We present a navigation planning framework for dynamic, multi-agent environments, where no explicit communication takes place among agents. Inspired by the collaborative nature of human navigation, our approach encodes the concept of coordination into an agent’s decision making through an inference mechanism about collaborative strategies of collision avoidance. Each such strategy represents a distinct avoidance protocol, prescribing a distinct class of navigation behaviors to agents. We model such classes as equivalence classes of multi-agent path topology, using the formalism of topological braids. This formalism may naturally encode any arbitrarily complex, spatiotemporal, multi-agent behavior, in any environment with any number of agents into a compact representation of dual algebraic and geometric nature. This enables us to construct a probabilistic inference mechanism that predicts the collective strategy of avoidance among multiple agents, based on observation of agents’ past behaviors. We incorporate this mechanism into an online planner that enables an agent to understand a multi-agent scene and determine an action that not only contributes progress towards its destination, but also reduction of the uncertainty of other agents regarding the agent’s role in the emerging strategy of avoidance. This is achieved by picking actions that compromise between energy efficiency and compliance with everyone’s inferred avoidance intentions. We evaluate our approach by comparing against a greedy baseline that only maximizes individual efficiency. Simulation results of statistical significance demonstrate that our planner results in a faster uncertainty decrease that facilitates the decision-making process of co-present agents. The algorithm’s performance highlights the importance of topological reasoning in decentralized, multi-agent planning and appears promising for real-world applications in crowded human environments.
ALAN: adaptive learning for multi-agent navigation
In multi-agent navigation, agents need to move towards their goal locations while avoiding collisions with other agents and obstacles, often without communication. Existing methods compute motions that are locally optimal but do not account for the aggregated motions of all agents, producing inefficient global behavior especially when agents move in a crowded space. In this work, we develop a method that allows agents to dynamically adapt their behavior to their local conditions. We formulate the multi-agent navigation problem as an action-selection problem and propose an approach, ALAN, that allows agents to compute time-efficient and collision-free motions. ALAN is highly scalable because each agent makes its own decisions on how to move, using a set of velocities optimized for a variety of navigation tasks. Experimental results show that agents using ALAN, in general, reach their destinations faster than using ORCA, a state-of-the-art collision avoidance framework, and two other navigation models.
more »
« less
- PAR ID:
- 10074256
- Date Published:
- Journal Name:
- Autonomous Robots
- ISSN:
- 0929-5593
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
more » « less
-
Conventional Multi-Agent Path Finding (MAPF) problems aim to compute an ensemble of collision-free paths for multiple agents from their respective starting locations to pre-allocated destinations. This work considers a generalized version of MAPF called Multi-Agent Combinatorial Path Finding (MCPF) where agents must collectively visit a large number of intermediate target locations along their paths before arriving at destinations. This problem involves not only planning collision-free paths for multiple agents but also assigning targets and specifying the visiting order for each agent (i.e., target sequencing). To solve the problem, we leverage Conflict-Based Search (CBS) for MAPF and propose a novel approach called Conflict-Based Steiner Search (CBSS). CBSS interleaves (1) the collision resolution strategy in CBS to bypass the curse of dimensionality in MAPF and (2) multiple traveling salesman algorithms to handle the combinatorics in target sequencing, to compute optimal or bounded sub-optimal paths for agents while visiting all the targets. We also develop two variants of CBSS that trade off runtime against solution optimality. Our test results verify the advantage of CBSS over the baselines in terms of computing cheaper paths and improving success rates within a runtime limit for up to 20 agents and 50 targets. Finally, we run both Gazebo simulation and physical robot tests to validate that the planned paths are executablemore » « less
-
Conventional Multi-Agent Path Finding (MAPF) problems aim to compute an ensemble of collision-free paths for multiple agents from their respective starting locations to pre-allocated destinations. This work considers a generalized version of MAPF called Multi-Agent Combinatorial Path Finding (MCPF) where agents must collectively visit a large number of intermediate target locations along their paths before arriving at destinations. This problem involves not only planning collision-free paths for multiple agents but also assigning targets and specifying the visiting order for each agent (i.e., target sequencing). To solve the problem, we leverage Conflict-Based Search (CBS) for MAPF and propose a novel approach called Conflict-Based Steiner Search (CBSS). CBSS interleaves (1) the collision resolution strategy in CBS to bypass the curse of dimensionality in MAPF and (2) multiple traveling salesman algorithms to handle the combinatorics in target sequencing, to compute optimal or bounded sub-optimal paths for agents while visiting all the targets. We also develop two variants of CBSS that trade off runtime against solution optimality. Our test results verify the advantage of CBSS over the baselines in terms of computing cheaper paths and improving success rates within a runtime limit for up to 20 agents and 50 targets. Finally, we run both Gazebo simulation and physical robot tests to validate that the planned paths are executable.more » « less
-
NA (Ed.)Conventional Multi-Agent Path Finding (MAPF) problems aim to compute an ensemble of collision-free paths for multiple agents from their respective starting locations to pre-allocated destinations. This work considers a generalized version of MAPF called Multi-Agent Combinatorial Path Finding (MCPF) where agents must collectively visit a large number of intermediate target locations along their paths before arriving at destinations. This problem involves not only planning collisionfree paths for multiple agents but also assigning targets and specifying the visiting order for each agent (i.e. multi-target sequencing). To solve the problem, we leverage the well-known Conflict-Based Search (CBS) for MAPF and propose a novel framework called Conflict-Based Steiner Search (CBSS). CBSS interleaves (1) the conflict resolving strategy in CBS to bypass the curse of dimensionality in MAPF and (2) multiple traveling salesman algorithms to handle the combinatorics in multi-target sequencing, to compute optimal or bounded sub-optimal paths for agents while visiting all the targets. Our extensive tests verify the advantage of CBSS over baseline approaches in terms of computing shorter paths and improving success rates within a runtime limit for up to 20 agents and 50 targets. We also evaluate CBSS with several MCPF variants, which demonstrates the generality of our problem formulation and the CBSS framework.more » « less
-
This paper considers a planar multi-agent coordination problem. Unlike other related works, we explicitly consider a globally shared wireless communication channel where individual agents must choose both a frequency and power to transmit their messages at. This problem is motivated by the pressing need for algorithms that are able to efficiently and reliably operate on overcrowded wireless networks or otherwise poor-performing RF environments. We develop a self-triggered coordination algorithm that guarantees convergence to the desired set of states with probability 1. The algorithm is developed by using ideas from event/self-triggered coordination and allows agents to autonomously decide for themselves when to broadcast information, at which frequency and power, and how to move based on information received from other agents in the network. Simulations illustrate our results.more » « less
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1007/s10514-018-9719-4
