Creating High-quality Paths for Motion Planning

Abstract

Many algorithms have been proposed that create a path for a robot in an environment with obstacles. Most methods are aimed at finding a solution. However, for many applications, the path must be of a good quality as well. That is, a path should be short and should keep some amount of minimum clearance to the obstacles. Traveling along such a path reduces the chances of collisions due to the difficulty of measuring and controlling the precise position of the robot. This paper reports a new technique, called Partial shortcut, which decreases the path length. While current methods have difficulties in removing all redundant motions, the technique efficiently removes these motions by interpolating one degree of freedom at a time. Two algorithms are also studied that increase the clearance along paths. The first one is fast but can only deal with rigid, translating bodies. The second algorithm is slower but can handle a broader range of robots, including three-dimensional free-flying and articulated robots, which may reside in arbitrary high-dimensional configuration spaces. A big advantage of these algorithms is that clearance along paths can now be increased efficiently without using complex data structures and algorithms. Finally, we combine the two criteria and show that highquality paths can be obtained for a broad range of robots.

Motion Planning for 6-D Manipulation with Aerial Towed-cable Systems

“…collision-free and satisfying the aforementioned feasibility constraints) configurations. After performing a smoothing operation (based on the random shortcut method [8]) on the paths generated by RRT and T-RRT, we evaluate the path quality by computing the average cost avgC, the maximal cost maxC, the mechanical work M W , and the integral of the cost IC. The mechanical work of a path is the sum of the positive cost variations along the path [9].…”

Efficient navigation for anyshape holonomic mobile robots in dynamic environments

“…Whereas this approach finds feasible solutions and paths in high-dimensions [9], it assumes that the environment is static and tends to converge to a solution that is suboptimal. Therefore, paths computed by RRT-based motion planning algorithms are typically post-processed to reduce the effects of randomization [3,5]. Even though numerous methods for motion planning and trajectory optimization can be found in the literature, the trade-off between the optimality of the solution and the computational effort is still crucial.…”

LQG-MP: Optimized Path Planning for Robots with Motion Uncertainty and Imperfect State Information

“…This can be addressed by smoothing. However, most smoothing techniques in randomized motion planning repeatedly attempt to reduce path length between two randomly chosen states along the path [8]. These techniques tend to bring the path closer to obstacles, which can be suboptimal with respect to the planning objective, for instance when mimimizing the probability of collisions, and tend to map multiple paths to the same smoothed result, hence they are not ideal for LQG-MP.…”