Motion planning

About: Motion planning is a research topic. Over the lifetime, 32846 publications have been published within this topic receiving 553548 citations.

Neural network architectures for robotic applications

Abstract: The authors propose a ring VLSI systolic architecture for implementing artificial neural networks (ANNs) with applications to robotic processing. Key design issues concerning algorithms, applications, and architectures are examined. A variety of neural networks is considered, including single-layer feedback neural networks, competitive learning networks, and multilayer feed-forward networks. It is demonstrated that the ANNs are suitable to all three levels of robotic processing applications including task planning, path planning, and path control levels. For these applications, a programmable systolic array is developed than can exploit the strength of VLSI to provide intensive and pipelined computing. Both the retrieving and learning phases are integrated in the design. The proposed architecture, which is more versatile than other existing ANNs, can accommodate all the useful neural networks for robotic processing. >

A new potential field method for robot path planning

Abstract: Presents an artificial potential field method for path planning of non-spherical single-body robots. The model simulates steady-state heat transfer with variable thermal conductivity. The optimal path problem is then the same as a heat flow with minimal thermal resistance. The novelty of this technique is that the thermal resistance in the configuration space for all different orientations of the robot can be superimposed. This reduces a search on R/sup n//spl times/SO(n) to one on R/sup n/ followed by a search on SO(n). Examples are presented to demonstrate the approach.

Network Connectivity Preserving Formation Stabilization and Obstacle Avoidance via a Decentralized Controller

Abstract: A decentralized control method is developed to enable a group of agents to achieve a desired global configuration while maintaining global network connectivity and avoiding obstacles, using only local feedback and no radio communication between the agents for navigation. By modeling the interaction among the agents as a graph, and given a connected initial graph with a desired neighborhood between agents, the developed method ensures the desired communication links remain connected for all time. To guide the agents to a desired configuration while avoiding obstacles, a decentralized controller is developed based on the navigation function formalism. By proving that the proposed controller is a qualified navigation function, convergence to the desired formation is guaranteed.

Energy Efficient UAV Communication With Energy Harvesting

Abstract: This paper investigates an unmanned aerial vehicle (UAV)-enabled wireless communication system with energy harvesting, where the UAV transfers energy to the users in half duplex or full duplex, and the users harvest energy for data transmission to the UAV. We minimize the total energy consumption of the UAV while accomplishing the minimal data transmission requests of the users. The original optimization problem is decomposed into two subproblems: path planning subproblem and energy minimization subproblem with fixed path planning. For path planning subproblem, the optimal visiting order is obtained by using the dual method and the trajectory is optimized via the successive convex approximation technique. For energy minimization subproblem with fixed path planning, we firstly obtain the optimal portion of data transmission time within the entire procedure and the optimal transmission power of each user. Then, the the energy minimization subproblem is greatly simplified and it is efficiently solved via a one-dimensional search method. Simulation results are illustrated to verify the theoretical findings.

Ros_Control: A Generic And Simple Control Framework For Ros

Abstract: In recent years the Robot Operating System (ROS) has become the 'de facto' standard framework for robotics software development. The `ros_control` framework provides the capability to implement and manage robot controllers with a focus on both _real-time performance_ and _sharing of controllers_ in a robot-agnostic way. The primary motivation for a sepate robot-control framework is the lack of realtime-safe communication layer in ROS. Furthermore, the framework implements solutions for controller-lifecycle and hardware resource management as well as abstractions on hardware interfaces with minimal assumptions on hardware or operating system. The clear, modular design of `ros_control` makes it ideal for both research and industrial use and has indeed seen many such applications to date. The idea of `ros_control` originates from the `pr2_controller_manager` framework specific to the PR2 robot but `ros_control` is fully robot-agnostic. Controllers expose standard ROS interfaces for out-of-the box 3rd party solutions to robotics problems like manipulation path planning (`MoveIt!`) and autonomous navigation (the `ROS navigation stack`). Hence, a robot made up of a mobile base and an arm that support `ros_control` doesn't need any additional code to be written, only a few controller configuration files and it is ready to navigate autonomously and do path planning for the arm. `ros_control` also provides several libraries to support writing custom controllers.