35,925 research outputs found
Automated Vehicles Have Arrived: What\u27s a Transit Agency to Do?
Ongoing innovations in automated and connected road vehicles create a path of radical transformation of personal mobility, the automotive industry, trucking, public transit, the taxi industry, urban planning, transportation infrastructure, jobs, vehicle ownership, and other physical and social aspects of our built world and daily lives.
In considering automated vehicle (AV) deployments and their cost, as well as the changes in traffic volume, congestion, rights of way, and the complexities of mixed fleets with both automated and non-automated vehicles, the time frame of impacts can only be surmised.
Still, it is worth considering a framework for understanding and managing the forthcoming process of change covered in this perspective
TZC: Efficient Inter-Process Communication for Robotics Middleware with Partial Serialization
Inter-process communication (IPC) is one of the core functions of modern
robotics middleware. We propose an efficient IPC technique called TZC (Towards
Zero-Copy). As a core component of TZC, we design a novel algorithm called
partial serialization. Our formulation can generate messages that can be
divided into two parts. During message transmission, one part is transmitted
through a socket and the other part uses shared memory. The part within shared
memory is never copied or serialized during its lifetime. We have integrated
TZC with ROS and ROS2 and find that TZC can be easily combined with current
open-source platforms. By using TZC, the overhead of IPC remains constant when
the message size grows. In particular, when the message size is 4MB (less than
the size of a full HD image), TZC can reduce the overhead of ROS IPC from tens
of milliseconds to hundreds of microseconds and can reduce the overhead of ROS2
IPC from hundreds of milliseconds to less than 1 millisecond. We also
demonstrate the benefits of TZC by integrating with TurtleBot2 that are used in
autonomous driving scenarios. We show that by using TZC, the braking distance
can be shortened by 16% than ROS
Multi-target detection and recognition by UAVs using online POMDPs
This paper tackles high-level decision-making techniques for robotic missions, which involve both active sensing and symbolic goal reaching, under uncertain probabilistic environments and strong time constraints. Our case study is a POMDP model of an online multi-target detection and recognition mission by an autonomous UAV.The POMDP model of the multi-target detection and recognition problem is generated online from a list of areas of interest, which are automatically extracted at the beginning of the flight from a coarse-grained high altitude observation of the scene. The POMDP observation model relies on a statistical abstraction of an image processing algorithm's output used to detect targets. As the POMDP problem cannot be known and thus optimized before the beginning of the flight, our main contribution is an ``optimize-while-execute'' algorithmic framework: it drives a POMDP sub-planner to optimize and execute the POMDP policy in parallel under action duration constraints. We present new results from real outdoor flights and SAIL simulations, which highlight both the benefits of using POMDPs in multi-target detection and recognition missions, and of our`optimize-while-execute'' paradigm
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