Obstacle Avoidance Constraints Research Articles

Local pursuit strategy-inspired cooperative trajectory planning algorithm for a class of nonlinear constrained dynamical systems

Cooperative trajectory planning is crucial for networked vehicles to respond rapidly in cluttered environments and has a significant impact on many applications such as air traffic or border security monitoring and assessment. One of the challenges in cooperative planning is to find a computationally efficient algorithm that can accommodate both the complexity of the environment and real hardware and configuration constraints of vehicles in the formation. Inspired by a local pursuit strategy observed in foraging ants, feasible and optimal trajectory planning algorithms are proposed in this paper for a class of nonlinear constrained cooperative vehicles in environments with densely populated obstacles. In an iterative hierarchical approach, the local behaviours, such as the formation stability, obstacle avoidance, and individual vehicle's constraints, are considered in each vehicle's (i.e. follower's) decentralised optimisation. The cooperative-level behaviours, such as the inter-vehicle collision avoidance, are considered in the virtual leader's centralised optimisation. Early termination conditions are derived to reduce the computational cost by not wasting time in the local-level optimisation if the virtual leader trajectory does not satisfy those conditions. The expected advantages of the proposed algorithms are (1) the formation can be globally asymptotically maintained in a decentralised manner; (2) each vehicle decides its local trajectory using only the virtual leader and its own information; (3) the formation convergence speed is controlled by one single parameter, which makes it attractive for many practical applications; (4) nonlinear dynamics and many realistic constraints, such as the speed limitation and obstacle avoidance, can be easily considered; (5) inter-vehicle collision avoidance can be guaranteed in both the formation transient stage and the formation steady stage; and (6) the computational cost in finding both the feasible and optimal solutions is low. In particular, the feasible solution can be computed in a very quick fashion. The minimum energy trajectory planning for a group of robots in an obstacle-laden environment is simulated to showcase the advantages of the proposed algorithms.

A PSO multi-robot exploration approach over unreliable MANETs

This paper proposes two extensions of Particle Swarm Optimization (PSO) and Darwinian Particle Swarm Optimization (DPSO), respectively denoted as RPSO (Robotic PSO) and RDPSO (Robotic DPSO), so as to adapt these promising biologically inspired techniques to the multi-robot systems domain, by considering obstacle avoidance and communication constraints. The concepts of social exclusion and social inclusion are used in the RDPSO algorithm as a ‘punish–reward’ mechanism, thus enhancing the ability to escape from local optima. Experimental results obtained in a simulated environment shows the superiority of the RDPSO evidencing that sociobiological inspiration can be useful to meet the challenges of robotic applications that can be described as optimization problems (e.g. search and rescue). Moreover, the performance of the RDPSO is further evaluated within a population of up to 12 physical robots under communication constraints. Experimental results with real platforms show that only 4 robots are needed to accomplish the herein proposed mission and, independently on the number of robots and maximum communication distance, the global optimum is achieved in approximately 90% of the experiments.

Evolutionary indirect approach to solving trajectory planning problem for industrial robots operating in workspaces with obstacles

Abstract In this paper, an indirect method for trajectory planning for industrial robots has been addressed using an evolutionary algorithm. The algorithm is divided into three stages: (1) The acquisition of Adjacent Configurations (AC) for Path Planning subjected to kinematics, geometric and obstacle avoidance constraints. (2) The acquisition of a collision-free path between initial and goal robot configurations. This path consists of a set of ACs, and (3) The acquisition of a temporal history of the evolution for the robot joint coordinates, by minimizing the required time subjected to actuator limits. This algorithm has been evaluated by comparing the results with the direct procedures proposed by Rubio articles in 2009 and 2010.

Decentralized Hybrid Model Predictive Control of a Formation of Unmanned Aerial Vehicles

AbstractThis paper proposes a hierarchical MPC strategy for autonomous navigation of a formation of unmanned aerial vehicles (UAVs) of quadcopter type under obstacle and collision avoidance constraints. Each vehicle is stabilized by a lower-level local linear MPC controller around a desired position, that is generated, at a slower sampling rate, by a hybrid MPC controller per vehicle. Such an upper control layer is based on a hybrid dynamical model of the UAV in closed-loop with its linear MPC controller and of its surrounding environment (i.e., the other UAVs and obstacles). The resulting decentralized scheme controls the formation based on a leader-follower approach. The performance of the hierarchical control scheme is assessed through simulations and comparisons with other path planning strategies, showing the ability of linear MPC to handle the strong couplings among the dynamical variables of each quadcopter under motor voltage and angle/position constraints, and the flexibility of the decentralized hybrid MPC scheme in planning the desired paths on-line.

Smooth Flight Trajectory Planning in the Presence of No-Fly Zones and Obstacles

This paper describes a novel procedure to generate continuously differentiable optimal flight trajectories in the presence of arbitrarily shaped no-fly zones and obstacles having a fixed position in time. The operational flight scenario is first discretized with a finite dimensional grid of positions-directions pairs. A weighted and oriented graph is then defined for which the nodes are the earlier mentioned grid points and for which the arcs correspond to minimum length trajectories compliant with obstacle avoidance constraints. Arcs are obtained via solving convex quadratic programming optimization problems that can also account for geometrical constraints such as trajectory curvature limitations. The problem of finding an optimal trajectory be tween two nodes of the so-called core paths graph is then solved via a minimum cost path search algorithm. In a real-time application perspective, the generation of the core paths graph is computationally cumbersome. Moreover, the aircraft position and direction rarely coincide with one of the graph nodes. However, if the graph is built offline and stored, the definition of an optimal trajectory connecting any points of the space domain requires a reduced computational effort. The particular case of piecewise polynomial trajectories minimizing a flight path's length, compliant with constraints on curvature and flight-path angles, is fully developed. Two- and three-dimensional examples are discussed to show the applicability as well as the effectiveness of the technique.

Path Planning for Wheeled Mobile Robots Using Core Paths Graphs

This paper describes a novel procedure based on Core Path Graphs to generate continuously differentiable sub-optimal paths for wheeled mobile robots in the presence of obstacles. The operational scenario is first discretized with a finite dimensional grid of positions-directions pairs. A weighted and oriented graph is then defined whose nodes are the above mentioned grid points, and whose arcs correspond to minimum length trajectories compliant with obstacle avoidance constraints. Arcs are obtained via solving convex quadratic programming optimization problems. A minimum cost path search algorithm is then solved to find an optimal trajectory between two nodes of the so-called Core Paths Graph. The presence of a-priori unknown obstacles is managed by isolating on-line the non suitable arcs of the Core Paths Graph. A numerical example on a wheeled mobile robot is discussed to show the applicability of the proposed technique

Non-uniform rational B-spline-based minimum cost trajectory planning for autonomous robots

This paper proposes a new novel trajectory planning method by using two evolutionary algorithms namely Elitist non-dominated sorting genetic algorithm (NSGA-II) and differential evolution (DE) for an autonomous robot manipulator (STANFORD robot) whose workspace includes several obstacles. The aim of the problem is to minimise a multicriterion cost function with actuator constraints, joint limits and obstacle avoidance constraints by considering dynamic equations of motion. Trajectories are defined by non uniform rational B-spline (NURBS) functions. This is a nonlinear constrained optimisation problem with 6 objective functions, 32 constraints and 288 variables. The multicriterion cost function is a weighted balance of transfer time, mechanical energy of the actuators, singularity avoidance, penalty function to guarantee the collision free motion, joint jerks and joint accelerations. All types of obstacles (fixed, moving and oscillating obstacles) are present in the workspace of the robot. The results obtained from NSGA-II and DE are compared and analysed.

A reinforcement learning based neural network architecture for obstacle avoidance in multi-fingered grasp synthesis

The ability to learn from interaction with the exterior world as well as variability are two main features of living organisms. The aim of this study is to present and discuss the property of a stochastic reinforcement learning based model of upper limb posture generation that exhibits both properties. The capacity of the model to discover suitable postures satisfying task and obstacle avoidance constraints is demonstrated by simulation. Also, task equivalent configurations that can be linked to recent findings in the motor control literature are generated by the proposed formalism due to its stochastic nature.

Evolutionary collision-free optimal trajectory planning for intelligent robots

This paper presents optimization procedures based on evolutionary algorithms such as the elitist non-dominated sorting genetic algorithm (NSGA-II) and differential evolution (DE) for solving the trajectory planning problem of intelligent robot manipulators with the prevalence of fixed, moving, and oscillating obstacles. The aim is the minimization of a combined objective function, with the constraints being actuator constraints, joint limits, and the obstacle avoidance constraint by considering dynamic equations of motion. Trajectories are defined by B-spline functions. This is a non-linear constrained optimization problem with six objective functions, 31 constraints, and 42 variables. The combined objective function is a weighted balance of transfer time, the mean average of actuator efforts and power, penalty for collision-free motion, singularity avoidance, joint jerks, and joint accelerations. The obstacles are present in the workspace of the robot. The distance between potentially colliding parts is expressed as obstacle avoidance. Further, the motion is represented using translational and rotational matrices. The proposed optimization techniques are explained by applying them to an industrial robot (PUMA 560 robot). Also, the results obtained from NSGA-II and DE are compared and analyzed. This is the first research work which considers all the decision criteria for the trajectory planning of industrial robots with obstacle avoidance. A comprehensive user-friendly general-purpose software package has been developed using VC++ to obtain the optimal solutions using the proposed DE algorithm.

Real-time control of redundant robotic manipulators for mobile obstacle avoidance

This paper offers an efficient solution for the real-time operation of a redundant robot moving in a variable environment. An approach for fixed and mobile obstacle avoidance is proposed. The basic idea is to solve the redundancy as an unconstrained optimization problem where the redundancy is integrated with the path tracking and the obstacle avoidance constraints into an augmented objective function. Here, an iterative solution of the Inverse Geometric Model (IGM) is used, which requires no matrix inversion and iterates directly on the joint position, being thus suitable for on-line application and also preserving repeatability. A method of formulating anti-collision constraints using a novel concept of pseudo-distance unifies in a simple and original way the modeling of various obstacles.

Coordination of reaching and grasping by capitalizing on obstacle avoidance and other constraints.

Reaching and grasping an object can be viewed as the solution of a multiple-constraint satisfaction problem. The constraints include contact with the object with the appropriate effectors in the correct positions as well as generation of a collision-free trajectory. We have developed a computational model that simulates reaching and grasping based on these notions. The model, rendered as an animation program, reproduces many basic features of the kinematics of human reaching and grasping behavior. The core assumptions of the model are: (1) tasks are defined by flexibly organized constraint hierarchies; (2) manual positioning acts, including prehension acts, are first specified with respect to goal postures and then are specified with respect to movements towards those goal postures; (3) goal postures are found by identifying the stored posture that is most promising for the task, as determined by the constraint hierarchy, and then by generating postures that are more and more dissimilar to the most-promising stored posture until a deadline is reached, at which time the best posture that was found during the search is defined as the goal posture; (4) depending on when the best posture was encountered in the search, the deadline for the search in the next trial is either increased or decreased; (5) specification of a movement to the goal posture begins with straight-line interpolation in joint space between the starting posture and goal posture; (6) if an internal simulation of this default movement suggests that it will result in collision with an obstacle, the movement can be reshaped until an acceptable movement is found or until time runs out; (7) movement reshaping occurs by identifying a via posture that serves as a body position to which the actor moves from the starting posture and then back to the starting posture, while simultaneously making the main movement from the starting posture to the goal posture; (8) the via posture is identified using the same posture-generating algorithm as used to identify the goal posture. These processes are used both for arm positioning and, with some elaboration, for prehension. The model solves a number of problems with an earlier model, although it leaves some other problems unresolved.

Collision-Free Cartesian Trajectory Generation Using Raster Scanning and Genetic Algorithms

An algorithm for Cartesian trajectory generation by redundant robots in environments with obstacles is presented. The algorithm combines a raster scanning technique, genetic algorithms and functions for interpolation in the joint coordinates space in order to approximate a desired Cartesian curve by the robot’s hand tip under maximum allowed position deviation. A raster scanning technique determines a minimal set of knot points on the desired curve in order to generate a Cartesian trajectory with bounded position approximation error. Genetic algorithms are used to determine an acceptable robot configuration under obstacle avoidance constraints corresponding to a knot point. Robot motion between two successive knot points is finally achieved using well known interpolation techniques in the joint coordinates space. The proposed algorithm is analyzed and its performance is demonstrated through simulated experiments carried out on planar redundant robots.

Application of genetic algorithms to point-to-point motion of redundant manipulators

In this paper, the problem of point-to-point motion of redundant robot manipulators working in environments with obstacles is considered. The problem is formulated as a constrained optimization problem and is solved using a method based on genetic algorithms (GAs). The objetive of this method is to minimize the end-effector's positional error subject to the obstacle avoidance constraints. The efficiency of the proposed method is demonstrated through multiple experiments carried out on a redundant planar manipulator.

Piecewise smooth and safe trajectory planning

SUMMARYA new approach to generating smooth piecewise local trajectories for mobile robots is proposed in this paper. Given the configurations (position and direction) of two points, we search for the trajectory that minimizes the integral of acceleration (tangential and normal). The resulting trajectory should not only be smooth but also safe in order to be applicable in real-life situations. Therefore, we investigate two different obstacle-avoidance constraints that satisfy the minimization problem. Unfortunately, in this case the problem becomes more complex and not suitable for real time implementations. Therefore, we introduce two simple solutions, based on the idea of polynomial fitting, to generate safe trajectories once a collision is detected with the original smooth trajectory. Simulation results of the different algorithms are presented.

Optimal robot plant planning using the minimum-time criterion

A path planning technique is presented which produces time-optimal manipulator motions in a workspace containing obstacles. The full nonlinear equations of motion are used in conjunction with the actuator limitations to produce optimal trajectories. The Cartesian path of the manipulator is represented with B-spline polynomials, and the shape of this path is varied in a manner that minimizes the traversal time. Obstacle avoidance constraints are included in the problem through the use of distance functions. In addition to computing the optimal path, the time-optimal open-loop joint forces and corresponding joint displacements are obtained as functions of time. The examples presented show a reduction in the time required for typical motions.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>