Exploring Robot Trajectory Planning -- A Comparative Analysis of Algorithms And Software Implementations in Dynamic Environments
- URL: http://arxiv.org/abs/2407.13330v1
- Date: Thu, 18 Jul 2024 09:30:27 GMT
- Title: Exploring Robot Trajectory Planning -- A Comparative Analysis of Algorithms And Software Implementations in Dynamic Environments
- Authors: Arunabh Bora,
- Abstract summary: Trajectory planning is a crucial word in Modern & Advanced Robotics.
It's a way of generating a smooth and feasible path for the robot to follow over time.
Trajectory planning is extensively used in Automobile Industrial Robot, Manipulators, and Mobile Robots.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Trajectory Planning is a crucial word in Modern & Advanced Robotics. It's a way of generating a smooth and feasible path for the robot to follow over time. The process primarily takes several factors to generate the path, such as velocity, acceleration and jerk. The process deals with how the robot can follow a desired motion path in a suitable environment. This trajectory planning is extensively used in Automobile Industrial Robot, Manipulators, and Mobile Robots. Trajectory planning is a fundamental component of motion control systems. To perform tasks like pick and place operations, assembly, welding, painting, path following, and obstacle avoidance. This paper introduces a comparative analysis of trajectory planning algorithms and their key software elements working strategy in complex and dynamic environments. Adaptability and real-time analysis are the most common problems in trajectory planning. The paper primarily focuses on getting a better understanding of these unpredictable environments.
Related papers
- Robotic warehousing operations: a learn-then-optimize approach to large-scale neighborhood search [84.39855372157616]
This paper supports robotic parts-to-picker operations in warehousing by optimizing order-workstation assignments, item-pod assignments and the schedule of order fulfillment at workstations.
We solve it via large-scale neighborhood search, with a novel learn-then-optimize approach to subproblem generation.
In collaboration with Amazon Robotics, we show that our model and algorithm generate much stronger solutions for practical problems than state-of-the-art approaches.
arXiv Detail & Related papers (2024-08-29T20:22:22Z) - Multi-Agent Path Finding with Real Robot Dynamics and Interdependent Tasks for Automated Warehouses [1.2810395420131764]
Multi-Agent Path Finding (MAPF) is an important optimization problem underlying the deployment of robots in automated warehouses and factories.
We consider a realistic problem of online order delivery in a warehouse, where a fleet of robots bring the products belonging to each order from shelves to workstations.
This creates a stream of inter-dependent pickup and delivery tasks and the associated MAPF problem consists of computing realistic collision-free robot trajectories.
arXiv Detail & Related papers (2024-08-26T15:13:38Z) - A Meta-Engine Framework for Interleaved Task and Motion Planning using Topological Refinements [51.54559117314768]
Task And Motion Planning (TAMP) is the problem of finding a solution to an automated planning problem.
We propose a general and open-source framework for modeling and benchmarking TAMP problems.
We introduce an innovative meta-technique to solve TAMP problems involving moving agents and multiple task-state-dependent obstacles.
arXiv Detail & Related papers (2024-08-11T14:57:57Z) - RoboScript: Code Generation for Free-Form Manipulation Tasks across Real
and Simulation [77.41969287400977]
This paper presents textbfRobotScript, a platform for a deployable robot manipulation pipeline powered by code generation.
We also present a benchmark for a code generation benchmark for robot manipulation tasks in free-form natural language.
We demonstrate the adaptability of our code generation framework across multiple robot embodiments, including the Franka and UR5 robot arms.
arXiv Detail & Related papers (2024-02-22T15:12:00Z) - POA: Passable Obstacles Aware Path-planning Algorithm for Navigation of
a Two-wheeled Robot in Highly Cluttered Environments [53.41594627336511]
Passable Obstacles Aware (POA) planner is a novel navigation method for two-wheeled robots in a cluttered environment.
Our algorithm allows two-wheeled robots to find a path through passable obstacles.
arXiv Detail & Related papers (2023-07-16T19:44:27Z) - Logic programming for deliberative robotic task planning [2.610470075814367]
We present a survey on recent advances in the application of logic programming to the problem of task planning.
We analyze different planners and their suitability for specific robotic applications, based on expressivity in domain representation, computational efficiency and software implementation.
arXiv Detail & Related papers (2023-01-18T14:11:55Z) - SABER: Data-Driven Motion Planner for Autonomously Navigating
Heterogeneous Robots [112.2491765424719]
We present an end-to-end online motion planning framework that uses a data-driven approach to navigate a heterogeneous robot team towards a global goal.
We use model predictive control (SMPC) to calculate control inputs that satisfy robot dynamics, and consider uncertainty during obstacle avoidance with chance constraints.
recurrent neural networks are used to provide a quick estimate of future state uncertainty considered in the SMPC finite-time horizon solution.
A Deep Q-learning agent is employed to serve as a high-level path planner, providing the SMPC with target positions that move the robots towards a desired global goal.
arXiv Detail & Related papers (2021-08-03T02:56:21Z) - An advantage actor-critic algorithm for robotic motion planning in dense
and dynamic scenarios [0.8594140167290099]
In this paper, we modify existing advantage actor-critic algorithm and suit it to complex motion planning.
It achieves higher success rate in motion planning with lesser processing time for robot to reach its goal.
arXiv Detail & Related papers (2021-02-05T12:30:23Z) - Fast-reactive probabilistic motion planning for high-dimensional robots [15.082715993594121]
p-Chekov is a fast-reactive motion planning system that can provide safety guarantees for high-dimensional robots suffering from process noises and observation noises.
Comprehensive theoretical and empirical analysis shows that p-Chekov can effectively satisfy user-specified chance constraints over collision risk in practical robotic manipulation tasks.
arXiv Detail & Related papers (2020-12-03T17:51:07Z) - Predicting Sample Collision with Neural Networks [5.713670854553366]
We present a framework to address the cost of expensive primitive operations in sampling-based motion planning.
We evaluate our framework on multiple planning problems with a variety of robots in 2D and 3D workspaces.
arXiv Detail & Related papers (2020-06-30T14:56:14Z) - Thinking While Moving: Deep Reinforcement Learning with Concurrent
Control [122.49572467292293]
We study reinforcement learning in settings where sampling an action from the policy must be done concurrently with the time evolution of the controlled system.
Much like a person or an animal, the robot must think and move at the same time, deciding on its next action before the previous one has completed.
arXiv Detail & Related papers (2020-04-13T17:49:29Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.