Path Planning Using a Potential Field Representation

Coordinated Science Laboratory was formerly known as Control Systems LaboratoryNational Science Foundation / ECS 83-52408Rockwell Internationa

Multi-objective path planning using spline representation

Off-line point to point navigation to calculate feasible paths and optimize them for different objectives is computationally difficult. Path planning problem is truly a multi-objective problem, as reaching the goal point in short time is desirable for an autonomous vehicle while ability to generate safe paths in crucial for vehicle viability. Path representation methodologies using piecewise polynomial and B-splines have been used to ensure smooth paths. Multi-objective path planning studies using NSGA-II algorithm to optimize path length and safety measures computed using one of the three metrics (i) an artificial potential field, (ii) extent of obstacle hindrance and (iii) a measure of visibility are implemented. Multiple tradeoff solutions are obtained on complex scenarios. The results indicate the usefulness of treating path planning as a multiobjective problem

Optimal field coverage path planning on 2D and 3D surfaces

With the rapid adoption of automatic guidance systems, automated path planning has great potential to further optimize field operations. Field operations should be done in a manner that minimizes time, travel over field surfaces and is coordinated with specific field operations, machine characteristics and topographical features of arable lands. To reach this goal, intelligent coverage path planning algorithm is key. This dissertation documents our innovative research in optimal field coverage path planning on both 2D and 3D surfaces. To determine the full coverage pattern of a given 2D planar field by using boustrophedon paths, it is necessary to know whether to and how to decompose a field into sub-regions and how to determine the travel direction within each sub-region. A geometric model was developed to represent this coverage path planning problem, and a path planning algorithm was developed based on this geometric model. The search mechanism of the algorithm was guided by a customized cost function resulting from the analysis of different headland turning types and implemented with a divide-and-conquer strategy. The complexity of the algorithm was analyzed, and methods for reducing the computational time were discussed. Field examples with complexity ranging from a simple convex shape to an irregular polygonal shape that has multiple obstacles within its interior were tested with this algorithm. The results were compared with other reported approaches or farmers\u27 actual driving patterns. These results indicated the proposed algorithm was effective in producing optimal field decomposition and coverage path direction in each sub-region. In real world, a great proportion of farms have rolling terrains, which have considerable influences to the design of coverage paths. Coverage path planning in 3D space has a great potential to further optimize field operations. To design optimal coverage paths on 3D terrain surfaces, there were five important steps: terrain modeling and representation, topography impacts analysis, terrain decomposition and classification, coverage cost analysis and the development of optimal path searching algorithm. Each of the topics was investigated in this dissertation research. The developed algorithms and methods were successfully implemented in software and tested with practical 3D terrain farm fields with various topographical features. Each field was decomposed into sub-regions based on terrain features. An optimal seed curve was found for each sub-region and parallel coverage paths were generated by offsetting the seed curve sideways until the whole sub-region was completely covered. Compared with the 2D planning results, the experimental results of 3D coverage path planning showed its superiority in reducing both headland turning cost and soil erosion cost

AMP-CAD: Automatic Assembly Motion Planning Using C AD Models of Parts

Assembly with robots involves two kinds of motions, those that are point-to-point and those that are force/torque guided, the former kind of motions being faster and more amenable to automatic planning and the latter kind being necessary for dealing with tight clearances. In this paper, we describe an assembly motion planning system that uses descriptions of assemblies and CAD models of parts to automatically figure out which motions should be point-to-point and which motions should be force/torque guided. Our planner uses graph search over a potential field representation of parts to calculate candidate assembly paths. Given the tolerances of the parts and other uncertainties, these paths are then analyzed for the likelihood of collisions. Those path segments that are prone to collisions are then marked for execution under force/torque control. The calculation of the various motions is facilitated by an object-oriented and feature-based assembly representation. A highlight of this representation is the manner in which tolerance information is taken into account: Representation of, say, a part contains a pointer to the boundary representation of the part in its most material condition form. As first defined by Requicha, the most material condition form of a geometric entity is obtained by expanding all the convexities and shrinking all the concavities by relevant tolerances. An integral part of the assembly motion planner is the execution unit. Residing in this unit is knowledge of the different types of automatic EDR (error detection and recovery) strategies. Therefore, during the execution of the force/torque guided motion, this unit invokes the EDR strategies appropriate to the geometric constraints relevant to the motion. This system, called AMP-CAD, has been experimentally verified using a Cincinnati Milacron T3-726 robot and a Puma 762 robot on a variety of assemblies

Artificial intelligence makes computers lazy

This paper looks at the age-old problem of trying to instil some degree of intelligence in computers. Genetic Algorithms (GA) and Genetic Programming (GP) are techniques that are used to evolve a solution to a problem using processes that mimic natural evolution. This paper reflects on the experience gained while conducting research applying GA and GP to two quite different problems: Medical Diagnosis and Robot Path Planning. An observation is made that when these algorithms are not applied correctly the computer seemingly exhibits lazy behaviour, arriving at a suboptimal solutions. Using examples, this paper shows how this 'lazy' behaviour can be overcome

Generic Drone Control Platform for Autonomous Capture of Cinema Scenes

The movie industry has been using Unmanned Aerial Vehicles as a new tool to produce more and more complex and aesthetic camera shots. However, the shooting process currently rely on manual control of the drones which makes it difficult and sometimes inconvenient to work with. In this paper we address the lack of autonomous system to operate generic rotary-wing drones for shooting purposes. We propose a global control architecture based on a high-level generic API used by many UAV. Our solution integrates a compound and coupled model of a generic rotary-wing drone and a Full State Feedback strategy. To address the specific task of capturing cinema scenes, we combine the control architecture with an automatic camera path planning approach that encompasses cinematographic techniques. The possibilities offered by our system are demonstrated through a series of experiments

Learning the hidden human knowledge of UAV pilots when navigating in a cluttered environment for improving path planning

© 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other worksWe propose in this work a new model of how the hidden human knowledge (HHK) of UAV pilots can be incorporated in the UAVs path planning generation. We intuitively know that human’s pilots barely manage or even attempt to drive the UAV through a path that is optimal attending to some criteria as an optimal planner would suggest. Although human pilots might get close but not reach the optimal path proposed by some planner that optimizes over time or distance, the final effect of this differentiation could be not only surprisingly better, but also desirable. In the best scenario for optimality, the path that human pilots generate would deviate from the optimal path as much as the hidden knowledge that its perceives is injected into the path. The aim of our work is to use real human pilot paths to learn the hidden knowledge using repulsion fields and to incorporate this knowledge afterwards in the environment obstacles as cause of the deviation from optimality. We present a strategy of learning this knowledge based on attractor and repulsors, the learning method and a modified RRT* that can use this knowledge for path planning. Finally we do real-life tests and we compare the resulting paths with and without this knowledge.Accepted versio