1,720,991 research outputs found
Motion Planning as Online Learning: A Multi-Armed Bandit Approach to Kinodynamic Sampling-Based Planning
Full text linkKinodynamic motion planners allow robots to perform complex manipulation tasks under dynamics constraints or with black-box models. However, they struggle to find high-quality solutions, especially when a steering function is unavailable. This letter presents a novel approach that adaptively biases the sampling distribution to improve the planner's performance. The key contribution is to formulate the sampling bias problem as a non-stationary multi-armed bandit problem, where the arms of the bandit correspond to sets of possible transitions. High-reward regions are identified by clustering transitions from sequential runs of kinodynamic RRT and a bandit algorithm decides what region to sample at each timestep. The letter demonstrates the approach on several simulated examples as well as a 7-degree-of-freedom manipulation task with dynamics uncertainty, suggesting that the approach finds better solutions faster and leads to a higher success rate in execution
Safety-Aware Time-Optimal Motion Planning With Uncertain Human State Estimation
Full text linkHuman awareness in robot motion planning is crucial for seamless interaction with humans. Many existing techniques slow clown, stop, or change the robot's trajectory locally to avoid collisions with humans. Although using the information on the human's state in the path planning phase could reduce future interference with the human's movements and make safety stops less frequent, such an approach is less widespread. This paper proposes a novel approach to embedding a human model in the robot's path planner. The method explicitly addresses the problem of minimizing the path execution time, including slowdowns and stops owed to the proximity of humans. For this purpose, it converts safety speed limits into configuration-space cost functions that drive the path's optimization. The costmap can be updated based on the observed or predicted state of the human. The method can handle deterministic and probabilistic representations of the human state and is independent of the prediction algorithm. Numerical and experimental results on an industrial collaborative cell demonstrate that the proposed approach consistently reduces the robot's execution time and avoids unnecessary safety speed reductions
Real-time trajectory scaling for robot manipulators
Full text linkRecent developments in industrial robotics use real-time trajectory modification to improve throughput and safety in automatic processes. Online trajectory scaling is often used to this purpose. In this paper, we propose a feedback trajectory scaling approach that is able to recover from the delay introduced by the speed modulation and improves the path-following performance thanks to an additional inner control loop. Simulation and experimental results on an industrial 6-degree-of-freedom robot show the effectiveness of the proposed approach compared to standard algorithms
Adaptive hybrid local-global sampling for fast informed sampling-based optimal path planning
No full textEnergy Minimization in Time-Constrained Robotic Tasks via Sequential Quadratic Programming
No full textInverse Kinematics of Redundant Manipulators With Dynamic Bounds on Joint Movements
Full text linkRedundant manipulators are usually required to perform tasks in the operational space, but collision-free path planning is computed in the configuration space. Limiting the deviation with respect to the collision-free configuration-space trajectory may allow the robot to avoid collisions without modifying the primary task. This letter proposes a method to guarantee that the solution of the inverse kinematic problem deviates from the nominal joint-space trajectory less than a desired threshold. The excursion limitation is ensured by means of linear constraints and the automatic regulation of the weights of secondary tasks. Numerical and experimental results prove the validness of the proposed approach
Predictive Inverse Kinematics for Redundant Manipulators: Evaluation in Re-Planning Scenarios
No full textIn this paper, we analyze the effectiveness of a predictive redundancy resolution for constrained manipulators in case of on-line re-planning. The method is suitably modified to cope with re-planning issues, such as possible infeasible motions and position errors. Several re-planning scenarios are evaluated. Their definition is based on the smoothness of the re-planned task with respect to the current state of the robot. This allows a deep investigation of the behavior of the method under different conditions. Simulations results on a 7-degree-of-freedom KUKA LWR IV demonstrate remarkable advantages of the predictive method, both in terms of task error and redundancy exploitation
Predictive Inverse Kinematics for Redundant Manipulators with Task Scaling and Kinematic Constraints
No full textThe paper presents a fast online predictive method to solve the task-priority differential inverse kinematics of redundant manipulators under kinematic constraints. It implements a task-scaling technique to preserve the desired geometrical task, when the trajectory is infeasible for the robot capabilities. Simulation results demonstrate the effectiveness of the methodology
Anytime Informed Multi-Path Replanning Strategy for Complex Environments
Full text linkIn many real-world applications (e.g., human-robot collaboration), the environment changes rapidly, and the intended path may become invalid due to moving obstacles. In these situations, the robot should quickly find a new path to reach the goal, possibly without stopping. Planning from scratch or repairing the current graph can be too expensive and time-consuming. This paper proposes MARS, a sampling-based Multi-pAth Replanning Strategy that enables a robot to move in dynamic environments with unpredictable obstacles. The novelty of the method is the exploitation of a set of precomputed paths to compute a new solution in a few hundred milliseconds when an obstacle obstructs the robot's path. The algorithm enhances the search speed by using informed sampling, builds a directed graph to reuse results from previous replanning iterations, and improves the current solution in an anytime fashion to make the robot responsive to environmental changes. In addition, the paper presents a multithread architecture, applicable to several replanning algorithms, to handle the execution of the robot's trajectory with continuous replanning and the collision checking of the traversed path. The paper compares state-of-the-art sampling-based path-replanning algorithms in complex and high-dimensional scenarios, showing that MARS is superior in terms of success rate and quality of solutions found. An open-source ROS-compatible implementation of the algorithm is also provided
How motion planning affects human factors in human-robot collaboration
Full text linkDependability of robot co-workers plays an important role in increasing the effectiveness of human-robot interaction in manufacturing. Our goal is to understand the role of motion planning parameters in human-robot collaboration and to provide guidelines for the selection of the most suitable motion planner. The human factors analysis provided in this paper highlights that repeatability of the motion and predictability of the robot timing affect the quality of human-robot collaboration. Copyright (C) 2020 The Authors
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