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Quadrotor unmanned aerial vehicles (UAVs) often encounter intricate environmental and dynamic limitations in real-world applications, underscoring the significance of proficient trajectory planning for ensuring both safety and efficiency during flights. To tackle this challenge, we introduce an innovative approach that harmonizes sophisticated environmental insights with the dynamic state of a UAV within a potential field framework. Our proposition entails a quadrotor trajectory planner grounded in a kinodynamic gene regulation network potential field. The pivotal contribution of this study lies in the amalgamation of environmental perceptions and kinodynamic constraints within a newly devised gene regulation network (GRN) potential field. By enhancing the gene regulation network model, the potential field becomes adaptable to the UAV's dynamic conditions and its surroundings, thereby extending the GRN into a kinodynamic GRN (K-GRN). The trajectory planner excels at charting courses that guide the quadrotor UAV through intricate environments while taking dynamic constraints into account. The amalgamation of environmental insights and kinodynamic constraints within the potential field framework bolsters the adaptability and stability of the generated trajectories. Empirical results substantiate the efficacy of our proposed methodology. [ABSTRACT FROM AUTHOR]

Featured Application: This work provides a generalization of the velocity obstacle (VO) collision avoidance strategy to nonlinear second-order underactuated systems in three-dimensional dynamic uncertain environments. This work provides a generalization of the three-dimensional velocity obstacle (VO) collision avoidance strategy for nonlinear second-order underactuated systems in three-dimensional dynamic uncertain environments. A hierarchical architecture is exploited to deal with conflicting multiple subtasks, which are defined as several rotations and are parameterized by quaternions. An improved VO method considering the kinodynamic constraints of a class of fixed-wing unmanned aerial vehicles (UAV) is proposed to implement the motion planning. The position error and velocity error can be mapped onto one desired axis so that, only relying on an engine, UAVs can achieve the goal of point tracking without collision. Additionally, the performance of the closed-loop system is demonstrated through a series of simulations performed in a three-dimensional manner. [ABSTRACT FROM AUTHOR]

This work provides a generalization of the three-dimensional velocity obstacle (VO) collision avoidance strategy for nonlinear second-order underactuated systems in three-dimensional dynamic uncertain environments. A hierarchical architecture is exploited to deal with conflicting multiple subtasks, which are defined as several rotations and are parameterized by quaternions. An improved VO method considering the kinodynamic constraints of a class of fixed-wing unmanned aerial vehicles (UAV) is proposed to implement the motion planning. The position error and velocity error can be mapped onto one desired axis so that, only relying on an engine, UAVs can achieve the goal of point tracking without collision. Additionally, the performance of the closed-loop system is demonstrated through a series of simulations performed in a three-dimensional manner.

In this paper, we introduce a novel and efficient hybrid trajectory planning method for autonomous driving in highly constrained environments. The contributions of this paper are fourfold. First, we present a trajectory planning framework that is able to handle geometry constraints, nonholonomic constraints, and dynamics constraints of cars in a humanlike and layered fashion and generate curvature-continuous, kinodynamically feasible, smooth, and collision-free trajectories in real time. Second, we present a derivative-free global path modification algorithm to extract high-order state information in free space for state sampling. Third, we extend the regular state-space sampling method widely used in on-road autonomous driving systems to a multi-phase deterministic state-space sampling method that is able to approximate complex maneuvers. Fourth, we improve collision checking accuracy and efficiency by using a different car footprint approximation strategy and a two-phase collision checking routine. A range of challenging simulation experiments show that the proposed method returns high-quality trajectories in real time and outperforms existing planners, such as hybrid A* and conjugate-gradient descent path smoother in terms of path quality, efficiency, and computation resources used.

Many applications demand a dynamical system to reach a goal state under kinematic and dynamic (i.e., kinodynamic) constraints. Moreover, industrial robots perform such motions over and over again and therefore demand efficiency, i.e., optimal motion. In many applications, the initial state may not be constrained and can be taken as an additional variable for optimization. The semi-stochastic kinodynamic planning (SKIP) algorithm presented in this paper is a novel method for trajectory optimization of a fully actuated dynamic system to reach a goal state under kinodynamic constraints. The basic principle of the algorithm is the parameterization of the motion trajectory to a vector in a high-dimensional space. The kinematic and dynamic constraints are formulated in terms of time and the trajectory parameters vector. That is, the constraints define a time-varying domain in the high dimensional parameters space. We propose a semi stochastic technique that finds a feasible set of parameters satisfying the constraints within the time interval dedicated to task completion. The algorithm chooses the optimal solution based on a given cost function. Statistical analysis shows the probability to find a solution if one exists. For simulations, we found a time-optimal trajectory for a 6R manipulator to hit a disk in a desired state. [ABSTRACT FROM AUTHOR]

Abstract A randomized kinodynamic path planning algorithm based on the incremental sampling-based method is proposed here as the state-of-the-art in this field applicable in an autonomous underwater vehicle. Designing a feasible path for this vehicle from an initial position and velocity to a target position and velocity in three-dimensional spaces by considering the kinematic constraints such as obstacles avoidance and dynamic constraints such as hard bounds and non-holonomic characteristic of AUV are the main motivation of this research. For this purpose, a closed-loop rapidly-exploring random tree (CL-RRT) algorithm is presented. This CL-RRT consists of three tightly coupled components: a RRT algorithm, three fuzzy proportional-derivative controllers for heading and diving control and a six degree-of-freedom nonlinear AUV model. The branches of CL-RRT are expanded in the configuration space by considering the kinodynamic constraints of AUV. The feasibility of each branch and random offspring vertex in the CL-RRT is checked against the mentioned constraints of AUV. Next, if the planned branch is feasible by the AUV, then the control signals and related vertex are recorded through the path planner to design the final path. This proposed algorithm is implemented on a single board computer (SBC) through the xPC Target and then four test-cases are designed in 3D space. The results of the processor-in-the-loop tests are compared by the conventional RRT and indicate that the proposed CL-RRT not only in a rapid manner plans an initial path, but also the planned path is feasible by the AUV. [ABSTRACT FROM AUTHOR]

This paper provides a new framework for the collective motion control of a team of n -link doubly nonholonomic mobile manipulators in a constrained environment. A continuous decentralized motion planner is proposed. It guarantees the establishment and strict maintenance of a team formation using the Lyapunov-based control scheme (LbCS), which takes into account all the practical limitations, and the constraints due to fixed obstacles, nonholonomy and globally rigid formation requirements. The control scheme inherently utilizes artificial potential fields within an overarching leader–follower framework to mobilize the prescribed globally rigid formation. The designated leader of the team is modeled as a moving reference point, referred to as the virtual leader . With its protective polygonal region, it directs the motion of the team and manipulate the geometry of the formation. It is the authors’ belief that this navigation and globally rigid formation control problem of n -link doubly nonholonomic mobile manipulators is treated for the first time with the use of continuous time-invariant control laws within the framework of LbCS and a variant of the leader–follower scheme. The effectiveness of the motion planner and the resulting acceleration-based control laws are demonstrated via computer simulations. [ABSTRACT FROM AUTHOR]

The paper considers the problem of motion planning and posture control of multiple n-link doubly nonholonomic mobile manipulators in an obstacle-cluttered and bounded workspace. The workspace is constrained with the existence of an arbitrary number of fixed obstacles (disks, rods and curves), artificial obstacles and moving obstacles. The coordination of multiple n-link doubly nonholonomic mobile manipulators subjected to such constraints becomes therefore a challenging navigational and steering problem that few papers have considered in the past. Our approach to developing the controllers, which are novel decentralized nonlinear acceleration controllers, is based on a Lyapunov control scheme that is not only intuitively understandable but also allows simple but rigorous development of the controllers. Via the scheme, we showed that the avoidance of all types of obstacles was possible, that the manipulators could reach a neighborhood of their goal and that their final orientation approximated the desired orientation. Computer simulations illustrate these results. [ABSTRACT FROM AUTHOR]

In this paper, we present a suboptimal and analytical solution to the trajectory generation of mobile robots operating in a dynamic environment with moving obstacles. The proposed solution explicitly addresses both the robot kinodynamic constraints and the geometric constraints due to obstacles while ensuring the suboptimal performance to a combined performance metric. In particular, the proposed design is based on a family of parameterized trajectories, which provides a unified way to embed the kinodynamic constraints, geometric constraints, and performance index into a set of parameterized constraint equations. To that end, the suboptimal solution to the constrained optimization problem can be analytically obtained. The solvability conditions to the constraint equations are explicitly established, and the proposed solution enhances the methodologies of real-time path planning for mobile robots with kinodynamic constraints. Both the simulation and experiment results verify the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

We present an algorithm that enables a humanoid robot to reason about its environment and use the available objects to build bridges, stairs and lever-fulcrum systems. Facing a challenge that is otherwise intractable, such as climbing a height or moving a heavy object, the proposed planner reasons about the physical limitations of the robot to design functional structures. Inducing constraints on the space of possible designs within a classical planning framework, the algorithm outputs feasible structures that can be used towards accomplishing goals. We present results in dynamic simulation with Golem Hubo, walking on a bridge to cross a hazardous area, and in real-world with Golem Krang, overturning 100 kg loads and pushing 240 kg obstacles. [ABSTRACT FROM PUBLISHER]

Abstract: In this paper, we propose a novel path planning algorithm for a mobile robot in dynamic and cluttered environments with kinodynamic constraints. We compute the arrival time field as a bias which gives larger weights for shorter and safer paths toward a goal. We then implement a randomized path search guided by the arrival time field for building the path considering kinematic and dynamic (kinodynamic) constraints of an actual robot. We also consider path quality by adding heuristic constraints on the randomized path search, such as reducing unstable movements of the robot by using a heading criterion. The path will be extracted by backtracking the nodes which reach the goal area to the root of the tree generated by the randomized search, and the motion from the very first node will be sent to the robot controller. We provide a brief comparison between our algorithm and other existing algorithms. Simulation and experimental results prove that our algorithm is fast and reliable to be implemented on the real robot and is able to handle kinodynamic problems effectively. [Copyright &y& Elsevier]

Abstract: A mobile manipulator is a robotic system made up of two components; a mobile platform and a manipulator mounted on the platform equipped with non-deformable wheels. Such a combined system requires complex design and control. This paper considers the autonomous navigation problem of a nonholonomic mobile platform and an -link nonholonomic manipulator fixed to the platform. For this planar -link doubly nonholonomic manipulator, we present the first ever set of nonlinear continuous controllers for obstacle avoidance. The controllers provide a collision-free trajectory within a constrained workspace cluttered with fixed obstacles of different shapes and sizes whilst satisfying the nonholonomic and kinodynamic constraints associated with the robotic system. An advantage of the proposed method is the ease at which the acceleration-based control laws can be derived from the Lyapunov function. The effectiveness of the nonholonomic planner is demonstrated via computer simulations. [Copyright &y& Elsevier]

This paper shows how the Lyapunov method can be used to generate continuous time-invariant control laws that, to some extent, simultaneously solve the multi-task problem of navigation, steering, and point and posture stabilizations of nonholonomic car-like mobile robots. Even though the proposed method does not guarantee asymptotic stability, it provides a starting point for looking at the difficult problem of posture stability - essentially the problem of parking a car-like vehicle - without the use of discontinuous or time-varying control laws, which are currently the only laws capable of circumventing the well-known Brockett Theorem on the stabilization of nonholonomic systems and providing complete solutions to point and posture stabilities. [ABSTRACT FROM AUTHOR]