Fast Motion Planning of UAVs

Fast motion planning (FMP) of autonomous vehicles has been advanced rapidly for robotics research, particularly for trajectory planning of spacecraft. The FMP team at JPL and Caltech has developed an algorithm for autonomous vehicles in environments with many fixed obstacles. The spherical expansion and sequential convex programming (SE-SCP) algorithm is computationally efficient and guarantees any-time local optimality for a given function on top of being faster than other sampling-based motion planning methods. Spherical Expansion (SE) is randomized sampling to explore the workspace of the autonomous vehicle and it finds an initial cost-minimized path. Sequential convex programming (SCP) then optimizes this path and computes a locally optimal trajectory. Current development and simulation of the SE-SCP algorithm is still being tested with MATLAB software as well as the collaborative robotics software called the Robot Operating System (ROS). ROS has advantages over MATLAB since it is a flexible framework for writing robotics software and includes a collection of tools, libraries, and conventions specifically for robotics improvement. By developing a SE-SCP simulation in ROS, a ROS package can be created and uploaded online, which provides opportunities for the public to easily utilize the software and apply the SE-SCP algorithm for motion planning to their own autonomous vehicles

Multi-robot team formation control in the GUARDIANS project

Purpose The GUARDIANS multi-robot team is to be deployed in a large warehouse in smoke. The team is to assist firefighters search the warehouse in the event or danger of a fire. The large dimensions of the environment together with development of smoke which drastically reduces visibility, represent major challenges for search and rescue operations. The GUARDIANS robots guide and accompany the firefighters on site whilst indicating possible obstacles and the locations of danger and maintaining communications links. Design/methodology/approach In order to fulfill the aforementioned tasks the robots need to exhibit certain behaviours. Among the basic behaviours are capabilities to stay together as a group, that is, generate a formation and navigate while keeping this formation. The control model used to generate these behaviours is based on the so-called social potential field framework, which we adapt to the specific tasks required for the GUARDIANS scenario. All tasks can be achieved without central control, and some of the behaviours can be performed without explicit communication between the robots. Findings The GUARDIANS environment requires flexible formations of the robot team: the formation has to adapt itself to the circumstances. Thus the application has forced us to redefine the concept of a formation. Using the graph-theoretic terminology, we can say that a formation may be stretched out as a path or be compact as a star or wheel. We have implemented the developed behaviours in simulation environments as well as on real ERA-MOBI robots commonly referred to as Erratics. We discuss advantages and shortcomings of our model, based on the simulations as well as on the implementation with a team of Erratics.</p

Public entities driven robotic innovation in urban areas

Cities present new challenges and needs to satisfy and improve lifestyle for their citizens under the concept “Smart City”. In order to achieve this goal in a global manner, new technologies are required as the robotic one. But Public entities unknown the possibilities offered by this technology to get solutions to their needs. In this paper the development of the Innovative Public Procurement instruments is explained, specifically the process PDTI (Public end Users Driven Technological Innovation) as a driving force of robotic research and development and offering a list of robotic urban challenges proposed by European cities that have participated in such a process. In the next phases of the procedure, this fact will provide novel robotic solutions addressed to public demand that are an example to be followed by other Smart Cities.Peer ReviewedPostprint (author's final draft

The Dandelion Program: 2016 in Review

[Excerpt] The Dandelion Program is an initiative to build valuable Information Technology, life, and executive functional skills to help establish careers for people on the autism spectrum. The implementation and assessment of Dandelion pods concentrates on identifying and supporting individuals on the autism spectrum with the potential to excel in testing, data science, and cyber security roles. Over the course of 2016, the program continued to grow at a rapid rate, and achieved a large number of significant milestones. This report will cover some of the most significant developments over the course of the last year

Analysis and Observations from the First Amazon Picking Challenge

This paper presents a overview of the inaugural Amazon Picking Challenge along with a summary of a survey conducted among the 26 participating teams. The challenge goal was to design an autonomous robot to pick items from a warehouse shelf. This task is currently performed by human workers, and there is hope that robots can someday help increase efficiency and throughput while lowering cost. We report on a 28-question survey posed to the teams to learn about each team's background, mechanism design, perception apparatus, planning and control approach. We identify trends in this data, correlate it with each team's success in the competition, and discuss observations and lessons learned based on survey results and the authors' personal experiences during the challenge