Contact Sequence Planning for Hexapod Robots in Sparse Foothold Environment Based on Monte-Carlo Tree

Xu, Peng; Ding, Liang; Wang, Zhikai; Gao, Haibo; Zhou, Ruyi; Gong, Zhaopei; Liu, G.

doi:10.1109/lra.2021.3133610

Cited by 20 publications

(10 citation statements)

References 21 publications

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“…Monte Carlo Tree Search (MCTS) [10] is a method for solving sequential decision problems. Peng et al proposed a sliding-MCTS method for solving state-sequence planning problems for legged robots and demonstrated that the robots have better traversability in sparse-foothold environments [11], but it still takes a long time to search for the result. Although progress has been made in terms of autonomous motion, the algorithms above cannot satisfy the full autonomy of legged robots due to imperfect environment sensing and localization algorithms.…”

Section: A Related Workmentioning

confidence: 99%

A Closed-Loop Shared Control Framework for Legged Robots

Wang

Ding

et al. 2024

IEEE/ASME Trans. Mechatron.

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Shared control, as a combination of human and robot intelligence, has been deemed as a promising direction towards complementing the perception and learning capabilities of legged robots. However, previous works on human-robot control for legged robots are often limited to simple tasks, such as controlling movement direction, posture, or single-leg motion, yet extensive training of the operator is required. To facilitate the transfer of human intelligence to legged robots in unstructured environments, this paper presents a user-friendly closed-loop shared control framework. The main novelty is that the operator only needs to make decisions based on the recommendations of the autonomous algorithm, without having to worry about operations or consider contact planning issues. Specifically, a rough navigation path from the operator is smoothed and optimized to generate a path with reduced traversing cost. The traversability of the generated path is assessed using fast Monte Carlo tree search (FastMCTS), which is subsequently fed back through an intuitive image interface and force feedback to help the operator make decisions quickly, forming a closed-loop shared control. The simulation and hardware experiments on a hexapod robot show that the proposed framework gives full play to the advantages of human-machine collaboration, and improves the performance in terms of learning time from the operator, mission completion time, and success rate than comparison methods.

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Section: A Related Workmentioning

confidence: 99%

A Closed-Loop Shared Control Framework for Legged Robots

Wang

Ding

et al. 2024

IEEE/ASME Trans. Mechatron.

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“…Researchers have made significant advancements in robot stability margin measurement, a key component in motion planning [29], trajectory generation [30], and motion control [31], which is crucial for maintaining and enhancing robot stability. However, little attention has been given to addressing tipping recovery mechanisms, especially for the heavy-duty hexapod robot with large inertia, complex motion strategies, and limited leg motion space.…”

Section: Introductionmentioning

confidence: 99%

Heavy-duty hexapod robot sideline tipping judgment and recovery

Zhang,

Zha,

Guo

et al. 2024

Robotica

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Heavy-duty hexapod robots are well-suited for physical transportation, disaster relief, and resource exploration. The immense locomotion capabilities conferred by the six appendages of these systems enable traversal over unstructured and challenging terrain. However, tipping can be a serious concern when moving with a tripod gait in these challenging environments, which may cause irreversible consequences such as compromised movement control and potential damage. In this paper, we focus on heavy-duty hexapod robot sideline tipping judgment and recovery during tripod gait motion, and a novel sideline tipping judgment and recovery method is proposed by adjusting an optimal swinging leg to the stance state. Considering the locomotion environments, motion mode, and tipping analysis, the robot’s stability margin is quantified, and the tipping event is evaluated by the Force Angle Stability Measure (FASM). The recovery method is initiated upon detecting that the robot is tipping, which involves the selection of an adjustment leg and the determination of an optimal foothold. Since the FASM is based on the foot force and robot center of gravity (CoG), the stability margin quantification expression is reformulated to the constraint form of quadratic programming (QP). Furthermore, a foot force distribution method, integrating stability margin considerations into the QP model, has been devised to ensure post-adjustment stability of the landing leg. Experiments on tipping judgment and recovery demonstrate the effectiveness of the proposed approaches on tipping judgment and recovery.

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“…Liang used the three-dimensional quasi-static equilibrium support region (3D QESR) as the constraint of the planning method for complex terrain and realized the quasi-static stable motion of the hexapod robot [ 19 ]. XuPeng treats the six-legged robot gait and foothold planning as a sequence optimization problem for the sparse drop point environment and uses the Monte Carlo tree search (MCTS) algorithm to optimize the entire traversal motion sequence to achieve balanced movement in a harsh environment [ 20 ].…”

Section: Introductionmentioning

confidence: 99%

Autonomous Planning of Discontinuous Terrain-Dependent Crawling for Space Dobby Robots

Jiang

Wei

Yu³

et al. 2023

Sensors

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Complex space missions require more space robotic extravehicular operations required to crawl on spacecraft surfaces with discontinuous features at the graspable point, greatly increasing the difficulty of space robot motion manipulation. Therefore, this paper proposes an autonomous planning method for space dobby robots based on dynamic potential fields. This method can realize the autonomous crawling of space dobby robots in discontinuous environments while considering the task objectives and the self-collision problem of robotic arms when crawling. In this method, a hybrid event–time trigger with event triggering as the main trigger is proposed by combining the working characteristics of space dobby robots and improving the gait timing trigger; the dynamic potential field function is designed to adjust the space robot robotic arm grasping point adaptively according to the space robot state. Simulation results verify the effectiveness of the proposed autonomous planning method.

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Contact Sequence Planning for Hexapod Robots in Sparse Foothold Environment Based on Monte-Carlo Tree

Cited by 20 publications

References 21 publications

A Closed-Loop Shared Control Framework for Legged Robots

A Closed-Loop Shared Control Framework for Legged Robots

Heavy-duty hexapod robot sideline tipping judgment and recovery

Autonomous Planning of Discontinuous Terrain-Dependent Crawling for Space Dobby Robots

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