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An Optimal and Energy Efficient Multi-Sensor Collision-Free Path Planning Algorithm for a Mobile Robot in Dynamic Environments
Abstract and Figures
There has been a remarkable growth in many different real-time systems in the area of autonomous mobile robots. This paper focuses on the collaboration of efficient multi-sensor systems to create new optimal motion planning for mobile robots. A proposed algorithm is used based on a new model to produce the shortest and most energy-efficient path from a given initial point to a goal point. The distance and time traveled, in addition to the consumed energy, have an asymptotic complexity of O(nlogn), where n is the number of obstacles. Real time experiments are performed to demonstrate the accuracy and energy efficiency of the proposed motion planning algorithm.
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... In the global path planning part, A* method [7] as one of the famous global planners has been improved in various ways such as using an energy-related cost function [8] or optimization algorithms [9]. In the local one, energy constraints were added to cost functions [10] or multi-sensors were used to generate optimal trajectories [11]. Using optimal control theory to achieve optimal velocity trajectory [12] and adding a model predictive control for trajectory tracking [13,14] have been famous ways in the motion control stage. ...
... During a mission, unexpected obstacles might appear to cross the SGVs' defined paths such as humans and other vehicles. An increase in the number of obstacles has a direct effect on SGV energy consumption [11]. Moreover, a nonoptimal reaction to avoid obstacles might raise the distance to the destination and waste time by generating useless motion [18]. ...
... The proposed method was modified by defining a new energy model and representing a new cost objective in order to reduce power consumption [45]. Alajlan et al. [46] proposed the use of a multi-sensor method for a WMR. To this end, infrared reflective sensors were applied for edge detection, and infrared measuring sensors, an ultrasonic sensor, and a camera were applied for obstacle detection, creating an integrated framework. ...
In recent years, the use of electric Autonomous Wheeled Mobile Robots (AWMRs) has dramatically increased in transport of the production chain. Generally, AWMRs must operate for several hours on a single battery charge. Since the energy density of the battery is limited, energy efficiency becomes a key element in improving material transportation performance during the manufacturing process. However, energy consumption is influenced by the navigation stages, because the type of motion necessary for the AWMR to perform during a mission is totally defined by these stages. Therefore, this paper analyzes methods of energy efficiency that have been studied recently for AWMR navigation stages. The selected publications are classified into planning and motion control categories in order to identify research gaps. Unlike other similar studies, this work focuses on these methods with respect to their implications for the energy consumption of AWMRs. In addition, by using an industrial Self-Guided Vehicle (SGV), we illustrate the direct influence of the motion planning stage on global energy consumption by means of several simulations and experiments. The results indicate that the reaction of the SGV in response to unforeseen obstacles can affect the amount of energy consumed. Hence, energy constraints must be considered when developing the motion planning of AWMRs.
... Facing the problem of enhancing energetic efficiency by focusing on motion design has the advantage of being a 'software solution' that can be applied to a wide range of systems. Not only are industrial robots the subject of studies on energy efficiency, since energy-saving trajectories for mobile robots are under investigation as well [11,12]. ...
This work explores the possibility of exploiting kinematic redundancy as a tool to enhance the energetic performance of a robotic cell. The test case under consideration comprises a three-degree-of-freedom Selective Compliance Assembly Robot Arm (SCARA) robot and an additional linear unit that is used to move the workpiece during a pick and place operation. The trajectory design is based on a spline interpolation of a sequence of via-points: The corresponding motion of the joints is used to evaluate, through the use of an inverse dynamic model, the actuators effort and the associated power consumption by the robot and by the linear unit. Numerical results confirm that the suggested method can improve both the execution time and the overall energetic efficiency of the cell.
Purpose
This paper aims to provide a comprehensive analysis of the state of the art in energy efficiency for autonomous mobile robots (AMRs), focusing on energy sources, consumption models, energy-efficient locomotion, hardware energy consumption, optimization in path planning and scheduling methods, and to suggest future research directions.
Design/methodology/approach
The systematic literature review (SLR) identified 244 papers for analysis. Research articles published from 2010 onwards were searched in databases including Google Scholar, ScienceDirect and Scopus using keywords and search criteria related to energy and power management in various robotic systems.
Findings
The review highlights the following key findings: batteries are the primary energy source for AMRs, with advances in battery management systems enhancing efficiency; hybrid models offer superior accuracy and robustness; locomotion contributes over 50% of a mobile robot’s total energy consumption, emphasizing the need for optimized control methods; factors such as the center of mass impact AMR energy consumption; path planning algorithms and scheduling methods are essential for energy optimization, with algorithm choice depending on specific requirements and constraints.
Research limitations/implications
The review concentrates on wheeled robots, excluding walking ones. Future work should improve consumption models, explore optimization methods, examine artificial intelligence/machine learning roles and assess energy efficiency trade-offs.
Originality/value
This paper provides a comprehensive analysis of energy efficiency in AMRs, highlighting the key findings from the SLR and suggests future research directions for further advancements in this field.
The local path planning, as one of the navigation stages, plays a significant role in the energy consumption of Self-Guided Vehicles (SGV). Since SGV must operate for several hours on a single battery charge to transport loads, its energy consumption is a critical issue. Therefore, this article puts forward an approach for boosting the energy efficiency of the local path planning stage using load position. Unlike other similar works which solely use robots’ kinematic and kinetic constraints to develop energy-efficient local path planners, this article considers the effect of load position on SGV’s dynamic. In this regard, first, the kinetic model of the differential drive SGV is developed to consider the change of SGV’s Center of Mass (CoM) affected by load properties. Second, machine learning methods are used to create two learning models for online estimation of the position of CoM (PoCoM) and prediction of required energy of sample trajectories. Hence, the generated SGV’s kinetic model is used to train the learning models. Finally, estimated parameters are employed to add a new constraint to extend the cost function of the local path planner. The outcomes of the study show that the proposed planner generates smoother and shorter paths to pass obstacles and corridors than a general one. Thus, SGV’s energy consumption decreases by considering the load effect.
This paper describes a rule base-Sugeno fuzzy hybrid controller for path planning of single as well as multiple humanoid robots in cluttered environments. Initially, sensor outputs regarding the obstacle distances are used as inputs to the rule base model, and turning angle is obtained as the output. The rule-based analysis is used for training the fuzzy controller with membership functions. The output from the rule base model along with other regular inputs is supplied to a Sugeno fuzzy model, and effective turning angle is obtained as the final output to avoid the obstacles present in the environment and navigate the humanoids safely to their target points. The proposed hybrid controller is tested on a V-REP simulation platform, and the simulation results are validated in an experimental set-up. To avoid the possibility of any inter-collision during navigation of multiple humanoids on a common platform, a Petri-net scheme is integrated along with the proposed hybrid model. Finally, the results obtained from simulation and experimental platforms are compared against each other with proper agreement and minimal percentage of deviations. To validate the proposed controller, it has also been tested against another existing navigational approach, and satisfactory performance enhancement has been observed.
Traditional Reinforcement Learning (RL) approaches are designed to work well in static environments. In many real-world scenarios, the environments are complex and dynamic, in which the performance of traditional RL approaches may drastically degrade. One of the factors that result in the dynamicity and complexity of the environment is a change in the position and number of obstacles. This article presents a path planning approach for autonomous mobile robots in a complex dynamic indoor environment, where the dynamic pattern of obstacles will not drastically affect the performance of RL models. Two independent modules, collision avoidance without considering the goal position and goal-seeking without considering obstacles avoidance, are trained independently using Artificial Neural Networks (ANN) and RL, to obtain their best control policies. Then, a switching function is used to combine the two trained modules for realizing the obstacle avoidance and global path planning in a complex dynamic indoor environment. Furthermore, this control system is designed with a special focus on the computational and memory requirements of resource-constrained robots. The design was tested in a realworld environment on a mini-robot with constrained resources. Along with static and dynamic obstacles avoidance, this system has the ability to achieve both static and dynamic targets. This control system can also be used to train a robot in the real world using RL when the robot cannot afford to collide. Robot behavior in the real ground shows a very strong correlation with the simulation results.
The field of autonomous mobile robotics has recently gained many researchers' interests. Due to the specific needs required by various applications of mobile robot systems, especially in navigation, designing a real time obstacle avoidance and path following robot system has become the backbone of controlling robots in unknown environments. Therefore, an efficient collision avoidance and path following methodology is needed to develop an intelligent and effective autonomous mobile robot system. This paper introduces a new technique for line following and collision avoidance in the mobile robotic systems. The proposed technique relies on the use of low-cost infrared sensors, and involves a reasonable level of calculations, so that it can be easily used in real-time control applications. The simulation setup is implemented on multiple scenarios to show the ability of the robot to follow a path, detect obstacles, and navigate around them to avoid collision. It also shows that the robot has been successfully following very congested curves and has avoided any obstacle that emerged on its path. Webots simulator was used to validate the effectiveness of the proposed technique.
Autonomous mobile robots have become a very popular and interesting topic in the last decade. Each of them are equipped with various types of sensors such as GPS, camera, infrared and ultrasonic sensors. These sensors are used to observe the surrounding environment. However, these sensors sometimes fail and have inaccurate readings. Therefore, the integration of sensor fusion will help to solve this dilemma and enhance the overall performance. This paper presents a collision free mobile robot navigation based on the fuzzy logic fusion model. Eight distance sensors and a range finder camera are used for the collision avoidance approach where three ground sensors are used for the line or path following approach. The fuzzy system is composed of nine inputs which are the eight distance sensors and the camera, two outputs which are the left and right velocities of the mobile robot’s wheels, and 24 fuzzy rules for the robot’s movement. Webots Pro simulator is used for modeling the environment and the robot. The proposed methodology, which includes the collision avoidance based on fuzzy logic fusion model and line following robot, has been implemented and tested through simulation and real time experiments. Various scenarios have been presented with static and dynamic obstacles using one robot and two robots while avoiding obstacles in different shapes and sizes.
This paper deals with the path-planning problem for mobile wheeled- or tracked-robot which drive in 2.5-D environments, where the traversable surface is usually considered as a 2-D-manifold embedded in a 3-D ambient space. Specially, we aim at solving the 2.5-D navigation problem using raw point cloud as input. The proposed method is independent of traditional surface parametrization or reconstruction methods, such as a meshing process, which generally has high-computational complexity. Instead, we utilize the output of 3-D tensor voting framework on the raw point clouds. The computation of tensor voting is accelerated by optimized implementation on graphics computation unit. Based on the tensor voting results, a novel local Riemannian metric is defined using the saliency components, which helps the modeling of the latent traversable surface. Using the proposed metric, we prove that the geodesic in the 3-D tensor space leads to rational path-planning results by experiments. Compared to traditional methods, the results reveal the advantages of the proposed method in terms of smoothing the robot maneuver while considering the minimum travel distance.
To avoid obstacle in the unknown environment for unmanned undersea vehicle (UUV), an obstacle avoiding system based on improved vector field histogram (VFH) is designed. Forward looking sonar is used to detect the environment, and the divisional sonar modal is applied to deal with the measure uncertainty. To adapt to the VFH, rolling occupancy grids are used for the map building, and high accuracy details of local environment are obtained. The threshold is adaptively adjusted by the statistic of obstacles to solve the problem that VFH is sensitive to threshold. To improve the environment adaptability, the hybrid-behaviors strategy is proposed, which selects the optimal avoidance command according to the motion status and environment character. The simulation shows that UUV could avoid the obstacles fast and escape from the U shape obstacles.
Collision avoidance is a fundamental requirement for mobile robots. Avoiding moving obstacles (also termed dynamic obstacles) with unpredictable direction changes, such as humans, is more challenging than avoiding moving obstacles whose motion can be predicted. Precise information on the future moving directions of humans is unobtainable for use in navigation algorithms. Furthermore, humans should be able to pursue their activities unhindered and without worrying about the robots around them. In this paper, both active and critical regions are used to deal with the uncertainty of human motion. A procedure is introduced to calculate the region sizes based on worst‐case avoidance conditions. Next, a novel virtual force
field‐based mobile robot navigation algorithm (termed QVFF) is presented. This algorithm may be used with both holonomic and nonholonomic robots. It incorporates improved virtual force functions for avoiding moving obstacles and its stability is proven using a piecewise continuous Lyapunov function. Simulation and experimental results are provided for a human walking towards the robot and blocking the path to a goal location. Next, the proposed algorithm is compared with five state‐of‐the‐art navigation algorithms for an environment with one human walking with an unpredictable change in direction. Finally, avoidance
results are presented for an environment containing three walking humans. The QVFF algorithm consistently generated collision‐free paths to the goal.
In the intelligent transportation system, various accident avoidance techniques have been applied. Among them, one of the most common issues is the collision, which is yet unsolved problem. To this end, we develop collision warning and avoidance system (CWAS), which is implemented in the wheeled mobile robot. Likewise, path planning is a crucial problem in the mobile robots to perform a given task correctly. Here, a tracking system is presented for the mobile robot, which follows an object. Thus, we have implemented an integrated CWAS and tracking system in the mobile robot. Both systems can be activated independently. In the CWAS, the robot is controlled through a remotely controlled device, and collision prediction and avoidance functions are performed. In the tracking system, the robot performs tasks autonomously, where the robot maintains a constant distance from the followed object. The surrounding information is obtained through the range sensors, and the control functions are performed through the microcontroller. The front, left, and right sensors are activated to track the object, and all the sensors are used for the CWAS. Two algorithms based on the sensory information are developed with the distance control approach. The proposed system is tested using the binary logic controller and the fuzzy logic controller (FLC). The comparison of both controllers is also described by preferring time delay and complexity. The efficiency of the robot is improved by increasing smoothness in motion using the FLC, achieving accuracy in tracking, and advancements in the CWAS. Finally, simulation and experimental outcomes have displayed the authenticity of the system.
This paper describes some improvements of Vector Field Histogram method used as reactive navigation of a mobile robot. In our application a laser rangefinder as a primary sensor for scanning an environment is used. Therefore, improvements in computation of the histogram values are presented. Further a VFH*-based technique that enables robot to sense and avoid dynamic obstacles is proposed. (C) 2012 Published by Elsevier Ltd.Selection and/or peer-review under responsibility of the Branch Office of Slovak Metallurgical Society at Faculty of Metallurgy and Faculty of Mechanical Engineering, Technical University of Kosice
Focusing on the navigation problem of mobile robots in environments with incomplete knowledge, a new hybrid navigation algorithm is proposed. The novel system architecture in the proposed algorithm is the main contribution of this paper. Unlike most existing hybrid navigation systems whose deliberative layers usually play the dominant role while the reactive layers are only simple executors, a more independent reactive layer that can guarantee convergence without the assistance of a deliberative layer is pursued in the proposed architecture, which brings two benefits. First, the burden of the deliberative layer is released, which is beneficial to guaranteeing real-time property and decreasing resource requirement. Second, some possible layer conflicts in the traditional architecture can be resolved, which improves the system stability. The convergence of the new algorithm has been proved. The simulation results show that compared with three traditional algorithms based on different architectures, the new hybrid navigation algorithm proposed in this paper performs more reliable in terms of escaping from traps, resolving conflicts between layers and decreasing the computational time for avoiding time out of the control cycle. The experiments on a real robot further verify the validity and applicability of the new algorithm.
In this paper, a novel obstacle avoidance method is designed and applied to an experimental autonomous ground vehicle system. The proposed method brings a new solution to the problem and has several advantages compared to previous methods. This novel algorithm is easy to tune and it takes into consideration the field of view and the nonholonomic constraints of the robot. Moreover the method does not have a local minimum problem and results in safer trajectories because of its inherent properties in the definition of the algorithm. The proposed algorithm is tested in simulations and after the observation of successful results, experimental tests are performed using static and dynamic obstacle scenarios. The experimental test platform is an autonomous ground vehicle with Ackermann steering geometry which brings nonholonomic constraints to the vehicle. Experimental results show that the task of obstacle avoidance can be achieved using the algorithm on the autonomous vehicle platform. The algorithm is very promising for application in mobile and industrial robotics where obstacle avoidance is a feature of the robotic system.