A Single-Planner Approach to Multi-Modal Humanoid Mobility

Dornbush, Andrew; Vijayakumar, K.; Bardapurkar, Sameer; Islam, Fahad; Ito, Masayuki; Likhachev, Maxim

doi:10.1109/icra.2018.8461134

Cited by 12 publications

(4 citation statements)

References 17 publications

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“…Another related approach is adaptive dimensionality planning [9], where a single graph search is made on a hybrid state space with multiple lowdimensional subspaces, a high-level subspace, and mappings between them. The approach has been applied to legged robots for automatically planning over crawling, bipedal and climbing motions [10], although currently reported results have low planning success rates.…”

Section: Related Workmentioning

confidence: 99%

Multi-controller multi-objective locomotion planning for legged robots

Brandão

Fallon

Havoutis

2019

2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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Different legged robot locomotion controllers have different advantages and disadvantages, from speed of motion to energy, computational speed, safety and others. In this paper we propose a method for planning locomotion with multiple controllers and sub-planners, explicitly considering the multiobjective nature of the problem. We propose a parameter-free method that plans in the space of body motion and controller choice, using utopian and lexicographic cost aggregation functions. We empirically analyze the behavior of the method in terms of planning success rates, Pareto-optimality and anytime behavior in cost space. We show that our method is faster than pure footstep planning methods both in computation (2x) and mission time (1.4x), it is safer than pure dynamic-walking methods, and achieves desirable Pareto-optimal solutions (up to 8x) faster than fairly-tuned traditional weighted-sum methods. Our conclusions are drawn from a combination of planning, physics simulation, and real robot experiments.

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Section: Related Workmentioning

confidence: 99%

Multi-controller multi-objective locomotion planning for legged robots

Brandão

Fallon

Havoutis

2019

2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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“…Homotopy identification is easy for low resolution work spaces; however, the concept is extremely computationally expensive when applied directly on high dimensional configuration spaces. Dornbush et al showed a powerful capability of the planning techniques being able to solve for motion of a large degree‐of‐freedom humanoid showing walking, climbing, and crawling behaviors. However, reduction in the number of state variables and taking only the most relevant ones were done for each stage.…”

Section: Related Workmentioning

confidence: 99%

On sampling inside obstacles for boosted sampling of narrow corridors

Kala

2019

Computational Intelligence

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Narrow corridors are a cause of intense problems in sampling-based approaches due to the small probability of generation of samples inside the narrow corridor. The obstacle-based sampling and bridge test sampling techniques rely on generating fresh samples at every iteration.Memory of the forbidden configuration space can lead to the discovery of new narrow corridors or generating additional key samples inside the same narrow corridor.Hence, in this paper, it is proposed to additionally solve the problem of generating a roadmap in the forbidden configuration space, called as the dual roadmap. The dual roadmap, so generated, has vertices that are collision-prone and stores the structure of the forbidden configuration space. To reduce memory and computation time, only the boundary of the forbidden configuration space is stored, which is more informative. The dual roadmap, so constructed, is used to generate valid samples inside the middle of the narrow corridors. The construction of the additional roadmap takes a very small time and memory and is largely the biproduct of obstacle-based sampling that is normally thrown away. Experimental results show that the proposed sampling method is very effective for finding narrow corridors as compared to popular sampling methodologies existing in the literature. KEYWORDS narrow corridor, probabilistic roadmap, robot motion planning, sampling-based robotics 430

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“…Thus, the system for motion synthesis has to offer a biologically inspired online modification of planned path and walking parameters and be supported by a robust motion control system that can compensate disturbances while constantly preserving dynamic balance. Path planner introduced by Dornbush et al (2018) decomposes the task into a sequence of smaller tasks and focuses the planning efforts to reason over much smaller search spaces. We find this is a human-like approach were local obstacles should be treated first, but always working on solving the main task.…”

Section: Introductionmentioning

confidence: 99%

Human-Inspired Online Path Planning and Biped Walking Realization in Unknown Environment

et al. 2019

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The focus of research in biped locomotion has moved toward real-life scenario applications, like walking on uneven terrain, passing through doors, climbing stairs and ladders. As a result, we are witnessing significant advances in the locomotion of biped robots, enabling them to move in hazardous environments while simultaneously accomplishing complex manipulation tasks. Yet, considering walking in an unknown environment, the efficiency of humanoid robots is still far from being comparable with the human. Currently, bipeds are very sensitive to external changes and they have severe constraints for adaptation of walk to conditions from such a complex environment. Promising approaches for efficient generation and realization of walking in a complex environment are based on biological solutions that have been developed for many years of evolution. This work presents one such human-inspired methodology for path planning and realization of biped walk appropriate for motion in a complex unfamiliar environment. Path planning results in calculating clothoid curves that represent well the human-like walking path. The robot walk is realized by the composition of parametric motion primitives. Such an approach enables on-line modification of planned path and walk parameters at any moment, instantly. To establish the relationship between high-level path planner and the low-level joint motion realization, we had to find a way to extract the parameters of the clothoid paths that can be linked with the parameters of the walk and consequently to motion primitive parameters. This enabled the robot to adopt its walking for avoiding the obstacles and for a smooth transition between different paths. In this paper we provide a complete framework that integrates the following components: (i) bio-inspired online path planning, (ii) path-dependent automatic calculation of high-level gait parameters (step length, walking speed, direction, and the height of the foot sole), and (iii) automatic calculation of low-level joint movements and corresponding control terms (driving motor voltage) through the adaptation of motion primitives which realize walking pattern and preserves the dynamic balance of the robot.

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A Single-Planner Approach to Multi-Modal Humanoid Mobility

Cited by 12 publications

References 17 publications

Multi-controller multi-objective locomotion planning for legged robots

Multi-controller multi-objective locomotion planning for legged robots

On sampling inside obstacles for boosted sampling of narrow corridors

Human-Inspired Online Path Planning and Biped Walking Realization in Unknown Environment

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