Keywords

1 Introduction

Nowadays, mobile robotics provides new opportunities for developing novel robotic systems. A wide range of wheels of various sizes, different design types and materials used allow to integrate mobile robotic platforms into many areas of a human life. Common mobile robot applications include industrial automation [1], transportation [2], medical care [3], emergency rescue operations [4], and other areas. Mobile robots are featured by different motion systems.

There are two different types of mobile robots drive systems: a holonomic and a non-holonomic. For a wheeled robot, a non-holonomic drive system is a robot configuration limited by a number of wheels or their orientation. Holonomic drive systems have more than two degrees of freedom, which provide more freedom and flexibility of motion. The main benefit of a holonomic drive system is an ability to travel in any desired direction at any specified orientation without additional rotations with regard to Z-axis (yaw) of a series of intermediate motions (e.g., a typical car parking procedure). To perform such locomotion a robotic platform uses a special design of wheels called mecanum or omnidirectional [5]. Omnidirectional wheels increase a robot mobility and are used in tasks where a high maneuverability is required. Omnidirectional robotic platforms are ideal for working zones with a limited space access and cluttered environments, e.g., for scheduling pick-up and delivery tasks in hospitals [6].

Performing safe robot navigation is a general issue faced by a robot operating in a real environment [7]. Real world environments usually contain people and other dynamic obstacles. A real-time path planning is an essential part of an autonomous exploration. An obstacle avoidance capability used by a path planning approach must detect obstacles quickly and replan an actual route [8]. A path planning part of a robot navigation involves global and local planners [9]. While a global planner finds an optimal path with a prior knowledge of an environment and static obstacles, a local planner recalculates the path to avoid dynamic obstacles. The main goal of a local planning approach is adjusting a plan produced by a global planner in an online fashion.

This paper presents a comparison of ROS local planners supporting a holonomic drive system: Trajectory Rollout [10], DWA [11], EBand [12] and TEB [13]. These local planner algorithms were selected because they are most popular for ROS environment, easily pluggable and support a holonomic motion. The main contribution of the paper is a benchmark to discover the most suitable ROS local planner for a holonomic system used within a dynamic environment. Virtual experiments were conducted in Gazebo simulator [14] using static and dynamic obstacles.

2 System Setup

2.1 Mecanum Wheel Robot

A virtual model of a modular multifunctional robotic omni-wheeled mobile platform ArtBul [15] was used for experiments. Mecanum wheel models were created in Blender software [16]. To reduce complex collision calculations and increase a real time factor (RTF), low-poly models were used for a roller collision part. A 3D model of the mecanum wheel is shown in Fig. 1. Each roller has its own joint and can be freely rotated along the Z-axis of its frame.

A ROS plugin was developed to control the robot in Gazebo simulation [17] by publishing messages with linear velocities along the X and Y axes and an angular velocity along the Z-axis to a robot command topic. To detect collisions gazebo_ros_bumper plugin [18] was used. The mobile platform with a laser range finder (LRF) and an enabled bumper plugin is depicted in Fig. 2.

Fig. 1.
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3D model of a mecanum wheel in Gazebo: a red, green and blue arrows’ set denotes a coordinate frame of each roller.

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Fig. 2.
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ArtBul mobile robot in Gazebo: blue rays visualize LRF beams.

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2.2 Virtual Environments

Simulation provides a significant support in early stage testing. A 3D modeling can be used to produce a necessary 3D digital representation of real objects with a varying difficulty. Modern modeling tools are often used for designing virtual environments [19]. Testing local planners requires a special navigation map called an occupancy grid map (OGM). The OGM is a 2D binary map that consists of cells. The OGM encodes occupancy data where white pixels represent free cells, black pixels are occupied cells and gray pixels are not yet explored. In our test cases, OGMs should not contain any information about obstacles because a goal of a local planner is a real-time path planning processing.

Two different virtual worlds in Gazebo were created to benchmark ROS local planner algorithms. The first world had 20 × 6 × 3 m dimensions and contained static obstacles (Fig. 3, top). An OGM of the first virtual environment is shown in Fig. 3, bottom. The second world of 10 × 10 × 3 m dimensions contained a single dynamic obstacle – a cube with a 1 m length side (Fig. 4). We created two motion patterns that the cube uses while moving: along X-axis (Fig. 4, left top) and Y-axis (Fig. 4, left bottom). An OGM of the second virtual world is shown in Fig. 4, right.

Fig. 3.
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A 3D virtual environment filled with static obstacles: cubes of varying sizes, cuboids and cylinders (top). The corresponding 2D OGM with static obstacles excluded (bottom).

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Fig. 4.
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(Left) 3D virtual environment with a dynamic obstacle: a cube moves along X-axis (top) and Y-axis (bottom). (Right) The corresponding 2D OGM with static obstacles excluded.

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3 ROS Local Planners

A motion control plays an important role in an autonomous navigation. ROS local planners use sensory information to perceive a current robot state and generates feasible trajectories that the robot is allowed to follow. Avoiding any dynamic or static obstacles that may (or may not) be included in a given global map is a responsibility of a ROS local planner. ROS local planners use sensory data from various sensors such as LRF sensors or ultrasound sensors, and various devices to plan an optimal trajectory [20].

All local planners use the same values for coinciding parameters and the same global and local costmap configurations. For all local planners limitations were set as follows: a linear acceleration was limited to 2.5 m/s2, an angular acceleration to 3.2 rad/s2, a linear speed to 0.5 m/s, and an angular speed to 1 rad/s. A controller tolerance in yaw/rotation (yaw_goal_tolerance) was set as 0.05 rad, a controller tolerance in the X and Y distance (xy_goal_tolerance) as 0.1 m.

Trajectory Rollout.

ROS package base_local_planner provides implementations of the Dynamic Window and Trajectory Rollout approaches to a local control. Base_local_planner is a basic ROS local planner that provides an application programming interface for other local planners. In order to use Algorithm Trajectory Rollout, the parameter dwa should be set to false. For mecanum wheel robots holonomic_robot parameter should be set to true.

DWA.

ROS package dwa_local_planner is a modular DWA implementation with more flexible y-axis variables for holonomic robots than base_local_planner’s DWA. DWA discretely samples a robot’s control space, performs a forward simulation for each sample, evaluates and filters each trajectory in the local costmap and finally selects a highest-scoring trajectory.

Eband.

ROS package eband_local_planner implements the Elastic Band (EBand) method. An elastic band is a deformable collision free path generated by a global path incorporating information about obstacles proximity. A main drawback of eband_local_planner is that the ROS-based method implementation does not support an obstacle avoidance for moving obstacles.

TEB.

ROS package teb_local_planner implements the Timed-Elastic-Band (TEB) method for an online trajectory optimization. A difference between TEB and EBand is that a local trajectory is optimized not by external forces, but by applying a cost function. For a holonomic robot min_turning_radius parameter should be set to 0 and weight_kinematics_nh parameter to 1.

4 Performance Comparison

Three experiments with different scenarios were conducted to identify a most suitable local planner for a mecanum wheeled robot. In the first experiment, a starting position of the robot was set to (0; −8) and a goal was set to (0; 8). A 2D occupancy grid map did not contain static obstacles. Next, several static obstacles were added to the world after a global map had been built. In the second experiment, the robot started at (−3.5; 0) and targeted to (3.5; 0). A cube with sides of 1 m moved linearly without an acceleration along a trajectory from point (2; 0) to point (−2; 0) and backwards, with 0.4 m/s linear velocity. In the third experiment, the cube moved from (0; 2) to (0; −2) and backwards. A reference trajectory depicted in Fig. 5 represents a suggested optimal path. Distance-optimal robot trajectories in the world with static objects are shown in Fig. 6.

Fig. 5.
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Distance optimal robot trajectory.

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Fig. 6.
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Robot trajectories built by the local planners (in the world with static obstacles).

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Experiments showed that the Trajectory local planner never generated linear velocities along the Y-axis, and the omnidirectional robot moved as a differential wheeled robot with any planner settings. The Eband local planner trajectory was the smoothest, but this planner cannot handle dynamic obstacles and does not perform an online trajectory replanning. The drawbacks of the Eband are that the algorithm uses a global costmap updated dynamically, does not publish a response after reaching a target point and a task execution time is unmeasurable.

Table 1 demonstrates experimental results of local planners evaluated in the first world. Max T, Min T and Avg T stand for a maximum, minimum and average task execution time, respectively. Max D, Min D and Avg D denote a maximum, minimum and average path length, respectively. Success (Suc) column depicts how many times the robot reached the goal without obstacle collisions. Success with collision (SwC) column depicts how many times the robot reached the goal with at least one obstacle collision. Failed (F) column depicts how many times the robot failed to reach the goal. The TEB local planner showed the lowest minimum time and the lowest average time to complete the task. In one case, the robot with the TEB collided with an obstacle because the TEB heavily loaded the PC system and a frequency of publishing velocities to a command topic decreased. The DWA achieved the lowest minimum and the lowest average path length.

Table 1. ROS local planners’ evaluation results (in the world with static obstacles).
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In the experiment with the moving along the X-axis cube, all planners generated approximately the same trajectory (Fig. 7). Table 2 demonstrates experiments results of the local planners evaluated in the second world with the dynamic obstacle (a pattern of motion along X-axis). In this case, the TEB also showed the lowest minimum and the lowest average time required for a successful task completion. The Trajectory local planner showed the worst result within 50 experiments with only 7 successful and 15 (completely) failed. The EBand was successful in all 50 cases.

Table 2. ROS local planners evaluation results (the pattern of motion along X-axis).
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Fig. 7.
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Robot trajectories built by the local planners (the pattern of motion along X-axis).

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In the case of the moving along the Y-axis cube, the DWA and the Trajectory Rollout showed a similar behavior (Fig. 8). When the cube appeared in the local costmap, the robot stopped and attempted to select an optimal movement trajectory. When the cube left the local costmap, the robot moved forward. The Eband and the TEB rebuilt the trajectory and continued the motion.

Table 3 demonstrates experimental results of the local planners evaluated in the second world with a dynamic obstacle (a pattern of motion along Y-axis). The TEB generated a maximum robot velocity. This method demonstrated the minimum task execution time and the least number of collisions with the obstacle. The worst result was shown by the DWA, which completely failed the task in 32 cases out of 50.

Table 3. ROS local planners evaluation results (the pattern of motion along Y-axis).
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Fig. 8.
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Robot trajectories built by the local planners (the pattern of motion along Y-axis).

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5 Conclusion

This paper presented a comparison of ROS local planners for mecanum wheeled robots (Trajectory Rollout, DWA, EBand, and TEB) and provided a benchmark that allowed to experimentally determine a recommended ROS local planner for a wheeled holonomic system. Virtual experiments were conducted in Gazebo simulator, which was used for modeling virtual environments with static and dynamic obstacles. Three types of virtual environments were employed with 50 virtual experiments within each environment. The algorithms were compared using a path length, a travelling time and a number of obstacle collisions.

The virtual experiments showed that the Trajectory Rollout local planner did not generate linear velocities along the Y-axis in all configurations; this planner demonstrated the worst task execution time and the longest trajectory path lengths. The DWA local planner handled static obstacles effectively, but performed poorly with dynamic obstacles. The EBand local planner did not rebuild a local motion trajectory when it worked on a non-renewable costmap of an explored environment; therefore, the use of this planner is possible only when a global costmap is dynamically updated, in which case the robot’s trajectory will be rebuilt by a global planner. The TEB local planner achieved a significantly better performance in terms of a task execution time and showed the least number of obstacle collisions. Therefore, the TEB local planner could be recommended for dynamic scenes since it demonstrated the best results within a dynamic environment.