On the performance of sampling-based optimal motion planners

Sampling based algorithms provide efficient methods of solving robot motion planning problem. The advantage of these approaches is the ease of their implementation and their computational efficiency. These algorithms are probabilistically complete i.e. they will find a solution if one exists, given a suitable run time. The drawback of sampling based planners is that there is no guarantee of the quality of their solutions. In fact, it was proven that their probability of reaching an optimal solution approaches zero. A breakthrough in sampling planning was the proposal of optimal based sampling planners. Current optimal planners are characterized with asymptotic optimality i.e. they reach an optimal solutions as time approaches infinity. Motivated by the slow convergence of optimal planners, post-processing and heuristic approach have been suggested. Due to the nature of the sampling based planners, their implementation requires tuning and selection of a large number of parameters that are often overlooked. This paper presents the performance study of an optimal planner under different parameters and heuristics. We also propose a modification in the algorithm to improve the convergence rate towards an optimal solution

Autonomous robots path planning: An adaptive roadmap approach

Developing algorithms that allow robots to independently navigate unknown environments is a widely researched area of robotics. The potential for autonomous mobile robots use, in industrial and military applications, is boundless. Path planning entails computing a collision free path from a robots current position to a desired target. The problem of path planning for these robots remains underdeveloped. Computational complexity, path optimization and robustness are some of the issues that arise. Current algorithms do not generate general solutions for different situations and require user experience and optimization. Classical algorithms are computationally extensive. This reduces the possibility of their use in real time applications. Additionally, classical algorithms do not allow for any control over attributes of the generated path. A new roadmap path planning algorithm is proposed in this paper. This method generates waypoints, through which the robot can avoid obstacles and reach its goal. At the heart of this algorithm is a method to control the distance of the waypoints from obstacles, without increasing its computational complexity. Several simulations were run to illustrate the robustness and adaptability of this approach, compared to the most commonly used path planning methods

Examining the use of B-splines in parking assist systems

The main objective of the presented study and simulations conducted was to investigate the prospect of using B-spline curves for the automatic parking, i.e. self-driving, or intelligent vehicles. We consider the problem of parallel parking for a non-holonomic vehicle with a known maximum path curvature. The relationship between the properties of the path and the geometry of corresponding parking spot is revealed. The unique properties of B-splines are exploited to synthesize a path that is smooth and of continuous curvature. The contributions of this project are in the generations of better, smooth continuous paths. This improves passenger comfort during the parallel parking maneuver and allow vehicles to park in tighter spots by increasing the feasible range of the parking manoeuver

Sampling-based robot motion planning: a review

Motion planning is a fundamental research area in robotics. Sampling-based methods offer an efcient solution for what is otherwise a rather challenging dilemma of path planning. Consequently, these methods have been extended further away from basic robot planning into further difcult scenarios and diverse applications. A comprehensive survey of the growing body of work in sampling-based planning is given here. Simulations are executed to evaluate some of the proposed planners and highlight some of the implementation details that are often left unspecied. An emphasis is placed on contemporary research directions in this eld. We address planners that tackle current issues in robotics. For instance, real-life kinodynamic planning, optimal planning, replanning in dynamic environments, and planning under uncertainty are discussed. The aim of this paper is to survey the state of the art in motion planning and to assess selected planners, examine implementation details and above all shed a light on the current challenges in motion planning and the promising approaches that will potentially overcome those problems

Goal-Directed Reasoning and Cooperation in Robots in Shared Workspaces: an Internal Simulation Based Neural Framework

From social dining in households to product assembly in manufacturing lines, goal-directed reasoning and cooperation with other agents in shared workspaces is a ubiquitous aspect of our day-to-day activities. Critical for such behaviours is the ability to spontaneously anticipate what is doable by oneself as well as the interacting partner based on the evolving environmental context and thereby exploit such information to engage in goal-oriented action sequences. In the setting of an industrial task where two robots are jointly assembling objects in a shared workspace, we describe a bioinspired neural architecture for goal-directed action planning based on coupled interactions between multiple internal models, primarily of the robot’s body and its peripersonal space. The internal models (of each robot’s body and peripersonal space) are learnt jointly through a process of sensorimotor exploration and then employed in a range of anticipations related to the feasibility and consequence of potential actions of two industrial robots in the context of a joint goal. The ensuing behaviours are demonstrated in a real-world industrial scenario where two robots are assembling industrial fuse-boxes from multiple constituent objects (fuses, fuse-stands) scattered randomly in their workspace. In a spatially unstructured and temporally evolving assembly scenario, the robots employ reward-based dynamics to plan and anticipate which objects to act on at what time instances so as to successfully complete as many assemblies as possible. The existing spatial setting fundamentally necessitates planning collision-free trajectories and avoiding potential collisions between the robots. Furthermore, an interesting scenario where the assembly goal is not realizable by either of the robots individually but only realizable if they meaningfully cooperate is used to demonstrate the interplay between perception, simulation of multiple internal models and the resulting complementary goal-directed actions of both robots. Finally, the proposed neural framework is benchmarked against a typically engineered solution to evaluate its performance in the assembly task. The framework provides a computational outlook to the emerging results from neurosciences related to the learning and use of body schema and peripersonal space for embodied simulation of action and prediction. While experiments reported here engage the architecture in a complex planning task specifically, the internal model based framework is domain-agnostic facilitating portability to several other tasks and platforms

Randomised kinodynamic motion planning for an autonomous vehicle in semi-structured agricultural areas

A randomised motion planner is presented that operates within a suitable timeframe for constrained mobile robots in agricultural environment. The core of this approach relies on splitting planning into two efficient phases to reduce its computational time. The effectiveness of sampling based planners is combined with the robustness of parametric vector-valued splines. The first phase involves relaxed two-dimensional path planning using rapidly-exploring random trees (RRT). Recent advances in sampling based planning are leveraged to improve the performance of the planner. Detailed implementation of the RRT approach and parameter selection are highlighted using comprehensive analysis and simulations. Feasible continuous paths with bounded curvature for nonholonomic robots are generated using B-spline curves. Curve segment parameters are formulated with respect to vehicle specifications. Manoeuvres satisfying maximum curvature constraints and path continuity are designed based on the segment parameters. Numerical experiments are used to validate the practicality of the proposed two-phase planner in solving kinodynamic motion queries, in real-time and replanning under limited sensing conditions

Robotics application in remote data acquisition and control for solar ponds

This paper presents one application of industrial robots in the automation of renewable energy production. The robot supports remote performance monitoring and maintenance of salinity gradient solar ponds. The details of the design, setup and the use of the robot sampling station and the remote Data Acquisition (DAQ) system are given here. The use of a robot arm, to position equipment and sensors, provides accurate and reliable real time data needed for autonomous monitoring and control of this type of green energy production. Robot upgrade of solar ponds can be easily integrated with existing systems. Data logged by the proposed system can be remotely accessed, plotted and analysed. Thus the simultaneous and remote monitoring of a large scale network of ponds can be easily implemented. This provides a fully automated solution to the monitoring and control of green energy production operations, which can be used to provide heat and electricity to buildings. Remote real time monitoring will facilitate the setup and operations of several solar ponds around cities

Developing a navigation system for mobile robots

Design solution of a novel mobile robot navigation system, presented here, is used to control robot's locomotion across slippery surfaces. Usually, motion control strategies, are based on assumption of sufficient traction between tyres and the road. Motion across slippery surfaces can endanger the robot and its surroundings. Our solution combines Light Detection and Ranging (LIDAR) measurements with odometry data. It performs well on any surface, regardless of sensing, localization and navigation errors, within an indoor environment, in real-time. An accelerated feature detection method is used to improve LIDAR localization update rate and improve localization accuracy. Experiments conducted validate proposed approach

In the Passenger Seat: Investigating comfort measures in autonomous cars

The prospect of driverless cars wide-scale deployment is imminent owing to the advances in robotics, computational power, communications, and sensor technologies. This promises highway fatality reductions and improvements in traffic and fuel efficiency. Our understanding of the effects arising from commuting in autonomous cars is still limited. The novel concept of the loss of driver controllability is introduced here. It requires a reassessment of vehicle's comfort criteria. In this review paper, traditional comfort measures are examined and autonomous passenger awareness factors are proposed. We categorize path-planning methods in light of the offered factors. The objective of the review presented in this article is to highlight the gap in path planning from a passenger comfort perspective and propose some research solutions. It is expected that this investigation will generate more research interest and bring innovative solutions into this field

Continuous path smoothing for car-like robots using B-spline curves?

A practical approach for generating motion paths with continuous steering for car-like mobile robots is presented here. This paper addresses two key issues in robot motion planning; path continuity and maximum curvature constraint for nonholonomic robots. The advantage of this new method is that it allows robots to account for their constraints in an efficient manner that facilitates real-time planning. B-spline curves are leveraged for their robustness and practical synthesis to model the vehicle's path. Comparative navigational-based analyses are presented to selected appropriate curve and nominate its parameters. Path continuity is achieved by utilizing a single path, to represent the trajectory, with no limitations on path, or orientation. The path parameters are formulated with respect to the robot's constraints. Maximum curvature is satisfied locally, in every segment using a smoothing algorithm, if needed. It is demonstrated that any local modifications of single sections have minimal effect on the entire path. Rigorous simulations are presented, to highlight the benefits of the proposed method, in comparison to existing approaches with regards to continuity, curvature control, path length and resulting acceleration. Experimental results validate that our approach mimics human steering with high accuracy. Accordingly, efficiently formulated continuous paths ultimately contribute towards passenger comfort improvement. Using presented approach, autonomous vehicles generate and follow paths that humans are accustomed to, with minimum disturbances