2,161 research outputs found
A Model-Predictive Motion Planner for the IARA Autonomous Car
We present the Model-Predictive Motion Planner (MPMP) of the Intelligent
Autonomous Robotic Automobile (IARA). IARA is a fully autonomous car that uses
a path planner to compute a path from its current position to the desired
destination. Using this path, the current position, a goal in the path and a
map, IARA's MPMP is able to compute smooth trajectories from its current
position to the goal in less than 50 ms. MPMP computes the poses of these
trajectories so that they follow the path closely and, at the same time, are at
a safe distance of eventual obstacles. Our experiments have shown that MPMP is
able to compute trajectories that precisely follow a path produced by a Human
driver (distance of 0.15 m in average) while smoothly driving IARA at speeds of
up to 32.4 km/h (9 m/s).Comment: This is a preprint. Accepted by 2017 IEEE International Conference on
Robotics and Automation (ICRA
Network Latency in Teleoperation of Connected and Autonomous Vehicles:A Review of Trends, Challenges, and Mitigation Strategies
With remarkable advancements in the development of connected and autonomous vehicles (CAVs), the integration of teleoperation has become crucial for improving safety and operational efficiency. However, teleoperation faces substantial challenges, with network latency being a critical factor influencing its performance. This survey paper explores the impact of network latency along with state-of-the-art mitigation/compensation approaches. It examines cascading effects on teleoperation communication links (i.e., uplink and downlink) and how delays in data transmission affect the real-time perception and decision-making of operators. By elucidating the challenges and available mitigation strategies, the paper offers valuable insights for researchers, engineers, and practitioners working towards the seamless integration of teleoperation in the evolving landscape of CAVs
Simulating and Training Autonomous Rover Navigation in Unity Engine Using Local Sensor Data
Autonomous navigation is essential to remotely operating mobile vehicles on Mars, as communication takes up to 20 minutes to travel between the Earth and Mars. Several autonomous navigation methods have been implemented in Mars rovers and other mobile robots, such as odometry or simultaneous localization and mapping (SLAM) until the past few years when deep reinforcement learning (DRL) emerged as a viable alternative. In this thesis, a simulation model for end-to-end DRL Mars rover autonomous navigation training was created using Unity Engine, using local inputs such as GNSS, LiDAR, and gyro. This model was then trained in navigation in a flat environment using the proximal policy optimization (PPO) algorithm. The results of the training and future work are discussed
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