The key problem for autonomous robots is how to navigate through complex, obstacle filled environments . The navigation problem is broken up into generating a  reference path to the goal and then using a local planner to track the reference path and avoid obstacles by generating velocity and steering references for a control system to execute. This paper presents a novel local planner architecture for path following and obstacle avoidance by proposing a hybrid system that uses a classic path following algorithm in parallel with a neural network. Our solution, called the modification architecture, uses a path following algorithm to follow a reference path and a neural network to modify the references generated by the path follower in order to prevent collisions. The neural network is trained using reinforcement learning to deviate from the reference trajectory just enough to avoid crashing. We present our local planner in a hierarchical planning framework, nested between a global reference planner and a low-level control system. The system is evaluated in the context of F1/10th scale autonomous racing in random forests and on a race track . The results demonstrate that our solution is able to follow a reference trajectory while avoiding obstacles using only 10 sparse laser range finder readings. Our hybrid system overcomes previous limitations by not requiring an obstacle map, human demonstrations, or an instrumented training setup .
The key problem for autonomous robots is how to navigate through complex, obstacle-filled environments, without colliding into obstacles . The navigation problem is split into generating a global reference path to the goal and then using a local planner to track the reference path and avoid obstacles by generating velocity and steering references for the robot control system to execute. This paper presents a hybrid local planning architecture that combines a classic path following algorithm with a deep reinforcement learning agent. The novel "modification planner" uses the path follower to track the global plan and the deep reinforcement learning agent to modify the references generated by the path follower to avoid obstacles. Importantly, our architecture does not require an updated obstacle map and only 10 laser range finders to avoid obstacles. The modification planner is evaluated in the context of F1/10th autonomous racing and compared to a mapless navigation baseline, the Follow the Gap Method and an optimisation based planner . The results show that the modification planner can achieve faster average times compared to the baseline mapless navigation planner and a 94\% success rate which is similar to the baseline .
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obstacle filled environments
obstacle-filled environments, without colliding into obstacles
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broken up
split
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global
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the robot
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novel local planner architecture for path following and obstacle avoidance by proposing a hybrid system that uses
hybrid local planning architecture that combines
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in parallel with a neural network. Our solution, called the modification architecture, uses a path following algorithm to follow a reference path and a neural network
with a deep reinforcement learning agent. The novel "modification planner" uses the path follower to track the global plan and the deep reinforcement learning agent
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in order to prevent collisions. The neural network is trained using reinforcement learning to deviate from the reference trajectory just enough to avoid crashing. We present our local planner in a hierarchical planning framework, nested between a global reference planner and a low-level control system. The system
to avoid obstacles. Importantly, our architecture does not require an updated obstacle map and only 10 laser range finders to avoid obstacles. The modification planner
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scale autonomous racing in random forests and on a race track
autonomous racing and compared to a mapless navigation baseline, the Follow the Gap Method and an optimisation based planner
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demonstrate that our solution is able to follow a reference trajectory while avoiding obstacles using only 10 sparse laser range finder readings. Our hybrid system overcomes previous limitations by not requiring an obstacle map, human demonstrations, or an instrumented training setup
show that the modification planner can achieve faster average times compared to the baseline mapless navigation planner and a 94\% success rate which is similar to the baseline