Reactive Trajectory Generation in an Unknown Environment

Autonomous trajectory generation for unmanned aerial vehicles (UAVs) in unknown environments continues to be an important research area as UAVs become more prolific. We define a trajectory generation algorithm for a vehicle in an unknown environment with wind disturbances, that relies only on the vehicle's on-board distance sensors and communication with other vehicles within a finite region to generate a smooth, collision-free trajectory up to the fourth derivative. The proposed trajectory generation algorithm can be used in conjunction with high-level planners and low-level motion controllers. The algorithm provides guarantees that the trajectory does not violate the vehicle's thrust limitation, sensor constraints, or a user-defined clearance radius around other vehicles and obstacles. Simulation results of a quadrotor moving through an unknown environment with a moving obstacle demonstrates the trajectory generation performance.Comment: Revised version with minor text updates and more representative simulation results for IROS 2017 conferenc

On the Power of Manifold Samples in Exploring Configuration Spaces and the Dimensionality of Narrow Passages

We extend our study of Motion Planning via Manifold Samples (MMS), a general algorithmic framework that combines geometric methods for the exact and complete analysis of low-dimensional configuration spaces with sampling-based approaches that are appropriate for higher dimensions. The framework explores the configuration space by taking samples that are entire low-dimensional manifolds of the configuration space capturing its connectivity much better than isolated point samples. The contributions of this paper are as follows: (i) We present a recursive application of MMS in a six-dimensional configuration space, enabling the coordination of two polygonal robots translating and rotating amidst polygonal obstacles. In the adduced experiments for the more demanding test cases MMS clearly outperforms PRM, with over 20-fold speedup in a coordination-tight setting. (ii) A probabilistic completeness proof for the most prevalent case, namely MMS with samples that are affine subspaces. (iii) A closer examination of the test cases reveals that MMS has, in comparison to standard sampling-based algorithms, a significant advantage in scenarios containing high-dimensional narrow passages. This provokes a novel characterization of narrow passages which attempts to capture their dimensionality, an attribute that had been (to a large extent) unattended in previous definitions.Comment: 20 page

Conservative collision prediction and avoidance for stochastic trajectories in continuous time and space

Existing work in multi-agent collision prediction and avoidance typically assumes discrete-time trajectories with Gaussian uncertainty or that are completely deterministic. We propose an approach that allows detection of collisions even between continuous, stochastic trajectories with the only restriction that means and variances can be computed. To this end, we employ probabilistic bounds to derive criterion functions whose negative sign provably is indicative of probable collisions. For criterion functions that are Lipschitz, an algorithm is provided to rapidly find negative values or prove their absence. We propose an iterative policy-search approach that avoids prior discretisations and yields collision-free trajectories with adjustably high certainty. We test our method with both fixed-priority and auction-based protocols for coordinating the iterative planning process. Results are provided in collision-avoidance simulations of feedback controlled plants.Comment: This preprint is an extended version of a conference paper that is to appear in \textit{Proceedings of the 13th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2014)

Constraint-aware distributed robotic assembly and disassembly

Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2012.Cataloged from PDF version of thesis.Includes bibliographical references (p. 83-85).In this work, we present a distributed robotic system capable of the efficient assembly and disassembly of complex three-dimensional structures. We introduce algorithms for equitable partitioning of work across robots and for the efficient ordering of assembly or disassembly tasks while taking physical constraints into consideration. We then extend these algorithms to a variety of real-world situations, including when component parts are unavailable or when the time requirements of assembly tasks are non-uniform. We demonstrate the correctness and efficiency of these algorithms through a multitude of simulations. Finally, we introduce a mobile robotic platform and implement these algorithms on them. We present experimental data from this platform on the effectiveness and applicability of our algorithms.by Timothy Ryan Schoen.M.Eng

Distributed Maze Solving By Cooperative Robotic Platforms

Problem solving based on sensor created area maps is a challenging problem that can benefit from a multi-robot approach. Cooperative problems are most eloquently designed through distributed services and systems. This thesis designs and implements a full distributed maze solving solution using simulated robotic sensor platforms. A distributed spatial communication system was developed and tested as a contributing element of the maze solving solution. Autonomous algorithms for communication, cooperation, and navigation were constructed and tested through simulation in maze solving tests. Working with an assumed map creating technology in tandem with the aforementioned developed technologies resulted in an effective complete solution. Although a great deal of future work is recommended to address imperfect mapping complications, it was found through simulation and mathematical analysis that multiple cooperative robotic platforms can result in significant performance improvements