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Sampling-based Path Planner for Robot Navigation in Dynamic Environments
The goal of this project is to implement a sampling-based path planner based on the RRTx algorithm in order to perform autonomous navigation and obstacle avoidance for different robots while navigating towards a goal configuration.
In the recent years, the use of robotic platforms has increased exponentially to a wide range of different settings and situations, such as mapping and inspection. In order to enable robots to navigate in complex, unknown and dynamic environments, they must be able to **move with guaranteed safety**, even when facing environments they do not have prior knowledge of. However, the **full autonomous navigation** still remains a big challenge.
The aim of this project is to develop a **sampling-based planner** that performs trajectory-generation for different platforms, such as Unmanned Aerial Vehicles (UAVs) and ground robots, to **navigate in dynamic environments**. The objective is to perform continuous trajectory re-planning in order to make the robot **able to react to sudden changes of the navigation environment**. The goals are the following:
- Implementation of the _RRTx_ algorithm for dynamic environments.
- Evaluation of the algorithm against other state-of-the-art sampling based planners.
- Use of the planner for the navigation of a real platform (UAV and/or ground robot).
The student will have the opportunity to work on a challenging project about robot autonomous navigation. We offer the opportunity to work with real set-ups and equipment provided by V4RL.
In the recent years, the use of robotic platforms has increased exponentially to a wide range of different settings and situations, such as mapping and inspection. In order to enable robots to navigate in complex, unknown and dynamic environments, they must be able to **move with guaranteed safety**, even when facing environments they do not have prior knowledge of. However, the **full autonomous navigation** still remains a big challenge.
The aim of this project is to develop a **sampling-based planner** that performs trajectory-generation for different platforms, such as Unmanned Aerial Vehicles (UAVs) and ground robots, to **navigate in dynamic environments**. The objective is to perform continuous trajectory re-planning in order to make the robot **able to react to sudden changes of the navigation environment**. The goals are the following:
- Implementation of the _RRTx_ algorithm for dynamic environments. - Evaluation of the algorithm against other state-of-the-art sampling based planners. - Use of the planner for the navigation of a real platform (UAV and/or ground robot).
The student will have the opportunity to work on a challenging project about robot autonomous navigation. We offer the opportunity to work with real set-ups and equipment provided by V4RL.
The work to be undertaken involves four main work packages (WP):
- WP1: Familiarization with state-of-the-art sampling based planners;
- WP2: Implementation of the _RRTx_ algorithm.
- WP3: Evaluation of the new framework with respect to other already existing planning strategies.
- WP4: Design and conduct experiments with multiple mobile platforms (UAV and/or ground robot) to evaluate the selected approach.
The work to be undertaken involves four main work packages (WP):
- WP1: Familiarization with state-of-the-art sampling based planners; - WP2: Implementation of the _RRTx_ algorithm. - WP3: Evaluation of the new framework with respect to other already existing planning strategies. - WP4: Design and conduct experiments with multiple mobile platforms (UAV and/or ground robot) to evaluate the selected approach.
- Interest in Computer Sciences, Robotics and Autonomous Navigation;
- C++ programming experience;
- Experience in mobile robotics, Linux, ROS is beneficial.
- Interest in Computer Sciences, Robotics and Autonomous Navigation; - C++ programming experience; - Experience in mobile robotics, Linux, ROS is beneficial.
Interested Students please send CV, Bachelor and Master transcripts to Luca Bartolomei (lbartolomei@ethz.ch) and Nitish Kumar (nitish.kumar@inf.ethz.ch).
Interested Students please send CV, Bachelor and Master transcripts to Luca Bartolomei (lbartolomei@ethz.ch) and Nitish Kumar (nitish.kumar@inf.ethz.ch).