Motion planning

About: Motion planning is a research topic. Over the lifetime, 32846 publications have been published within this topic receiving 553548 citations.

Planning Paths for Elastic Objects under Manipulation Constraints

Abstract: This paper addresses the problem of planning paths for an elastic object from an initial to a final configuration in a static environment. It is assumed that the object is manipulated by two actuators and that it does not touch the obstacles in its environment at any time. The object may need to deform to achieve a collision-free path from the initial to the final configuration. Any required deformations are automatically computed by the planner according to the principles of elasticity theory from mechanics. The problem considered in this paper differs significantly from that of planning for a rigid or an articulated object. In the first part of the paper, the authors point out these differences and highlight the reasons that make planning for elastic objects an extremely difficult task. The authors then present a randomized algorithm for computing collision-free paths for elastic objects under the above-mentioned restrictions of manipulation. The paper includes a number of experimental results. The work...

Three-dimensional coverage path planning via viewpoint resampling and tour optimization for aerial robots

Abstract: This paper presents a new algorithm for three-dimensional coverage path planning for autonomous structural inspection operations using aerial robots. The proposed approach is capable of computing short inspection paths via an alternating two-step optimization algorithm according to which at every iteration it attempts to find a new and improved set of viewpoints that together provide full coverage with decreased path cost. The algorithm supports the integration of multiple sensors with different fields of view, the limitations of which are respected. Both fixed-wing as well as rotorcraft aerial robot configurations are supported and their motion constraints are respected at all optimization steps, while the algorithm operates on both mesh- and occupancy map-based representations of the environment. To thoroughly evaluate this new path planning strategy, a set of large-scale simulation scenarios was considered, followed by multiple real-life experimental test-cases using both vehicle configurations.

Coverage Path Planning for UAVs Photogrammetry with Energy and Resolution Constraints

Abstract: Unmanned Aerial Vehicles (UAVs) are starting to be used for photogrammetric sensing of large areas in several application domains, such as agriculture, rescuing, and surveillance. In this context, the problem of finding a path that covers the entire area of interest is known as Coverage Path Planning (CPP). Although this problem has been addressed by several authors from a geometrical point of view, other issues such as energy, speed, acceleration, and image resolution are not often taken into account. To fill this gap, this paper first proposes an energy model derived from real measurements, and then uses this model to implement a coverage path planning algorithm for reducing energy consumption, as well as guaranteeing a desired image resolution. In addition, two safety mechanisms are presented: the first, executed off-line, checks whether the energy stored in the battery is sufficient to perform the planned path; the second, performed online, triggers a safe return-to-launch (RTL) operation when the actual available energy is equal to the energy required by the UAV to go back to the starting point.

Motion Planning in Complex Environments using Closed-loop Prediction

Abstract: This paper describes the motion planning and control subsystems of Team MIT’s entry in the 2007 DARPA Grand Challenge. The novelty is in the use of closed-loop prediction in the framework of Rapidly-exploring Random Tree (RRT). Unlike the standard RRT, an input to the controller is sampled, followed by the forward simulation using the vehicle model and the controller to compute the predicted trajectory. This enables the planner to generate smooth trajectories much more efficiently, while the randomization allows the planner to explore cluttered environment. The controller consists of a Proportional-Integral speed controller and a nonlinear pure-pursuit steering controller, which are used both in execution and in the simulation-based prediction. The main advantages of the forward simulation are that it can easily incorporate any nonlinear control law and nonlinear vehicle dynamics, and the resulting trajectory is dynamically feasible. By using a stabilizing controller, it can handle vehicles with unstable dynamics. Several results obtained using MIT’s race vehicle demonstrate these features of the approach.