Path planning for a tethered robot using Multi-Heuristic A* with topology-based heuristics

Abstract — In this paper, we solve the path planning problem for a tethered mobile robot, which is connected to a fixed base by a cable of length L. The reachable space of the robot is restricted by the length of the cable and obstacles. The reachable space of the tethered robot can be computed by considering the topology class of the cable. However, it is computationally too expensive to compute this space a-priori. Instead, in this paper, we show how we can plan using a recently-developed variant of A * search, called Multi-Heuristic A*. Normally, the Multi-Heuristic A * algorithm takes in a fixed set of heuristic functions. In our problem, however, the heuristics represent length of paths to the goal along different topology classes, and there can be too many of them and not all the topology classes are useful. To deal with this, we adapt Multi-Heuristic A * to work with a dynamically generated set of heuristic functions. It starts out as a normal weighted A*. Whenever the search gets trapped in a local minimum, we find the proper topology class of the path to escape from it and add the corresponding new heuristic function into the set of heuristic functions considered by the search. We present experimental analysis comparing our approach with weighted A * on planning for a tethered robot in simulation. I

A Topological Approach to Workspace and Motion Planning for a Cable-controlled Robot in Cluttered Environments

There is a rising demand for multiple-cable controlled robots in stadiums or warehouses due to its low cost, longer operation time, and higher safety standards. In a cluttered environment the cables can wrap around obstacles. Careful choice needs to be made for the initial cable congurations to ensure that the workspace of the robot is optimized. The presence of cables makes it imperative to consider the homotopy classes of the cables both in the design and motion planning problems. In this thesis we study the problem of workspace planning for multiple-cable controlled robots in an environment with polygonal obstacles. This goal of this thesis is to establish a relationship between the workspace\u27s boundary and cable congurations of such robots, and solve related optimization and motion planning problems. We rst analyze the conditions under which a conguration of a cable-controlled robot can be considered valid, then discuss the relationship between cable conguration, the robot\u27s workspace and its motion state, and finally use graph search based motion planning in h-augmented graph to perform workspace optimization and to compute optimal paths for the robot. We demonstrated corresponding algorithms in simulations

Self–organised multi agent system for search and rescue operations

Autonomous multi-agent systems perform inadequately in time critical missions, while they tend to explore exhaustively each location of the field in one phase with out selecting the pertinent strategy. This research aims to solve this problem by introducing a hierarchy of exploration strategies. Agents explore an unknown search terrain with complex topology in multiple predefined stages by performing pertinent strategies depending on their previous observations. Exploration inside unknown, cluttered, and confined environments is one of the main challenges for search and rescue robots inside collapsed buildings. In this regard we introduce our novel exploration algorithm for multi–agent system, that is able to perform a fast, fair, and thorough search as well as solving the multi–agent traffic congestion. Our simulations have been performed on different test environments in which the complexity of the search field has been defined by fractal dimension of Brownian movements. The exploration stages are depicted as defined arenas of National Institute of Standard and Technology (NIST). NIST introduced three scenarios of progressive difficulty: yellow, orange, and red. The main concentration of this research is on the red arena with the least structure and most challenging parts to robot nimbleness

Path Planning and Control of an Autonomous Quadrotor Testbed in a Cluttered Environment

A classical problem for robotic navigation is how to efficiently navigate from one point to another and what to do if obstacles are encountered along the way. Many map based path planning algorithms attempt to solve this problem, all with varying levels of optimality and complexity. This work shows a review of selected algorithms, and two of these are selected for simulation and testing using a quadrotor unmanned aerial vehicle (UAV) in a dynamic indoor environment which requires replanning capabilities. The Dynamic A* algorithm, or simply D*, and the Probabilistic Roadmap method (PRM) are used in a scenario designed to test their respective functionality and usefulness with the goal of determining the better algorithm for flight testing given a partially known or changing environment.;The development of the quadrotor platform hardware is discussed as well as the associated software and capabilities. Both algorithms are redesigned to fit this specific application and display their respective planned and replanned paths in an intuitive and comparable manner. Simulation is performed and an obstacle is added to the map during the quadrotor motion, requiring a replanned path. Results are compared for both computed path length and computational intensity. Flight testing is performed in an indoor environment, and during the flight an obstacle is inserted into the flight path, requiring detection and replanning. Results are compared for computed path length and intuitively analyzed to compare optimality and complexity

Proceedings of the NASA Conference on Space Telerobotics, volume 1

The theme of the Conference was man-machine collaboration in space. Topics addressed include: redundant manipulators; man-machine systems; telerobot architecture; remote sensing and planning; navigation; neural networks; fundamental AI research; and reasoning under uncertainty

Robot motion planning with contact from global pseudo-inverse map

Thesis: Ph. D., Massachusetts Institute of Technology, Department of Mechanical Engineering, 2018.Cataloged from PDF version of thesis.Includes bibliographical references (pages 101-108).In the robot motion planning problems, environment and its objects are often treated as obstacles to be avoided. However, there are situations where contacting with the environment is not costly. Moreover, in many cases, making contact can actually help a robot to maneuver around to reach a goal state which would not have been possible otherwise. This thesis presents a framework for motion planner that utilizes multiple contacts with the environment and its objects. The planner is targeted to autonomously generate motion, where robot has to make multiple contact with different part of its body in order to achieve a task objective. It is motivated by and has significance in developing a robust humanoid planner that is capable of recovering from a fall down. The recent DRC has been marked with compilation of humanoid robots falling down, but only one robot managed to recover to a standing up position. In a real disaster scenario, the inability to stand up would mean end of the rescue mission for what is extremely expensive machinery. A robust planner capable of recovery is must and this work contributes towards it. The developed planner autonomously generates standing up motion from fall down in the presence of torque limits. The proposed multi-contact motion planner leverages upon following two key components. Existing multi-contact planners require good initial seeds to successfully generate a motion. These are hard to find and often manually encoded. Here, we utilize pre-computed global pseudo-inverse map (inverse kinematic map for each contact-state that has property of global resolution, connected by connectivity functions) to generate multi-contact motion from current configuration to the goal without need for an initial seed. Nevertheless, constructing the global pseudo-inverse map is computationally expensive. In an effort to facilitate the construction, we utilize singular configurations as a heuristic to reduce the search space and justify its use based on the physical analysis. Although computationally expensive, once pre-computed, the global map can be used to generate plans fast online in a multi-query manner.by Changrak Choi.Ph. D

Workshop on Fuzzy Control Systems and Space Station Applications

The Workshop on Fuzzy Control Systems and Space Station Applications was held on 14-15 Nov. 1990. The workshop was co-sponsored by McDonnell Douglas Space Systems Company and NASA Ames Research Center. Proceedings of the workshop are presented

Automated decision making and problem solving. Volume 2: Conference presentations

Related topics in artificial intelligence, operations research, and control theory are explored. Existing techniques are assessed and trends of development are determined

Advancing Quantitative Risk Analysis for Critical Water Infrastructure

Critical infrastructure systems play a vital role in the supply of lifeline services to businesses and the wider public. It is of paramount importance for national security, public health, and economic prosperity that these critical structures function properly. Unfortunately, with respect to drinking water infrastructures in the US, much of the pipeline assets are nearing the end of their useful life and utilities are challenged with maintaining these systems with limited budgets and information. Risk analysis is a useful decision making tool which can allow managers to better identify weaknesses, and aid better investment decisions regarding maintenance, inspection, and repair. The current practice for risk analysis and management of critical water systems falls short of the approaches preferred by risk researchers. The aim of this thesis is to advance to practice and theory. This involves the evaluation of existing methods as well as the incorporation of modern analytical tools to fundamentally advance the state of practice. This thesis first critically analyzes a popular risk analysis standard (J100-10) to establish the knowledge gap between practice and theory in the water domain. Two quantitative methodologies are then explored: machine learning and mathematical optimization. The research here demonstrates how they can be integrated into a broader risk framework and used to improve assessments for water systems. The work presented in this dissertation represents a significant contribution to the field of infrastructure risk and reliability analysis. While the domain application is specific to drinking water systems, the techniques can be applied for other types of networked infrastructures.PHDIndustrial & Operations EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/153423/1/tyjchen_1.pd

Using MapReduce Streaming for Distributed Life Simulation on the Cloud

Distributed software simulations are indispensable in the study of large-scale life models but often require the use of technically complex lower-level distributed computing frameworks, such as MPI. We propose to overcome the complexity challenge by applying the emerging MapReduce (MR) model to distributed life simulations and by running such simulations on the cloud. Technically, we design optimized MR streaming algorithms for discrete and continuous versions of Conway’s life according to a general MR streaming pattern. We chose life because it is simple enough as a testbed for MR’s applicability to a-life simulations and general enough to make our results applicable to various lattice-based a-life models. We implement and empirically evaluate our algorithms’ performance on Amazon’s Elastic MR cloud. Our experiments demonstrate that a single MR optimization technique called strip partitioning can reduce the execution time of continuous life simulations by 64%. To the best of our knowledge, we are the first to propose and evaluate MR streaming algorithms for lattice-based simulations. Our algorithms can serve as prototypes in the development of novel MR simulation algorithms for large-scale lattice-based a-life models.https://digitalcommons.chapman.edu/scs_books/1014/thumbnail.jp