attention modules provide insight regarding the relative importance of sensed inputs during training and planning. We extend and validate our approach in a dynamic simulation, demonstrating successful safe local planning in environments with a continuous terrain and a variety of discrete obstacles. By adding the geometric transformation between two successive timesteps and the corresponding action as inputs, our architecture is able to navigate on surfaces with different levels of friction. Reinforcement learning, autonomous vehicle navigation, motion and path planning.

Minimalistic Control for Reaching Absolute State of a One-Legged Dynamic Robot on Uncertain Terrain

Abstract Footstep and path planning for dynamic legged robots is complex, and even if such a plan exists, execution is even harder. We propose a new method for a planar model of a dynamic legged robot that brings the trajectory to an absolute desired destination even on unknown rough terrain with minimal sensing. This can later aid a global planner to reach “way-points” with low destination errors. The basic block of the technique incorporates two consecutive jumps, each triggers a minimalistic control method to govern a sole controller—the leg angle during flight. Only two detection sensors and initial state information are required during implementation. Prior to execution, an optimization process is initiated to obtain the temporal control laws for both jumps. This work presents the process of obtaining the control parameters and studies the performance and limitations of the scheme.

Static Gait Planning Method for Quadruped Robot Walking on Unknown Rough Terrain

To enable quadruped robot to walk through unknown rough terrains without any machine vision system, a continuous static gait planning method is proposed in this paper. A algorithm for on-line terrain complexity evaluation according to touchdown times of swing feet is presented to make quadruped robot obtain the information of terrain without relying on machine vision system. Quadruped robot can obtain enough stability margin during walking process by using the proposed body trajectory planning method, and motion continuity of body can be guaranteed. In order to improve the terrain adaptability of quadruped robot, a tunable parameter is set in the body planning method, and the relationship between value of the tunable parameter and terrain complexity estimation is given to further improve terrain adaptability and energy utilization rate. With the proposed static gait generation method, quadruped robot can autonomous plan its static gait in real-time and travel over unknown rough terrains with enough stability margin. The simulation results show the correctness and effectiveness of the proposed method.

Design, Execution, and Postmortem Analysis of Prolonged Autonomous Robot Operations

In the context of space missions and terrestrial applications, both mission goals and task implementations for autonomous robots are becoming increasingly complex. Thus, the challenge of monitoring the achievement of task objectives and checking the correctness of their implementation is becoming more and more difficult. To tackle these problems, we propose an unified architecture that supports different stakeholders during the different phases of the deployment: 1) the design phase; 2) the runtime phase; and 3) the postmortem analysis phase. Furthermore, we implement this architecture by enhancing our task programming framework RMC advanced flow control with powerful logging, debugging, and profiling capabilities. We demonstrate the efficiency of our approach in the context of the ROBEX mission, during which the DLR Lightweight Rover Unit autonomously deployed several seismometers in an unknown rough terrain on Mt. Etna, Sicily. The analysis results for a state machine consisting of more than 1500 states and more than 1900 transitions are presented. Finally, we give a comparison between our framework and related software tools.

Online aircraft velocity and normal acceleration planning for rough terrain following

ABSTRACTThis paper attempts to develop an efficient online algorithm for terrain following in completely unknown rough terrain environments while incorporating aircraft dynamics in the guidance strategy. Unlike most existing works, the proposed algorithm does not generate the flight path directly. The algorithm employs acquired information from the vehicle onboard sensors and rapidly issues appropriate Guidance Commands (GCs) at every point along the way. A suitable dynamic model is developed which takes the lags in the vehicle dynamics into account. The flight path forms gradually as a result of applying the GCs to the vehicle dynamics. Terrain-conforming capability afforded by this approach allows for autonomous and safe low-level flight in unknown mountainous areas. It considerably enhances the autonomy level of the vehicle and in the case of manned aircraft could significantly lead to pilot workload reduction. The proposed scheme is proven to be promising for online applications.

Compliant landing of a trotting quadruped robot based on hybrid motion/force robust control

A compliant landing strategy for a trotting quadruped robot on unknown rough terrains based on contact force control is presented. Firstly, in order to lower the disturbance caused by the landing impact force, a landing phase is added between the swing phase and the stance phase, where the desired contact force is set as a small positive constant. Secondly, the joint torque optimization of the stance legs is formulated as a quadratic programming (QP) problem subject to equality and inequality/bound constraints. And a primal-dual dynamical system solver based on linear variational inequalities (LVI) is applied to solve this QP problem. Furthermore, based on the optimization results, a hybrid motion/force robust controller is designed to realize the tracking of the contact force, while the constraints of the stance feet landing angles are fulfilled simultaneously. Finally, the experiments are performed to validate the proposed methods.

Active modification of the environment by a robot with construction abilities

Field robots are widely used to accomplish a variety of tasks in many different fields. However, setting of the locomotive ability of these robots at the design phase may prevent the traversal of unknown rough terrain. To address this shortcoming of existing robots, we designed a robot that is able to modify its environment by using polyurethane foam to construct auxiliary structures to facilitate movement across previously impassable terrain. Two robots were implemented with the ability to eject one- and two-part polyurethane foam, respectively. First, we investigated the specifications of the different types of polyurethane foam, specifically the volume expansion and curing time thereof. Two-part polyurethane foam cures in approximately 2 min, compared with 1 h for the one-part foam, but requires more accurate spraying, and its vertical expansion needs to be considered for accurate construction of auxiliary structures. The performance of each robot was tested in two experiments in the field. The first involved filling a deep ditch before crossing over it, while in the second experiment, each robot constructed a slope leading up to a high step, allowing the robot to move onto the step. Both robots succeeded in completing these tasks successfully, with the main difference in performance being the time taken before the robot was able to traverse the obstacles. Using two-part polyurethane foam resulted in much shorter curing times, although the structures constructed were not as even as those for the one-part polyurethane foam, and the robot needed to wait 10 s between the applications of each successive layer of foam to account for the vertical expansion of the material. Our findings demonstrate the effectiveness of our polyurethane foam construction robots in overcoming obstacles in unknown rough terrain.

Dynamic Stability Analysis of a Trotting Quadruped Robot Based on Switching Control

This paper presents a dynamic stability analysis method for a trotting quadruped robot on unknown rough terrains, which is based on the Lyapunov theory of a switching system. Firstly, the dynamical...

Remote control system of disaster response robot with passive sub-crawlers considering falling down avoidance

Abstract This paper describes a remote control system for a crawler-type mobile robot with passive sub-crawlers. This system has a great advantage because it utilizes an essential compliant mechanism that allows the angle of the sub-crawlers to be adapted to the shape of the road surface. Its operation is extremely simple, and it is only necessary to control the movement direction and driving speed in comparison with the case of controlling active sub-crawlers. However, a robot with passive sub-crawlers cannot recover from a situation in which it is stuck. The operator must select a traversable route for unknown rough terrain using only the information obtained from camera images and some sensor data from the robot. In this study, a remote control system for a crawler robot with passive sub-crawlers was developed based on a warning system. This system evaluates the currently selected route by calculating the stabilization for the robot when falling down in the roll and pitch directions. Experimental results obtained using a prototype crawler robot with passive sub-crawlers demonstrated the effectiveness of the proposed system.

Path Replanning Method for an AUV in Natural Hydrothermal Vent Fields: Toward 3D Imaging of a Hydrothermal Chimney

AbstractAutonomous underwater vehicles (AUVs) can operate without the need for human control or tether cables as long as there is sufficient energy. AUVs have recently been used for seafloor imaging. Visual observation by AUVs provides high-resolution color information of the seafloor. However, conventional observation techniques that follow a prespecified path offer limited coverage because it is impossible for operators to build a suitable path in unknown rough terrain. A flawed prespecified path will produce incomplete observation. If unobserved areas are found during postprocessing, another dive is necessary, which increases the total cost. To overcome this problem, the authors have proposed a path replanning method to realize high-coverage observation in one dive. With this method, the AUV evaluates unobserved areas after the first prespecified observation; if unobserved areas are found, the AUV recreates an appropriate path to cover what was missed. The validity of the proposed method was previously evaluated using an artificial target in a tank and in shallow seas at a depth of approximately 35 m. In this study, the feasibility of the method was validated in a more challenging setting: experimental data were taken from a hydrothermal vent field in Kagoshima Bay, Japan.

Motion control of a quadruped robot in unknown rough terrain using 3D spring damper leg model

This paper presents a simple method to control the motion of a quadruped robot in unknown rough terrain using a full dynamic model. First, using the stiffness control method, the behavior of the four legs is approximated using four 3D spring damper systems. In this way, the dynamic model can be derived in Cartesian space. Based on this model, a control strategy is proposed to preserve the asymptotical stability of the system. In addition, a reflex motion control is introduced to cope with the rotational disturbance of the robot body. Finally, dynamic simulations and experiments of a quadruped walking robot were performed on unknown rough terrain to verify the proposed method.

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.

Study of Control Algorithm for Wheeleg Robot Walking in Unknown Rough Terrains

This paper presents three algorithms to control the motion of wheeleg robot in unknown rough terrains. First, on the basis of the wheeleg robot, we establish the plane geometry model. By this way, we can easily derive the kinematics and dynamics equations. Based on these equations, we design the control strategy, include three algorithms: posture control, force compensation control and force control. We lay emphasis on researching force control. Then, dynamic simulation of the wheeleg robot is performed in unknown rough terrains to verify the proposed algorithms. Finally, analysing and comparing the three algorithms, and drawing the conclusion.

Hybrid control for SLIP-based robots running on unknown rough terrain

SUMMARYRapid and efficient dynamic stability control has been one of the important motivations in legged robot research, especially for legged robots running at high speed and/or on rough terrain. This paper presents a feasible control strategy, named Hybrid Feedback Control (HFC), for running systems based on the spring-loaded inverted pendulum principle (SLIP). The HFC strategy, which comprises two modules (i.e., touchdown angle control and energy compensation), predicts and regulates touchdown angle of the current cycle and need-to-complement energy input of the next cycle through hybrid feedback of flying apex state. This strategy can significantly reduce the computational complexity and enable the system to quickly converge to its control target, meeting the requirements of real-time control. Simulation experiments on various terrains were conducted to verify the adaptability of our HFC strategy. Results of these simulation experiments show that the approach herein can realize the periodical stability control of SLIP systems on different terrain conditions quickly and effectively.

A Study of the Sole Mechanism of Biped Robots to Rough Terrain Locomotion

This paper describes the study of a biped robot foot sole mechanism for use in unknown rough terrain locomotion. The mechanism has a structure consisting of three toes and frictional locking and unlocking mechanisms. The mechanisms level the bottom of the biped robot’s foot by using the three toes when on unknown rough terrain. To stabilize the sole of the biped robot on a level surface, the frictional locking and unlocking mechanisms of the three toes are used for the sole mechanism. Stable walking control of the biped robot is simplified by the sole mechanism we developed.

Autonomous navigation of a humanoid robot over unknown rough terrain using a laser range sensor

The present paper describes the integration of laser-based perception, footstep planning, and walking control of a humanoid robot for navigation over previously unknown rough terrain. A perception system that obtains the shape of the surrounding environment to an accuracy of a few centimeters is realized based on input obtained using a scanning laser range sensor. A footstep planner decides the sequence of stepping positions using the obtained terrain shape. A walking controller that can cope with a few centimeters of error in terrain shape measurement is achieved by combining the generation of a 40-ms cycle online walking pattern and a ground reaction force controller with sensor feedback. An operational interface was developed to send commands to the robot. A mixed-reality display was adopted to realize an intuitive interface. The navigation system was implemented on the HRP-2, a full-size humanoid robot. The performance of the proposed system for navigation over unknown rough terrain and the accuracy of the terrain shape measurement were investigated through several experiments.

Stereo-vision-based navigation of a six-legged walking robot in unknown rough terrain

In this paper we present a visual navigation algorithm for the six-legged walking robot DLR Crawler in rough terrain. The algorithm is based on stereo images from which depth images are computed using the semi-global matching (SGM) method. Further, a visual odometry is calculated along with an error measure. Pose estimates are obtained by fusing inertial data with relative leg odometry and visual odometry measurements using an indirect information filter. The visual odometry error measure is used in the filtering process to put lower weights on erroneous visual odometry data, hence, improving the robustness of pose estimation. From the estimated poses and the depth images, a dense digital terrain map is created by applying the locus method. The traversability of the terrain is estimated by a plane fitting approach and paths are planned using a D* Lite planner taking the traversability of the terrain and the current motion capabilities of the robot into account. Motion commands and the traversability measures of the upcoming terrain are sent to the walking layer of the robot so that it can choose an appropriate gait for the terrain. Experimental results show the accuracy of the navigation algorithm and its robustness against visual disturbances.