Feedrate planning for machining with industrial six-axis robots

The authors want to thank Stäubli for providing the necessary information of the controller, Dynalog for its contribution to the experimental validations and X. Helle for its material contributions.Nowadays, the adaptation of industrial robots to carry out high-speed machining operations is strongly required by the manufacturing industry. This new technology machining process demands the improvement of the overall performances of robots to achieve an accuracy level close to that realized by machine-tools. This paper presents a method of trajectory planning adapted for continuous machining by robot. The methodology used is based on a parametric interpolation of the geometry in the operational space. FIR filters properties are exploited to generate the tool feedrate with limited jerk. This planning method is validated experimentally on an industrial robot

An efficient method for multiobjective optimal control and optimal control subject to integral constraints

We introduce a new and efficient numerical method for multicriterion optimal control and single criterion optimal control under integral constraints. The approach is based on extending the state space to include information on a "budget" remaining to satisfy each constraint; the augmented Hamilton-Jacobi-Bellman PDE is then solved numerically. The efficiency of our approach hinges on the causality in that PDE, i.e., the monotonicity of characteristic curves in one of the newly added dimensions. A semi-Lagrangian "marching" method is used to approximate the discontinuous viscosity solution efficiently. We compare this to a recently introduced "weighted sum" based algorithm for the same problem. We illustrate our method using examples from flight path planning and robotic navigation in the presence of friendly and adversarial observers.Comment: The final version accepted by J. Comp. Math. : 41 pages, 14 figures. Since the previous version: typos fixed, formatting improved, one mistake in bibliography correcte

A path planning and path-following control framework for a general 2-trailer with a car-like tractor

Maneuvering a general 2-trailer with a car-like tractor in backward motion is a task that requires significant skill to master and is unarguably one of the most complicated tasks a truck driver has to perform. This paper presents a path planning and path-following control solution that can be used to automatically plan and execute difficult parking and obstacle avoidance maneuvers by combining backward and forward motion. A lattice-based path planning framework is developed in order to generate kinematically feasible and collision-free paths and a path-following controller is designed to stabilize the lateral and angular path-following error states during path execution. To estimate the vehicle state needed for control, a nonlinear observer is developed which only utilizes information from sensors that are mounted on the car-like tractor, making the system independent of additional trailer sensors. The proposed path planning and path-following control framework is implemented on a full-scale test vehicle and results from simulations and real-world experiments are presented.Comment: Preprin

Autonomous Trajectory Design Considering the Limitation of Torque and Drag Forces

In the oil and gas industry, designing well trajectories is an important part of drilling operations that affect well construction, completion, and production. But the current trajectory planning process works in isolation and does not take many engineering constraints into account, which leads to inefficiency, manual iterations, and less-than-ideal results. This study aims to solve the problem by making an automated system for designing 3D trajectories that uses engineering calculations and focuses on torque & drag analysis. The objectives of this research include the development of algorithms to automate and optimize trajectory design, the integration of torque and drag calculations to avoid drill string damage through buckling or over torque, and the evaluation of the system’s performance through case studies. The research also explores the kick-off point optimization and trajectory optimization between target points to enhance well placement and planning efficiency. The significance of this research lies in its potential to revolutionize well planning processes and minimize the cost associated with planning complex wells. It will help the industry by making trajectory planning easier, saving time and money, and minimizing the risks of drilling operations

Intelligent manipulation technique for multi-branch robotic systems

New analytical development in kinematics planning is reported. The INtelligent KInematics Planner (INKIP) consists of the kinematics spline theory and the adaptive logic annealing process. Also, a novel framework of robot learning mechanism is introduced. The FUzzy LOgic Self Organized Neural Networks (FULOSONN) integrates fuzzy logic in commands, control, searching, and reasoning, the embedded expert system for nominal robotics knowledge implementation, and the self organized neural networks for the dynamic knowledge evolutionary process. Progress on the mechanical construction of SRA Advanced Robotic System (SRAARS) and the real time robot vision system is also reported. A decision was made to incorporate the Local Area Network (LAN) technology in the overall communication system

Air Vehicle Path Planning

This dissertation explores optimal path planning for air vehicles. An air vehicle exposed to illumination by a tracking radar is considered and the problem of determining an optimal planar trajectory connecting two prespecified points is addressed. An analytic solution yielding the trajectory minimizing the received radar energy reflected from the target is derived using the Calculus of Variations. Additionally, the related problem of an air vehicle tracked by a passive sensor is also solved. Using the insights gained from the single air vehicle radar exposure minimization problem, a hierarchical cooperative control law is formulated to determine the optimal trajectories that minimize the cumulative exposure of multiple air vehicles during a rendezvous maneuver. The problem of one air vehicle minimizing exposure to multiple radars is also addressed using a variational approach, as well as a sub-optimal minimax argument. Local and global optimality issues are explored. A novel decision criterion is developed determining the geometric conditions dictating when it is preferable to go between or around two radars. Lastly, an optimal minimum time control law is obtained for the search and target identification mission of an autonomous air vehicle. This work demonstrates that an awareness of the consequences of embracing sub-optimal and non-globally optimal solutions for optimization problems, such as air vehicle path planning, is essential