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Register a new userCollisionIK: A Per-Instant Pose Optimization Method for Generating Robot Motions with Environment Collision Avoidance
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Added by
Daniel Rakita
Publication date
2021
fields
Informatics Engineering
and research's language is
English
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In this work, we present a per-instant pose optimization method that can generate configurations that achieve specified pose or motion objectives as best as possible over a sequence of solutions, while also simultaneously avoiding collisions with static or dynamic obstacles in the environment. We cast our method as a multi-objective, non-linear constrained optimization-based IK problem where each term in the objective function encodes a particular pose objective. We demonstrate how to effectively incorporate environment collision avoidance as a single term in this multi-objective, optimization-based IK structure, and provide solutions for how to spatially represent and organize external environments such that data can be efficiently passed to a real-time, performance-critical optimization loop. We demonstrate the effectiveness of our method by comparing it to various state-of-the-art methods in a testbed of simulation experiments and discuss the implications of our work based on our results.
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We present a general decentralized formulation for a large class of collision avoidance methods and show that all collision avoidance methods of this form are guaranteed to be collision free. This class includes several existing algorithms in the literature as special cases. We then present a particular instance of this collision avoidance method, CARP (Collision Avoidance by Reciprocal Projections), that is effective even when the estimates of other agents positions and velocities are noisy. The methods main computational step involves the solution of a small convex optimization problem, which can be quickly solved in practice, even on embedded platforms, making it practical to use on computationally-constrained robots such as quadrotors. This method can be extended to find smooth polynomial trajectories for higher dynamic systems such at quadrotors. We demonstrate this algorithms performance in simulations and on a team of physical quadrotors. Our method finds optimal projections in a median time of 17.12ms for 285 instances of 100 randomly generated obstacles, and produces safe polynomial trajectories at over 60hz on-board quadrotors. Our paper is accompanied by an open source Julia implementation and ROS package.
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Perceiving obstacles and avoiding collisions is fundamental to the safe operation of a robot system, particularly when the robot must operate in highly dynamic human environments. Proximity detection using on-robot sensors can be used to avoid or mitigate impending collisions. However, existing proximity sensing methods are orientation and placement dependent, resulting in blind spots even with large numbers of sensors. In this paper, we introduce the phenomenon of the Leaky Surface Wave (LSW), a novel sensing modality, and present AuraSense, a proximity detection system using the LSW. AuraSense is the first system to realize no-dead-spot proximity sensing for robot arms. It requires only a single pair of piezoelectric transducers, and can easily be applied to off-the-shelf robots with minimal modifications. We further introduce a set of signal processing techniques and a lightweight neural network to address the unique challenges in using the LSW for proximity sensing. Finally, we demonstrate a prototype system consisting of a single piezoelectric element pair on a robot manipulator, which validates our design. We conducted several micro benchmark experiments and performed more than 2000 on-robot proximity detection trials with various potential robot arm materials, colliding objects, approach patterns, and robot movement patterns. AuraSense achieves 100% and 95.3% true positive proximity detection rates when the arm approaches static and mobile obstacles respectively, with a true negative rate over 99%, showing the real-world viability of this system.
This paper considers the problem of robot motion planning in a workspace with obstacles for systems with uncertain 2nd-order dynamics. In particular, we combine closed form potential-based feedback controllers with adaptive control techniques to guarantee the collision-free robot navigation to a predefined goal while compensating for the dynamic model uncertainties. We base our findings on sphere world-based configuration spaces, but extend our results to arbitrary star-shaped environments by using previous results on configuration space transformations. Moreover, we propose an algorithm for extending the control scheme to decentralized multi-robot systems. Finally, extensive simulation results verify the theoretical findings.
Unmanned aerial vehicles (UAVs) are expected to be an integral part of wireless networks, and determining collision-free trajectories for multiple UAVs while satisfying requirements of connectivity with ground base stations (GBSs) is a challenging task. In this paper, we first reformulate the multi-UAV trajectory optimization problem with collision avoidance and wireless connectivity constraints as a sequential decision making problem in the discrete time domain. We, then, propose a decentralized deep reinforcement learning approach to solve the problem. More specifically, a value network is developed to encode the expected time to destination given the agents joint state (including the agents information, the nearby agents observable information, and the locations of the nearby GBSs). A signal-to-interference-plus-noise ratio (SINR)-prediction neural network is also designed, using accumulated SINR measurements obtained when interacting with the cellular network, to map the GBSs locations into the SINR levels in order to predict the UAVs SINR. Numerical results show that with the value network and SINR-prediction network, real-time navigation for multi-UAVs can be efficiently performed in various environments with high success rate.
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