Motion planning

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

Comparison of Parallel Genetic Algorithm and Particle Swarm Optimization for Real-Time UAV Path Planning

Abstract: The development of autonomous unmanned aerial vehicles (UAVs) is of high interest to many governmental and military organizations around the world. An essential aspect of UAV autonomy is the ability for automatic path planning. In this paper, we use the genetic algorithm (GA) and the particle swarm optimization algorithm (PSO) to cope with the complexity of the problem and compute feasible and quasi-optimal trajectories for fixed wing UAVs in a complex 3D environment, while considering the dynamic properties of the vehicle. The characteristics of the optimal path are represented in the form of a multiobjective cost function that we developed. The paths produced are composed of line segments, circular arcs and vertical helices. We reduce the execution time of our solutions by using the “single-program, multiple-data” parallel programming paradigm and we achieve real-time performance on standard commercial off-the-shelf multicore CPUs. After achieving a quasi-linear speedup of 7.3 on 8 cores and an execution time of 10 s for both algorithms, we conclude that by using a parallel implementation on standard multicore CPUs, real-time path planning for UAVs is possible. Moreover, our rigorous comparison of the two algorithms shows, with statistical significance, that the GA produces superior trajectories to the PSO.

Automatic Planning of Manipulator Transfer Movements

Abstract: The class of problems that involve finding where to place or how to move a solid object in the presence of obstacles is discussed. The solution to this class of problems is essential to the automatic planning of manipulator transfer movements, i.e., the motions to grasp a part and place it at some destination. For example, planning transfer movements requires the ability to plan paths for the manipulator that avoid collisions with objects in the workspace and the ability to choose safe grasp points on objects. The approach to these problems described here is based on a method of computing an explicit representation of the manipulator configurations that would bring about a collision.

Real-Time Motion Planning for Agile Autonomous Vehicles

Abstract: Planning the path of an autonomous, agile vehicle in a dynamic environment is a very complex problem, especially when the vehicle is required to use its full maneuvering capabilities. Recent efforts aimed at using randomized algorithms for planning the path of kinematic and dynamic vehicles have demonstrated considerable potential for implementation on future autonomous platforms. This paper builds upon these efforts by proposing a randomized path planning architecture for dynamical systems in the presence of fixed and moving obstacles. This architecture addresses the dynamic constraints on the vehicle's motion, and it provides at the same time a consistent decoupling between low-level control and motion planning. The path planning algorithm retains the convergence properties of its kinematic counterparts. System safety is also addressed in the face of finite computation times by analyzing the behavior of the algorithm when the available onboard computation resources are limited, and the planning must be performed in real time. The proposed algorithm can be applied to vehicles whose dynamics are described either by ordinary differential equations or by higher-level, hybrid representations. Simulation examples involving a ground robot and a small autonomous helicopter are presented and discussed.

Automatic grasp planning using shape primitives

Abstract: Automatic grasp planning for robotic hands is a difficult problem because of the huge number of possible hand configurations. However, humans simplify the problem by choosing an appropriate prehensile posture appropriate for the object and task to be performed. By modeling an object as a set of shape primitives, such as spheres, cylinders, cones and boxes, we can use a set of rules to generate a set of grasp starting positions and pregrasp shapes that can then be tested on the object model. Each grasp is tested and evaluated within our grasping simulator "GraspIt!", and the best grasps are presented to the user. The simulator can also plan grasps in a complex environment involving obstacles and the reachability constraints of a robot arm.

Temporal-Logic-Based Reactive Mission and Motion Planning

Abstract: This paper provides a framework to automatically generate a hybrid controller that guarantees that the robot can achieve its task when a robot model, a class of admissible environments, and a high-level task or behavior for the robot are provided. The desired task specifications, which are expressed in a fragment of linear temporal logic (LTL), can capture complex robot behaviors such as search and rescue, coverage, and collision avoidance. In addition, our framework explicitly captures sensor specifications that depend on the environment with which the robot is interacting, which results in a novel paradigm for sensor-based temporal-logic-motion planning. As one robot is part of the environment of another robot, our sensor-based framework very naturally captures multirobot specifications in a decentralized manner. Our computational approach is based on first creating discrete controllers satisfying specific LTL formulas. If feasible, the discrete controller is then used to guide the sensor-based composition of continuous controllers, which results in a hybrid controller satisfying the high-level specification but only if the environment is admissible.