Cost of Transport Estimation for Legged Robot Based on Terrain Features Inference from Aerial Scan

Prágr, Miloš; Čížek, Petr; Faigl, Jan

doi:10.1109/iros.2018.8593374

Cited by 20 publications

(6 citation statements)

References 22 publications

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“…Although the forward walking COT is in the range reported by other hexapod robots, e.g. [101], walking sideways lowers COT by 40%, as shown in figure 7. One possibility was that the lower COT is due to the fact that sideways walking only uses two joints per leg (ankle and knee) whereas forward walking uses three (ankle, knee and hip).…”

Section: Resultsmentioning

confidence: 65%

Sideways crab-walking is faster and more efficient than forward walking for a hexapod robot

et al. 2022

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Articulated legs enable the selection of robot gaits, including walking in different directions such as forward or sideways. For longer distances, the best gaits might maximize velocity or minimize the cost of transport (COT). Interestingly, while animals often adapt their morphology for walking either forward (like insects) or sideways (like crabs), robots that can walk forward or sideways often pick a direction by convention. In this paper, we compare walking in forward and sideways directions. To do this, a simple gait design method is introduced for determining forward and sideways gaits with equivalent body heights and step heights. Specifically, the frequency and stride lengths are tuned within reasonable constraints to find gaits that represent a robot’s performance potential in terms of speed and energy cost. Experiments are performed in both dynamic simulation in Webots and a laboratory environment with our 18 degree-of-freedom (DOF) hexapod robot, Sebastian. With the common three joint leg design, the results show that sideways walking is overall better (75% larger walking speed and 40% lower COT). The performance of sideways walking was better on both hard floors and granular media (dry play sand). This supports the development of future crab-like walking robots for future applications. In future work, this approach may be used to develop nominal gaits without extensive optimization, and to explore whether the advantages of sideways walking persist for other hexapod designs.

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Section: Resultsmentioning

confidence: 65%

Sideways crab-walking is faster and more efficient than forward walking for a hexapod robot

et al. 2022

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“…3a), which utilizes an underlying quadtree data structure. Each cell ν ∈ M 2.5D stores elevation and RGB color information, and it is further characterized with the five-dimensional geometric and appearance terrain feature descriptor desc(ν) that is a modification of the terrain descriptor used in [23]. The used geometric part of the descriptor, which is designed to distinguish the unstructured, linear, and planar shape [16] of the terrain, is defined as…”

Section: A Environment Representationmentioning

confidence: 99%

“…where λ 1 < λ 2 < λ 3 are the eigenvalues of the covariance matrix of the elevation and spatial values in the spatial δ descneighborhood of the cell ν. The residual sum of the squares feature utilized in [16,23] is relaxed, and the two-dimensional appearance part of the descriptor is the δ desc -neighborhood channel means of the ab channels of the Lab color space. For further information on the performance of individual descriptor parts and their combinations, we kindly refer the reader to [23].…”

Section: A Environment Representationmentioning

confidence: 99%

Online Incremental Learning of the Terrain Traversal Cost in Autonomous Exploration

Prágr

Čížek

Bayer

et al. 2019

Robotics: Science and Systems XV

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In this paper, we address motion efficiency in autonomous robot exploration with multi-legged walking robots that can traverse rough terrains at the cost of lower efficiency and greater body vibration. We propose a robotic system for online and incremental learning of the terrain traversal cost that is immediately utilized to reason about next navigational goals in building spatial model of the robot surrounding. The traversal cost experienced by the robot is characterized by incrementally constructed Gaussian Processes using Bayesian Committee Machine. During the exploration, the robot builds the spatial terrain model, marks untraversable areas, and leverages the Gaussian Process predictive variance to decide whether to improve the spatial model or decrease the uncertainty of the terrain traversal cost. The feasibility of the proposed approach has been experimentally verified in a fully autonomous deployment with a hexapod walking robot.

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“…However, areas which appear flat and thus easy to traverse may, in practice, be hard to traverse due to their terra-mechanical properties, as experienced by NASA’s Mars Rover Spirit stuck in soft sand ( Brown and Webster, 2010 ). In the presented approach, individual terra-mechanical properties are assumed to be partially unknown, and we learn a black box model to assess the traversability in a particular environment from the terrain appearance ( Prágr et al, 2018 ). Since the scope of the functional relation between the terrain appearance and traversability might be limited to a particular environment, we advocate that on long-term deployments and exploration missions, the terrain models are learned online incrementally ( Prágr et al, 2019b ) as a part of the mission ( Prágr et al, 2019a ).…”

Section: Introductionmentioning

confidence: 99%

Autonomous robotic exploration with simultaneous environment and traversability models learning

2022

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In this study, we address generalized autonomous mobile robot exploration of unknown environments where a robotic agent learns a traversability model and builds a spatial model of the environment. The agent can benefit from the model learned online in distinguishing what terrains are easy to traverse and which should be avoided. The proposed solution enables the learning of multiple traversability models, each associated with a particular locomotion gait, a walking pattern of a multi-legged walking robot. We propose to address the simultaneous learning of the environment and traversability models by a decoupled approach. Thus, navigation waypoints are generated using the current spatial and traversability models to gain the information necessary to improve the particular model during the robot’s motion in the environment. From the set of possible waypoints, the decision on where to navigate next is made based on the solution of the generalized traveling salesman problem that allows taking into account a planning horizon longer than a single myopic decision. The proposed approach has been verified in simulated scenarios and experimental deployments with a real hexapod walking robot with two locomotion gaits, suitable for different terrains. Based on the achieved results, the proposed method exploits the online learned traversability models and further supports the selection of the most appropriate locomotion gait for the particular terrain types.

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Cost of Transport Estimation for Legged Robot Based on Terrain Features Inference from Aerial Scan

Cited by 20 publications

References 22 publications

Sideways crab-walking is faster and more efficient than forward walking for a hexapod robot

Sideways crab-walking is faster and more efficient than forward walking for a hexapod robot

Online Incremental Learning of the Terrain Traversal Cost in Autonomous Exploration

Autonomous robotic exploration with simultaneous environment and traversability models learning

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