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Danesh Tarapore

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

8 papers

1 author row

IROS Conference 2025 Conference Paper

Blind-Wayfarer: A Minimalist, Probing-Driven Framework for Resilient Navigation in Perception-Degraded Environments

Yanran Xu
Klaus-Peter Zauner
Danesh Tarapore

Navigating autonomous robots through dense forests and rugged terrains is especially daunting when exteroceptive sensors—such as cameras and LiDAR sensors— fail under occlusions, low-light conditions, or sensor noise. We present Blind-Wayfarer, a probing-driven navigation framework inspired by maze-solving algorithms that relies primarily on a compass to robustly traverse complex, unstructured environments. In 1, 000 simulated forest experiments, Blind-Wayfarer achieved a 99. 7% success rate. In real-world tests in two distinct scenarios—with rover platforms of different sizes—our approach successfully escaped forest entrapments in all 20 trials. Remarkably, our framework also enabled a robot to escape a dense woodland, traveling from 45 m inside the forest to a paved pathway at its edge. These findings highlight the potential of probing-based methods for reliable navigation in challenging perception-degraded field conditions. Videos and code are available on our website https://sites.google.com/view/blind-wayfarer

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IROS Conference 2025 Conference Paper

SubCDM: Collective Decision-Making with a Swarm Subset

Samratul Fuady
Danesh Tarapore
Mohammad Divband Soorati

Collective decision-making is a key function of autonomous robot swarms, enabling them to reach a consensus on actions based on environmental features. Existing strategies require the participation of all robots in the decision-making process, which is resource-intensive and prevents the swarm from allocating the robots to any other tasks. We propose Subset-Based Collective Decision-Making (SubCDM), which enables decisions using only a swarm subset. The construction of the subset is dynamic and decentralized, relying solely on local information. Our method allows the swarm to adaptively determine the size of the subset for accurate decision-making, depending on the difficulty of reaching a consensus. Simulation results using one hundred robots show that our approach achieves accuracy comparable to using the entire swarm while reducing the number of robots required to perform collective decision-making, making it a resource-efficient solution for collective decision-making in swarm robotics.

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IROS Conference 2022 Conference Paper

Collective Decision Making in Communication-Constrained Environments

Thomas G. Kelly
Mohammad Divband Soorati
Klaus-Peter Zauner
Sarvapali D. Ramchurn
Danesh Tarapore

One of the main tasks for autonomous robot swarms is to collectively decide on the best available option. Achieving that requires a high quality communication between the agents that may not always be available in a real world environment. In this paper we introduce the communication-constrained collective decision-making problem where some areas of the environment limit the agents' ability to communicate, either by reducing success rate or blocking the communication channels. We propose a decentralised algorithm for mapping environmental features for robot swarms as well as improving collective decision making in communication-limited environments without prior knowledge of the communication landscape. Our results show that making a collective aware of the communication environment can improve the speed of convergence in the presence of communication limitations, at least 3 times faster, without sacrificing accuracy.

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ICRA Conference 2021 Conference Paper

ASVLite: a high-performance simulator for autonomous surface vehicles

Toby Thomas
David M. Bossens
Danesh Tarapore

The energy of ocean waves is the key distinguishing factor of marine environments compared to other aquatic environments such as lakes and rivers. Waves significantly affect the dynamics of marine vehicles; hence it is imperative to consider the dynamics of vehicles in waves when developing efficient control strategies for autonomous surface vehicles (ASVs). However, most marine simulators available open-source either exclude dynamics of vehicles in waves or use methods with high computational overhead. This paper presents ASVLite, a computationally efficient ASV simulator that uses frequency domain analysis for wave force computation. ASVLite is suitable for applications requiring low computational overhead and high run-time performance. Our tests on a Raspberry Pi 2 and a mid-range desktop computer show that the simulator has a high run-time performance to efficiently simulate irregular waves with a component wave count of up to 260 and large-scale swarms of up to 500 ASVs.

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EUMAS Conference 2021 Conference Paper

Large-Scale, Dynamic and Distributed Coalition Formation with Spatial and Temporal Constraints

Luca Capezzuto
Danesh Tarapore
Sarvapali D. Ramchurn

Abstract The Coalition Formation with Spatial and Temporal constraints Problem (CFSTP) is a multi-agent task allocation problem in which few agents have to perform many tasks, each with its deadline and workload. To maximize the number of completed tasks, the agents need to cooperate by forming, disbanding and reforming coalitions. The original mathematical programming formulation of the CFSTP is difficult to implement, since it is lengthy and based on the problematic Big-M method. In this paper, we propose a compact and easy-to-implement formulation. Moreover, we design D-CTS, a distributed version of the state-of-the-art CFSTP algorithm. Using public London Fire Brigade records, we create a dataset with 347588 tasks and a test framework that simulates the mobilization of firefighters in dynamic environments. In problems with up to 150 agents and 3000 tasks, compared to DSA-SDP, a state-of-the-art distributed algorithm, D-CTS completes \(3. 79\% \pm [42. 22\%, 1. 96\%]\) more tasks, and is one order of magnitude more efficient in terms of communication overhead and time complexity. D-CTS sets the first large-scale, dynamic and distributed CFSTP benchmark.

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ICRA Conference 2021 Conference Paper

Rapidly adapting robot swarms with Swarm Map-based Bayesian Optimisation

David M. Bossens
Danesh Tarapore

Rapid performance recovery from unforeseen environmental perturbations remains a grand challenge in swarm robotics. To solve this challenge, we investigate a behaviour adaptation approach, where one searches an archive of controllers for potential recovery solutions. We propose two algorithms: (i) Swarm Map-based Optimisation (SMBO), which selects and evaluates one controller at a time, for a homogeneous swarm, in a centralised fashion; and (ii) Swarm Map-based Optimisation Decentralised (SMBO-Dec), which performs an asynchronous batch-based Bayesian optimisation to simultaneously explore different controllers for groups of robots in the swarm. A simulation study investigates adaptation of a Thymio robot swarm in a collective foraging task. First, we investigate different groups of sensory-motor disturbances, including fault to proximity sensors, ground sensors, or actuators of individual robots, with 100 unique combinations for each type. Second, we investigate changes to the surrounding environment of the swarm, where the number of available resources drops or where one robot disrupts the rest of the swarm; for each such change, we include 30 unique conditions. The viability of SMBO and SMBO-Dec is demonstrated, comparing favourably to variants of random search and gradient descent, and various ablations, and improving performance up to 80% compared to the performance at the time of fault injection within less than 30 evaluations.

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EUMAS Conference 2020 Conference Paper

Anytime and Efficient Coalition Formation with Spatial and Temporal Constraints

Luca Capezzuto
Danesh Tarapore
Sarvapali D. Ramchurn

Abstract The Coalition Formation with Spatial and Temporal constraints Problem (CFSTP) is a multi-agent task scheduling problem where the tasks are spatially distributed, with deadlines and workloads, and the number of agents is typically much smaller than the number of tasks. Thus, the agents have to form coalitions in order to maximise the number of completed tasks. The state-of-the-art CFSTP solver, the Coalition Formation with Look-Ahead (CFLA) algorithm, has two main limitations. First, its time complexity is exponential with the number of agents. Second, as we show, its look-ahead technique is not effective in real-world scenarios, such as open multi-agent systems, where new tasks can appear at any time. In this work, we study its design and define an extension, called Coalition Formation with Improved Look-Ahead ( \(\text {CFLA}2\) ), which achieves better performance. Since we cannot eliminate the limitations of CFLA in \(\text {CFLA}2\), we also develop a novel algorithm to solve the CFSTP, the first to be simultaneously anytime, efficient and with convergence guarantee, called Cluster-based Task Scheduling (CTS). In tests where the look-ahead technique is highly effective, CTS completes up to 30% (resp. 10%) more tasks than CFLA (resp. \(\text {CFLA}2\) ) while being up to four orders of magnitude faster. Our results affirm CTS as the new state-of-the-art algorithm to solve the CFSTP.

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IROS Conference 2020 Conference Paper

Low-viewpoint forest depth dataset for sparse rover swarms

Chaoyue Niu
Danesh Tarapore
Klaus-Peter Zauner

Rapid progress in embedded computing hardware increasingly enables on-board image processing on small robots. This development opens the path to replacing costly sensors with sophisticated computer vision techniques. A case in point is the prediction of scene depth information from a monocular camera for autonomous navigation. Motivated by the aim to develop a robot swarm suitable for sensing, monitoring, and search applications in forests, we have collected a set of RGB images and corresponding depth maps. Over 100000 RGB/depth image pairs were recorded with a custom rig from the perspective of a small ground rover moving through a forest. Taken under different weather and lighting conditions, the images include scenes with grass, bushes, standing and fallen trees, tree branches, leaves, and dirt. In addition GPS, IMU, and wheel encoder data were recorded. From the calibrated, synchronized, aligned and timestamped frames about 9700 image-depth map pairs were selected for sharpness and variety. We provide this dataset to the community to fill a need identified in our own research and hope it will accelerate progress in robots navigating the challenging forest environment. This paper describes our custom hardware and methodology to collect the data, subsequent processing and quality of the data, and how to access it.

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