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Classical optimization methodologies fall short in very large and complex domains. In this book is suggested a different approach to optimization, an approach which is based on the 'blind' and heuristic mechanisms of evolution and population genetics. The genetic approach to optimization introduces a new philosophy to optimization in general, but particularly to engineering. By introducing the ?genetic? approach to robot trajectory generation, much can be learned about the adaptive mechanisms of evolution and how these mechanisms can solve real world problems. It is suggested further that optimization at large may benefit greatly from the adaptive optimization exhibited by natural systems when attempting to solve complex optimization problems, and that the determinism of classical optimization models may sometimes be an obstacle in nonlinear systems.This book is unique in that it reports in detail on an application of genetic algorithms to a real world problem, and explains the considerations taken during the development work. Futhermore, it addresses robotics in two new aspects: the optimization of the trajectory specification which has so far been done by human operators and has not received much attention for both automation and optimization, and the introduction of a heuristic strategy to a field predominated by deterministic strategies.
This book deals with the problems related to planning motion laws and t- jectories for the actuation system of automatic machines, in particular for those based on electric drives, and robots. The problem of planning suitable trajectories is relevant not only for the proper use of these machines, in order to avoid undesired e?ects such as vibrations or even damages on the mech- ical structure, but also in some phases of their design and in the choice and sizing of the actuators. This is particularly true now that the concept of “el- tronic cams” has replaced, in the design of automatic machines, the classical approach based on “mechanical cams”. The choice of a particular trajectory has direct and relevant implications on several aspects of the design and use of an automatic machine, like the dimensioning of the actuators and of the reduction gears, the vibrations and e?orts generated on the machine and on the load, the tracking errors during the motion execution. For these reasons, in order to understand and appreciate the peculiarities of the di?erent techniques available for trajectory planning, besides the ma- ematical aspects of their implementation also a detailed analysis in the time and frequency domains, a comparison of their main properties under di?erent points of view, and general considerations related to their practical use are reported.
Selected contributions to the Workshop WAFR 2002, held December 15-17, 2002, Nice, France. This fifth biannual Workshop on Algorithmic Foundations of Robotics focuses on algorithmic issues related to robotics and automation. The design and analysis of robot algorithms raises fundamental questions in computer science, computational geometry, mechanical modeling, operations research, control theory, and associated fields. The highly selective program highlights significant new results such as algorithmic models and complexity bounds. The validation of algorithms, design concepts, or techniques is the common thread running through this focused collection.
Interest in robot manipulators interacting with dynamic environments has been continuously growing because of the increasing demand for industrial robot collaboration. Human-robot collaboration and robot-robot collaboration are the two scenarios of robot collaboration that have generally been considered. The difficulties of such applications may be described from two perspectives: a good perception of environment and a proper algorithm to react to the dynamic environment for the robot manipulators. Online trajectory generation is one of the approaches for robot reaction. In the generation of the trajectory, the transformation between joint space and task space is necessary since the sensor measurement of the environment is in task space and the trajectory of the robot manipulator is in joint space. The transformation needs to be done online in a dynamic environment and hence easily results in an exponential increase of the computational load. This dissertation proposes a safety index and the associated robot safety system in order to assess and ensure the safety of the agent in the collaboration scenarios. The agent could be a human worker in human-robot collaboration or another robot in robot-robot collaboration. In the robot safety system, the online trajectory generation algorithm is formulated in the optimization-based trajectory planning framework. The safety index is evaluated using the ellipsoid coordinates attached to the robot links that represents the distance between the robot manipulator and the agent. To account for the inertial effect, the momentum of the robot links are projected onto the coordinates to generate additional measures of safety. The safety index is used as a constraint in the formulation of the optimization problem so that a collision-free trajectory within a finite time horizon is generated online iteratively for the robot to move toward the desired position. To reduce the computational load for real-time implementation, the formulated optimization problem is further approximated by a quadratic problem. Moreover, a heuristic strategy is proposed to select the active constraints for the next iteration so as to further reduce the computational load. The safety index and the proposed online trajectory generation algorithm are simulated and validated in both a two-link planar robot and a seven-DOF robot in human-robot collaboration and robot-robot collaboration. Simulation results show that the proposed algorithm and robot safety system are capable of generating collision-free and smooth trajectories online. The proposed algorithm has been extended to consider measurement noise in the agent information. Two possible approaches have been proposed for handling zero-mean Gaussian noise in the agent information.
Obstacle Avoidance in Multi-robot Systems: Experiments in Parallel Genetic Algorithms offers a novel framework for solving the path planning problem for robot manipulators. Simple and efficient solutions are proposed for the path planning problem based on genetic algorithms. One of the attractive features of genetic algorithms is their ability to solve formidable problems in a robust and straightforward manner. Moreover, genetic algorithms are inherently parallel in nature, which makes them ideal candidates for parallel computing implementations.By combining the robustness of genetic algorithms with the power of parallel computers, this book provides an effective and practical approach to solving path planning problems. The book gives details of implementations that allow a better understanding of the complexities involved in the development of parallel path planning algorithms. The material presented is interdisciplinary in nature ? it combines topics from robotics, genetic algorithms, and parallel processing. The book can be used by practitioners and researchers in computer science and engineering.
In this thesis, a method is presented to construct minimum-time robot trajectories for predefined Cartesian end-effector path in a workspace containing obstacles. The method is preferably applied to a geometric collision-free path of a SCARA robot by using the theories of Bezier, B-spline, and parabolic blending curves. The motion of the manipulator is defined by Cartesian control points and represented by a sequence of generated Cartesian knots along the end-effector path which is transformed into sets of joint displacements with one set for each joint. Cubic spline functions are then used to fit the sequence of joint displacements for each joint. The minimum-time trajectory planning problem is formulated as the problem of minimizing the total traveling time subject to constraints on joint positions, velocities, accelerations, jerks, torques and end-effector acceleration. The modified pattern search goal programming method is proposed to solve this nonlinear optimization problem. The computer program, ROBOPATH, has been written to implement the algorithm for a manipulator with two links and two degrees of freedom. The examples show the algorithm to be a useful tool in the design of manipulators, robot tasks and workcells. The results show that different locations of a path and different path shapes resulted in different total traveling times. Also, as a result, approximation curve techniques gave shorter total traveling time than the interpolation curve technique.