Arena-Bench: A Benchmarking Suite for Obstacle Avoidance Approaches in Highly Dynamic Environments

Abstract

The ability to autonomously navigate safely, especially within dynamic environments, is paramount for mobile robotics. In recent years, DRL approaches have shown superior performance in dynamic obstacle avoidance. However, these learning-based approaches are often developed in specially designed simulation environments and are hard to test against conventional planning approaches. Furthermore, the integration and deployment of these approaches into real robotic platforms are not yet completely solved. In this paper, we present Arena-bench, a benchmark suite to train, test, and evaluate navigation planners on different robotic platforms within 3D environments. It provides tools to design and generate highly dynamic evaluation worlds, scenarios, and tasks for autonomous navigation and is fully integrated into the robot operating system. To demonstrate the functionalities of our suite, we trained a DRL agent on our platform and compared it against a variety of existing different model-based and learning-based navigation approaches on a variety of relevant metrics. Finally, we deployed the approaches towards real robots and demonstrated the reproducibility of the results. The code is publicly available at github.com/ignc-research/arena-bench.

arXiv commentRobotics and Automation Letters (RA-L), 2022, 8 pages, 6 figures

References in corpus (5)

• Social Force Model for Pedestrian Dynamics
• A Survey of Deep RL and IL for Autonomous Driving Policy Learning
• An Extensible Benchmarking Infrastructure for Motion Planning Algorithms
• SEAN: Social Environment for Autonomous Navigation
• MRPB 1.0: A Unified Benchmark for the Evaluation of Mobile Robot Local Planning Approaches

Cited by in corpus (1)

• Improving the Generalization of Unseen Crowd Behaviors for Reinforcement Learning based Local Motion Planners