being one of the major research fields in the robotics discipline, the robot motion planning problem deals with finding an obstacle-free start-to-goal path for a robot navigating among workspace obstacles. such a problem is also encountered in path planning of intelligent vehicles and automatic guided vehicles (agvs). traditional (exact) algorithms have failed to solve the problem effectively s...

Rapidly exploring Random Tree (RRT) path planning methods provide feasible paths between a start and goal point in configuration spaces containing obstacles, sacrificing optimality (eg. Shortest path) for speed. The raw resultant paths are generally jagged and the cost of extending the tree can increase steeply as the number of existing branches grow. This paper provides details of a speed-up m...

In this paper, using the Hypercube Diagonal Experiment we first investigate the convergence rates of samplingbased path-planning algorithms in terms of the dimensionnality of the search space. We show that the probability of sampling a point that improves the solution decreases exponentially with the dimension of the problem. We then analyze how the samples can be repositioned in the search spa...

Geometry Friends (GF) is a physics-based platform game, where players control one of two characters (a circle and a rectangle) through a series of both individual and cooperative levels. Each level is solved by retrieving a set of collectibles. This paper proposes an approach using Rapidly-exploring RandomTrees (RRTs) to find a solution for the individual levels of Geometry Friends. Solving a l...

In interactive human-robot path-planning, a capability for expressing the path topology provides a natural mechanism for describing task requirements. We propose a topology-aware RRT* algorithm that can explore in parallel any given set of topologies. The topological information used by the algorithm can either be assigned by the human prior to the planning or be selected from the human in post...

Trajectory design for high-dimensional systems with nonconvex constraints is a challenging problem considered in this paper. Classical dynamic programming is often employed, but can only find a global optimal solution for low-dimensional problems. Recently, randomized techniques were introduced into trajectory design with considerable success; however, their performance is not reliable when pro...

We propose a motion planner for car-like robots based on the rapidly-exploring random tree (RRT) method. Our motion planner was designed especially for cars driving on roads. So, its goal is to build trajectories from the car’s initial state to the goal state in real time, which stay within the desired lane bounds and keep a safe distance from obstacles. For that, our motion planner combines se...

In this paper we present a simple, computationally-efficient, two-tree variant of the RRT∗ algorithm along with several heuristics.

Kinodynamic motion planning is the problem of finding a collision-free path for a robot under constraints e.g., velocities and accelerations. State of the art techniques rely on sampling-based planning which samples and connects configurations until a valid path is found. Many sampling-based planners have been developed for non-holonomic problems e.g., Kinodynamic Rapidly-exploring Random Trees...