Cartesian Constrained Stochastic Trajectory Optimization for Motion Planning

This paper presents novel extensions of the Stochastic Optimization Motion Planning (STOMP), which considers cartesian path constraints. It potentially has high usage in many autonomous applications with robotic arms, where preservation or minimization of tool-point rotation is required. The original STOMP algorithm is unable to use the cartesian path constraints in a trajectory generation because it works only in robot joint space. Therefore, the designed solution, described in this paper, extends the most important parts of the algorithm to take into account cartesian constraints. The new sampling noise generator generates trajectory samples in cartesian space, while the new cost function evaluates them and minimizes traversed distance and rotation change of the tool-point in the resulting trajectory. These improvements are verified with simple experiments and the solution is compared with the original STOMP. Results of the experiments show that the implementation satisfies the cartesian constraints requirements.

UAV Path Planning Based on Multi-Stage Constraint Optimization

Evolutionary Algorithms (EAs) based Unmanned Aerial Vehicle (UAV) path planners have been extensively studied for their effectiveness and high concurrency. However, when there are many obstacles, the path can easily violate constraints during the evolutionary process. Even if a single waypoint causes a few constraint violations, the algorithm will discard these solutions. In this paper, path planning is constructed as a multi-objective optimization problem with constraints in a three-dimensional terrain scenario. To solve this problem in an effective way, this paper proposes an evolutionary algorithm based on multi-level constraint processing (ANSGA-III-PPS) to plan the shortest collision-free flight path of a gliding UAV. The proposed algorithm uses an adaptive constraint processing mechanism to improve different path constraints in a three-dimensional environment and uses an improved adaptive non-dominated sorting genetic algorithm (third edition—ANSGA-III) to enhance the algorithm’s path planning ability in a complex environment. The experimental results show that compared with the other four algorithms, ANSGA-III-PPS achieves the best solution performance. This not only validates the effect of the proposed algorithm, but also enriches and improves the research results of UAV path planning.

Aperio: managing 3D scene occlusion using a mechanical tool analogy for visualizing multi-partmesh data.

This thesis presents a novel interaction model for browsing complex 3D scenes containing numerous layers of occluding and intertwining structures that often hide regions of interest. The interaction model is realized through the development of a custom visualization application, Aperio. Aperio provides a set of virtual mechanical "metal" tools, such as rods, rings, "cookie" cutters and a knife, that support real-time, interactive exploration. Cutter tools are designed to create easily-understood cutaway views (or context-preserving ribbon slices) and rings and rods provide simple path constraints that support rigid transformations of models via "sliding", providing interactive exploded-view capabilities. All tools are based on a single underlying superquadric formulation and can ―"iteratively" be picked up and replanted to generate various views. A multi-pass, GPU-based capping algorithm provides real-time "solid cuts" rendering of surface meshes. We also present a user study to provide supporting evidence of Aperio‘s interaction simplicity and effectiveness for occlusion management.

Aperio: managing 3D scene occlusion using a mechanical tool analogy for visualizing multi-partmesh data.

This thesis presents a novel interaction model for browsing complex 3D scenes containing numerous layers of occluding and intertwining structures that often hide regions of interest. The interaction model is realized through the development of a custom visualization application, Aperio. Aperio provides a set of virtual mechanical "metal" tools, such as rods, rings, "cookie" cutters and a knife, that support real-time, interactive exploration. Cutter tools are designed to create easily-understood cutaway views (or context-preserving ribbon slices) and rings and rods provide simple path constraints that support rigid transformations of models via "sliding", providing interactive exploded-view capabilities. All tools are based on a single underlying superquadric formulation and can ―"iteratively" be picked up and replanted to generate various views. A multi-pass, GPU-based capping algorithm provides real-time "solid cuts" rendering of surface meshes. We also present a user study to provide supporting evidence of Aperio‘s interaction simplicity and effectiveness for occlusion management.

Modeling and Trajectory Planning Optimization for the Symmetrical Multiwheeled Omnidirectional Mobile Robot

Trajectory optimization is the series of actions that are taken into consideration in order to produce the best path such that it improves the overall performances of physical properties or reduces the consumption of the resources where the restriction system remains maintained. In this paper, first, a compact mathematical model for a symmetrical annular-shaped omnidirectional wheeled mobile robot (SAOWMR) is derived and verified. This general mathematical model provides an opportunity to conduct research, experiments, and comparisons on these omnidirectional mobile robots that have two, three, four, six, or even more omnidirectional wheels without the need to switch models or derive a new model. Then, a new computationally efficient method is proposed to achieve improvements in the trajectory planning optimization (TPO) for a SAOWMR. Moreover, the proposed method has been tested in collision-free navigation by incorporation of the path constraints. Numerical tests and simulations are presented aiming to ensure the efficiency and effectiveness of the proposed method.

Optimum Path Tracking Control for the Inverse Problem of Vehicle Handling Dynamics Based on the hp-Adaptive Gaussian Pseudospectral Method

We propose a vehicle path-tracking method based on the hp-adaptive Gaussian pseudospectral method (GPM), which tackles the problem of the slow convergence speed of the optimal control of vehicle path tracking. First, we establish a kinematic vehicle model by considering the path constraints and boundary constraints during the process of tracking the described path of a vehicle. Subsequently, finding the minimum error of the lateral distance between the prescribed path and the expected trajectory is set as the performance objective function. Finally, the vehicle path tracking problem is transformed into an optimal control problem. The optimization algorithm is combined with the sequential quadratic programming algorithm to optimize problems related to the control and state variables and the boundary and path constraints. The simulated results show that the hp-adaptive GPM can improve the convergence rate of the optimal control problem for vehicle path tracking. The proposed hp-adaptive pseudospectral method has a higher solving efficiency compared with traditional approaches for the vehicle path-tracking problem. Concurrently, the verification results of a real vehicle test indicate the feasibility of the proposed algorithm for solving the vehicle path tracking problem. This research provides valuable insight into the design work of lane changes and is an important step towards the design of feedback control laws for path tracking.