Motion planning

About: Motion planning is a research topic. Over the lifetime, 32846 publications have been published within this topic receiving 553548 citations.

Modified A-Star Algorithm for Efficient Coverage Path Planning in Tetris Inspired Self-Reconfigurable Robot with Integrated Laser Sensor.

Abstract: Advancing an efficient coverage path planning in robots set up for application such as cleaning, painting and mining are becoming more crucial. Such drive in the coverage path planning field proposes numerous techniques over the past few decades. However, the proposed approaches were only applied and tested with a fixed morphological robot in which the coverage performance was significantly degraded in a complex environment. To this end, an A-star based zigzag global planner for a novel self-reconfigurable Tetris inspired cleaning robot (hTetro) presented in this paper. Unlike the traditional A-star algorithm, the presented approach can generate waypoints in order to cover the narrow spaces while assuming appropriate morphology of the hTtero robot with the objective of maximizing the coverage area. We validated the efficiency of the proposed planning approach in the Robot Operation System (ROS) Based simulated environment and tested with the hTetro robot in real-time under the controlled scenarios. Our experiments demonstrate the efficiency of the proposed coverage path planning approach resulting in superior area coverage performance in all considered experimental scenarios.

Focused Trajectory Planning for autonomous on-road driving

Abstract: On-road motion planning for autonomous vehicles is in general a challenging problem. Past efforts have proposed solutions for urban and highway environments individually. We identify the key advantages/shortcomings of prior solutions, and propose a novel two-step motion planning system that addresses both urban and highway driving in a single framework. Reference Trajectory Planning (I) makes use of dense lattice sampling and optimization techniques to generate an easy-to-tune and human-like reference trajectory accounting for road geometry, obstacles and high-level directives. By focused sampling around the reference trajectory, Tracking Trajectory Planning (II) generates, evaluates and selects parametric trajectories that further satisfy kinodynamic constraints for execution. The described method retains most of the performance advantages of an exhaustive spatiotemporal planner while significantly reducing computation.

Robust Control of Mobility and Communications in Autonomous Robot Teams

Abstract: A team of robots is deployed to accomplish a task while maintaining a viable ad-hoc network capable of supporting data transmissions necessary for task fulfillment. Solving this problem necessitates: (i) Estimation of the wireless propagation environment to identify viable point-to-point communication links. (ii) Determination of end-to-end routes to support data traffic. (iii) Motion control algorithms to navigate through spatial configurations that guarantee required minimum levels of service. Consequently, we present methods for: (i) Estimation of point-to-point channels using pathloss and spatial Gaussian process models. (ii) Data routing so as to determine suitable end-to-end communication routes given estimates of point-topoint channel rates. (iii) Motion planning to determine robot trajectories restricted to configurations that ensure survival of the communication network. Due to the inherent uncertainty of wireless channels, the model of links and routes is stochastic. The criteria for route selection is to maximize the probability of network survival – defined as the ability to support target communication rates – given achievable rates on local point-topoint links. Maximum survival probability routes for present and future positions are input into a mobility control module that determines robot trajectories restricted to configurations that ensure the probability of network survival stays above a minimum reliability level. Local trajectory planning is proposed for simple environments and global planning is proposed for complex surroundings. The three proposed components are integrated and tested in experiments run in two different environments. Experimental results show successful navigation with continuous end-to-end connectivity.

Smooth and efficient obstacle avoidance for a tour guide robot

Abstract: We present the local path planning and obstacle avoidance method used on the autonomous tour-guide robot RoboX. It has proven its value during a 5 month operation of ten such robots in a real-world application, a very crowded exhibition. Three known approaches (DWA, elastic band, NF1) have been integrated into a system that performs smooth motion efficiently, in the sense of computational effort as well as goal-directedness. Apart from modifications to the DWA and the elastic band, we present the formulations that allow this fusion.

Double global optimum genetic algorithm–particle swarm optimization-based welding robot path planning

Abstract: Spot-welding robots have a wide range of applications in manufacturing industries. There are usually many weld joints in a welding task, and a reasonable welding path to traverse these weld joints has a significant impact on welding efficiency. Traditional manual path planning techniques can handle a few weld joints effectively, but when the number of weld joints is large, it is difficult to obtain the optimal path. The traditional manual path planning method is also time consuming and inefficient, and cannot guarantee optimality. Double global optimum genetic algorithm–particle swarm optimization (GA-PSO) based on the GA and PSO algorithms is proposed to solve the welding robot path planning problem, where the shortest collision-free paths are used as the criteria to optimize the welding path. Besides algorithm effectiveness analysis and verification, the simulation results indicate that the algorithm has strong searching ability and practicality, and is suitable for welding robot path planning.