4 research outputs found
Sampling-based Motion Planning via Control Barrier Functions
Robot motion planning is central to real-world autonomous applications, such
as self-driving cars, persistence surveillance, and robotic arm manipulation.
One challenge in motion planning is generating control signals for nonlinear
systems that result in obstacle free paths through dynamic environments. In
this paper, we propose Control Barrier Function guided Rapidly-exploring Random
Trees (CBF-RRT), a sampling-based motion planning algorithm for continuous-time
nonlinear systems in dynamic environments. The algorithm focuses on two
objectives: efficiently generating feasible controls that steer the system
toward a goal region, and handling environments with dynamical obstacles in
continuous time. We formulate the control synthesis problem as a Quadratic
Program (QP) that enforces Control Barrier Function (CBF) constraints to
achieve obstacle avoidance. Additionally, CBF-RRT does not require nearest
neighbor or collision checks when sampling, which greatly reduce the run-time
overhead when compared to standard RRT variants
RRT*FN (fixed nodes) - a novel path planning algorithm with effective memory utilization
In this work, we present a modified version of the RRT* motion planning algorithm, which limits the memory required for storing the tree. We run the RRT* algorithm until the tree has grown to a predefined number of nodes and afterwards we remove a weak node whenever a high performance node is added. A simple two-dimensional navigation problem is used to show the operation of the algorithm. The algorithm was also applied to a high-dimensional redundant robot manipulation problem to show the efficacy. The results show that our algorithm outperforms RRT and comes close to RRT* with respect
to the optimality of returned path, while needing much less number of nodes stored in the tree
RRT*FN (fixed nodes) - a novel path planning algorithm with effective memory utilization
In this work, we present a modified version of the RRT* motion planning algorithm, which limits the memory required for storing the tree. We run the RRT* algorithm until the tree has grown to a predefined number of nodes and afterwards we remove a weak node whenever a high performance node is added. A simple two-dimensional navigation problem is used to show the operation of the algorithm. The algorithm was also applied to a high-dimensional redundant robot manipulation problem to show the efficacy. The results show that our algorithm outperforms RRT and comes close to RRT* with respect
to the optimality of returned path, while needing much less number of nodes stored in the tree