This report presents a methodology to integrate vector field based motion planning techniques with optimal, differential constrained trajectory planners. The main motivation for this integration is the solution of robot motion planning problems that are easily and intuitively solved using vector fields, but are very difficult to be even posed as an optimal motion planning problem, mainly due to...

Mobile manipulators installed in warehouses and factories for conveying goods between working stations need to meet the requirements of time-critical workflows. Moreover, the systems are expected to deal with changing tasks, cluttered environments and constraints imposed by the goods to be delivered. In this paper, we present a novel planning framework for generating asymptotically optimal path...

This paper describes a 3D path planning system that is able to provide a solution trajectory for the automatic control of a robot. The proposed system uses a point cloud obtained from the robot workspace, with a Kinect V2 sensor to identify the interest regions and the obstacles of the environment. Our proposal includes a collision-free path planner based on the Rapidly-exploring Random Trees v...

Recent research results suggest that one can incorporate motion-planning techniques into the control loop of 3D navigation or tele-operation for more efficient navigation. However, the motion planner with this approach may not scale up well for large workspaces. In this paper, we propose a novel approach to overcome this scalability problem. We limit the region of interest for path-finding to a...

Engine malfunctions during helicopter flight poses a large risk to pilot and crew. Without a quick and coordinated reaction, such situations lead to a complete loss of control. An autonomous landing system is capable of reacting quickly to regain control, however current emergency landing methods focus only on the offline generation of dynamically feasible trajectories while ignoring the more s...

We present RRT, the first asymptotically optimal samplingbased motion planning algorithm for real-time navigation in dynamic environments (containing obstacles that unpredictably appear, disappear, and move). Whenever obstacle changes are observed, e.g., by onboard sensors, a graph rewiring cascade quickly updates the search-graph and repairs its shortest-path-to-goal subtree. Both graph and tr...

This paper introduces a novel path planning technique called MCRT which is aimed at non-deterministic, partially known, real-time domains populated with dynamically moving obstacles, such as might be found in a real-time strategy (RTS) game. The technique combines an efficient form of Monte-Carlo tree search with the randomized exploration capabilities of rapidly exploring random tree (RRT) pla...

Rapidly-exploring Random Tree Star(RRT*) is a recently proposed extension of Rapidly-exploring Random Tree (RRT) algorithm that provides a collisionfree, asymptotically optimal path regardless of obstacles geometry in a given environment. However, one of the limitation in the RRT* algorithm is slow convergence to optimal path solution. As a result it consumes high memory as well as time due to ...

This thesis presents a novel approach to address the challenge of planning paths for real-world multi-agent systems operating in complex environments. The technique developed, the Decentralized Multi-Agent Rapidly-exploring Random Tree (DMARRT) algorithm, is an extension of the CL-RRT algorithm to the multi-agent case, retaining its ability to plan quickly even with complex constraints. Moreove...

Small unmanned air vehicles are limited in sensor weight and power such that detection and avoidance of unknown obstacles during flight is difficult. This paper presents a low power low weight method of detection using a laser range finder. In addition, a rapidly-exploring random tree algorithm to generate waypoint paths around obstacles known a priori is presented, and a dynamic geometric algo...