Searching for the shortest-path in an unknown or changeable environment is a common problem in robotics and video games, in which agents need to update maps and to perform re-planning in order to complete their missions. D* Lite is a popular incremental heuristic search algorithm (i.e., it utilizes knowledge from previous searches). Its efficiency lies in the fact that it re-expands only those parts of the search-space that are relevant to registered changes and the current state of the agent. In this paper, we propose a new D* Extra Lite algorithm that is close to a regular A*, with reinitialization of the affected search-space achieved by search-tree branch cutting. The provided worst-case complexity analysis strongly suggests that D* Extra Lite’s method of reinitialization is faster than the focused approach to reinitialization used in D* Lite. In comprehensive tests on a large number of typical two-dimensional path-planning problems, D* Extra Lite was 1.08 to 1.94 times faster than the optimized version of D* Lite. Moreover, while demonstrating that it can be particularly suitable for difficult, dynamic problems, as the problem-complexity increased, D* Extra Lite’s performance further surpassed that of D*Lite. The source code of the algorithm is available on the open-source basis.

The paper presents Asymptotically Optimal A* (AOA*), a heuristically guided, sampling-based algorithm for motion planning without a steering function. AOA* is an attempt to adapt the A* algorithm for planning in a continuous search space while preserving the simplicity of the original A* and using a heuristic function as simple as an Euclidean distance. Using techniques such as adaptive cost-to-come-based pruning, open-list reinitialization, and adaptive blossom number adjustment, AOA* outperforms algorithms such as SST*, AO-RRT, and DIRT in terms of time to the first solution and convergence rate.

This paper presents the preliminary results of the work on a control algorithm for a two-finger gripper equipped with an electronic skin (e-skin). The e-skin measures the magnitude and location of the pressure applied to it. Contact localization allowed the development of a reliable control algorithm for robotic grasping. The main contribution of this work is the learning algorithm that adjusts the pose of the gripper during the pre-grasp approach step based on contact information. The algorithm was tested on different objects and showed comparable grasping reliability to the vision-based approach. The developed tactile sensor-rich gripper with a dedicated control algorithm may find applications in various fields, from industrial robotics to advanced interactive robots.